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Utilizing Hybrid Metaheuristic Approach to Design an Agricultural Closed-loop Supply Chain Network
... Merging the CLSC problem with sustainability criteria, i.e., carbon emissions of transportation, job opportunities, lost workdays, and so on, is an active research topic nowadays [22,23,24,25]. These CLSC models were also applied to numerous industrial cases like glass [26], tire [27], and agriculture industries [28]. ...
... The constraint set (24) defines the number of waste products that are transferred from distribution centers to the recovery center. The constraint set (25) computes the number of products that are sold by the distribution centers. The constraint set (26) defines the number of waste products which can be sold for second-hand use from distribution centers. ...
... • Addressing complexity with the Lagrangian-based heuristic algorithm: The Lagrangian-based heuristic algorithm presented in this study serves as a practical solution for managing the inherent complexity of network design models in global green CLSC problems. This tool enables supply chain practitioners to optimize network design decisions efficiently, leading to improved operational performance and enhanced sustainability [25,82]. ...
... The coconut, often referred to as the "tree of life," has garnered global attention due to its versatile applications and economic benefits. Projections indicate a significant rise in the value of coconut producers worldwide [16]. The cultivation and processing of coconuts provide substantial economic benefits and employment, particularly in major producer countries like the Philippines, Indonesia, India, Tanzania, Papua New Guinea, and Sri Lanka [17]. ...
... The RSC of agricultural waste has great potential to reduce the environmental and economic costs incurred due to the disposability of agricultural waste, contributing to better sustainable agricultural development. The environmental impact of agricultural waste can be reduced by designing an optimized supply chain distribution network and opening composting centers that minimize the closed-loop supply chain's total fixed and variable costs [26]. The reverse supply chain can reduce energy consumption and cost. ...
The irrational disposal of agricultural waste harms the interests of the main bodies of the related supply chain while seriously jeopardizing the environment. To a certain extent, the reverse supply chain (RSC) of agricultural waste can provide more high-quality resources for agricultural production and promote the green development of agricultural production. Therefore, RSC optimization is of great significance to the sustainable development of agriculture. We constructed an evolutionary game model of agricultural waste recyclers and reprocessing enterprises for agricultural waste. The stability of mixed strategies was analyzed using a Jacobi matrix, and evolutionary paths under varying parameter ranges were simulated using MATLAB. The simulation results show that in the early stage of RSC optimization, government subsidies to reprocessing enterprises and increased subsidies for agricultural waste recyclers are conducive to a more stable agricultural waste reverse-recycling market. When the agricultural waste reverse-recycling market reaches a certain scale, the government should gradually reduce subsidies, effectively preventing enterprises from being overly reliant on them. This study not only offers a decision-making foundation for agricultural waste recyclers and agricultural waste reprocessing enterprises to make optimal strategic choices but also serves as a reference for the government in formulating appropriate policies.
... The results showed that the highest total emissions came from the production and refrigeration in the warehouse (93 %), followed by transport emissions. Previous research on agricultural supply chains such as pistachio and pomegranate fruit was carried out byRajabi- Kafshgar et al. (2023) andGholipour et al. (2024). The research was carried out by considering the risk and environmental aspects of agricultural products.Xia et al. (2024) used a multi-objective mixed-integer nonlinear programming model under uncertainty to build an open-loop, five-tier reverse logistic network model. ...
This study contributes to the fish reverse supply chain due to a lack of social, economic and environmental impacts. This study aims to develop a mathematical model for the fish reverse supply chain with a multi-echelon, multiple periods, and products. The model optimizes total profit, job opportunities, and carbon emissions simultaneously. The proposed model provides social-economic insight for governments and industries to understand the increasing job opportunities if fish gelatine and powder industries can process fish waste. A sensitivity analysis shows that the supply of raw fish, selling prices, and purchasing costs are sensitive to total profit, carbon emissions, and job opportunities. The results show that the total profit for five months is USD 1,437,837, and the most significant contribution to the total cost is the costs of purchasing, emission costs, and production costs, which are 43.83%, 24.02%, and 18.15%, respectively. These results can assist managers in making optimal decisions regarding raw fish supply, halal fish gelatine, and fish powder production, impacting strategic, tactical, and operational policies.
... Fasihi et al. [ 35 ] presented a sustainable CLSC network in the food industry by considering uncertain circumstances using a fuzzy algorithm with two objective functions: maximizing customer satisfaction and supply chain profit. Rajabi-Kafshgar et al. [ 36 ] discussed a possible solution to the CLSC problem for used batteries by considering a multi-objective method. Their research developed and utilized the "Fully Fuzzy program approach" to obtain different ranges of profit obtained by implementing a Multiple-echelon battery closed-loop supply chain, which combined multi-product with multiple-period scenarios under uncertain information. ...
... One vital strategic decision in supply chain management is Supply Chain Network Design (SCND) (Rajabi-Kafshgar et al. 2023;Charati et al. 2024). Designing an efficient system to meet diverse customer needs, enhance product quality, ensure timely delivery, and reduce overall costs is crucial (Vali-Siar and Roghanian 2022). ...
Evidence shows that the issue of transportation has been one of the most decisive factors in optimizing the supply chain in the new global economy. The transportation aspect is also identified as a critical factor in achieving customer satisfaction and increasing supply chain efficiency. In response to the dynamic nature of customer demands, organizations are increasingly diversifying their products to remain competitive. To address this issue, a multi-product mathematical model is formulated considering transportation modes and fixed costs. Considering the complexity of the model, meta-heuristic algorithms including genetic algorithm, harmony search, simulation annealing and combining these algorithms with the restart mechanism have been proposed and evaluated. Then, the best operators and algorithm parameter values are obtained using the Taguchi method and the proposed algorithms are compared. The results showed that the hybrid simulated annealing algorithm performs better than other algorithms. This study contributes to the field of supply chain management by providing insights into transportation optimization in multi-product supply chains.
... SCND has emerged as a critical area of research driven by the intricate complexities prevalent in modern supply chains. Deterministic SCND models have been extensively investigated, e.g., Zhen et al. (2020); Naderi et al. (2020); Pazhani et al. (2021); Rajabi-Kafshgar et al. (2023). Currently, there has been a noticeable shift towards examining SCND models within the context of uncertainty due to the dynamic challenges faced by supply chain networks. ...
Resource scarcity has driven growing interest in circular economy (CE). Closed-loop supply chain (CLSC) with returnable transport items (RTIs) in the food industry is an important component of CE. However, existing works on food CLSC with RTIs have not simultaneously considered the perishability, facility location, and uncertain demand under limited information. Therefore, this work addresses a new food CLSC optimization problem. We first propose a non-linear chance-constrained programming model. It is then transformed into a mixed-integer linear programming model via using the distribution-free (DF) method and sample average approximation (SAA) method, respectively. An illustrative example reveals that the DF method needs only 10.50% of the computation time of the SAA method. To address large-scale problems, an improved Lagrangian relaxation (LR) method is developed. To address the computational challenge in large-scale problems, an improved Lagrangian relaxation (LR) algorithm is developed. Results show that CPLEX achieves a gap of 75.57%, while the LR surpasses it by finding near-optimal solutions with a gap of 1.22%, using only 31.82% of the computation time required by CPLEX. For this work, the main insights are summarized: 1) extending product shelf life can reduce the total cost; and 2) to alleviate uncertain demand and production risks, production capacity and product inventory capacity can be appropriately expanded, but excessive investment may not improve returns.
... They used some meta-heuristics algorithm and exact methods for solving the planned model. Rajabi-Kafshgar et al. (2023) considered the environmental impacts of agricultural wastes, and proposed a MILP model for an ASC network to minimize total costs. Some hybrid meth-heuristics algorithms such as KASA and GASA were used to find optimal solutions. ...
This study seeks to develop a closed-loop network for managing the pistachio Supply Chain (SC) under uncertainty. Then, a Mixed-Integer Linear Programming model is suggested to achieve optimal costs of the SC such transportation, production costs and CO2 emissions tax. It is assumed that the demand for the product depends on the freshness and price of the product and, to deal with uncertainty, a robust optimization approach is used. Furthermore, GAMS software as an exact solution method and four meta-heuristics algorithms including Whale Optimization Algorithm, Particle Swarm Optimization, Rat Swarm Optimizer and a new hybrid algorithm are used as the solution approach. The accuracy of the planned model is examined using a case study and to more measurement, a sensitivity analysis is performed. Finally, the computational time of the mentioned algorithms and their obtained results are compared. The numerical analysis showed that the hybrid algorithm, although having more computational time, is superior to others, which the results had a difference between 0.9 and 2.7% with the exact method. Therefore, it is showed that the hybrid approach is a valid approach to solve large-scale problems. Our findings are helpful for pistachio-producing countries.
... Simultaneously, driven by factors such as the scarcity of valuable resources, escalating customer demands, economic conditions, and the growing influence of environmental and social concerns, the design of reverse logistics networks is gaining traction among supply chain professionals [3]. The formulation of a closedloop supply chain yields benefits by fulfilling customer demands in diverse markets, both through direct product flows and through the reverse flow of returned products [4]. ...
This study aims to develop a model for the closed-loop supply chain of photovoltaic (PV) systems. The primary objective addresses strategic and tactical decision-making using a two-stage approach. To pinpoint suitable locations for solar power plants, the PROMETHEE II method is utilized, which is a component of multi-attribute decision making (MADM) approaches. Next, a multi-objective modeling of the closed-loop PV supply chain is conducted. This model aims to minimize total supply chain costs, reduce environmental impacts, mitigate adverse social effects, maximize the on-time delivery (OTD) of manufactured products, and maximize market share. Additionally, a robust fuzzy mathematical model is introduced to examine the model’s sustainability under various uncertainties. An evaluation of the effectiveness and utility of this model is conducted in Tehran city. Furthermore, a comprehensive analysis of various supply chain costs indicates that production centers have the highest costs, while separation centers have the lowest costs.
... It also specify the multifaceted interaction between the communal, cultural, environmental, and financial proportions of food (Braun et al., 2023). Food security in Global context: Neoliberal model for food security is meticulously linked with the renovation project, that wanted to alter old-style farming approaches into developed ones (Rajabi-Kafshgar et al., 2023) and agroindustry which commodities and converts agriculture into an business endeavor (Majeed and Mushtaq, 2022). The second are fixed on the implementation of current skills and free market ethics, with the goal of exploiting labor and land output and instituting market-oriented food delivery, correspondingly (Tsakirpaloglou et al., 2023). ...
Demand for food is progressively becoming challenging for ever increasing population worldwide. Majority of the discussion have focused on growing crop yield which has continuously sustained a vital approach to lessen food insecurity. Nevertheless, in spite of the fact that adequate food is presently produced per capita to nourish the population worldwide but yet a reasonable population suffers from food insecurity especially in under-developed republics. Fulfillment of upcoming food request will be more intricate by harmful variations in climate and other ecological influences, global climate change. This current paper presents true picture of a food systems method to investigate the multifaceted food security arena and highlights numerous substitutes to cop up this situation. These encompass (i) giving a outline for organizing discussions intended at increasing food security and recognizing the variety of interested parties who should be intricate; (ii) participating studies of the complete network of food system doings starting from crop harvesting to consumption of the produced food by those of the food security results i.e. continuous supply chain over yearly, consumption and accessibility rather tan merely focusing on crop production (iii) serving to both evaluate the effects of global ecological conservation on food systems and find responses to the earth system from food system activities; (iv) helping to identify intervention points for enhancing food security and examining interactions and trade-offs between bionetwork facilities and food security, and community welfare results of diverse adaptation paths; and motivation of health-care institution for augmentation of human health services (v) pinpointing where innovative study is desired.
... Arabi and Gholamian (2023) addressed the mining industry with a multi-period, multi-product mixed-integer quadratic programming model for a closed-loop stone supply chain network, considering resilience, quality, and two-stage stochastic programming. Rajabi-Kafshgar et al. (2023) developed a mixed linear mathematical model for an agricultural supply chain network, focusing on reverse logistics and employing meta-heuristic algorithms. Hosseini Dehshiri and Amiri (2024) integrated circular economy principles into supply chain network design, addressing hybrid uncertainties using a robust scenario-based possibilistic-stochastic programming approach. ...
In the current global landscape where sustainability is increasingly critical, this study offers a significant contribution by redefining supply chain models through the lens of circular economy principles. It introduces a novel two-stage stochastic model tailored to designing sustainable closed-loop supply chains, adept at navigating uncertainties in location, allocation, and routing decisions. A notable feature of the model is its integration of a speed-variant fuel-efficient green vehicle routing system, which enables simultaneous pick-up and delivery, underscoring a commitment to environmental sustainability and circular economy principles. This model is distinct in its holistic approach, balancing total cost considerations with the creation of job opportunities to boost social benefits, while also addressing the pressing issue of fossil fuel consumption's environmental impact. The innovative use of the heuristic backward scenario reduction method to generate stochastic uncertainty scenarios enhances the model's applicability in real-world conditions. The study employs an upgraded ε-constraint method for smaller instances and a range of advanced metaheuristic algorithms, including Non-dominated Sorting Genetic Algorithm II, Multi-Objective Particle Swarm Optimization, Strength Pareto Evolutionary Algorithm version 2, Multi-Objective Evolutionary Algorithm based on Decomposition, and Pareto Envelope based Selection Algorithm II. This research provides critical insights and tools for managers and policymakers, particularly in scenarios where adherence to sustainability and circular economy principles is vital. This study not only advances academic discourse but also offers pragmatic solutions for real-world supply chain challenges, emphasizing the importance of integrating environmental and social considerations into economic decision-making.
... Numerical analysis showed that reducing emissions positively affects profitability, particularly in remanufacturing. There are also many other similar studies in the literature addressing the CLSC design using different types of models and solution methods such as Abdolazimi et al. (2022), Sajadiyan et al. (2022), andRajabi-Kafshgar et al. (2023). Akbari-Kasgari et al. (2022) addressed the growing copper demand due to industrialization, emphasizing the need for sustainable copper supply chains. ...
One of the most critical pillars of Industry 4.0 (I4.0) is Additive Manufacturing (AM) or 3D Printing technology. This transformative technology has garnered substantial attention due to its capacity to streamline processes, save time, and enhance product quality. Simultaneously, environmental concerns are mounting, with the growing accumulation of plastic bottle waste, offering a potential source of recycled material for 3D printing. To thoroughly harness the potential of AM and address the challenge of plastic bottle waste, a robust supply chain network is essential. Such a network not only facilitates the reintegration of plastic bottle waste and 3D printing byproducts into the value chain but also delivers significant environmental, social, and economic benefits, aligning with the tenets of sustainable development and circular economy. To tackle this complex challenge, a Mixed-Integer Linear Programming (MILP) mathematical model is offered to configure a Closed-Loop Supply Chain (CLSC) network with a strong emphasis on circularity. Environmental considerations are integral, and the primary objective is to minimize the overall cost of the network. Three well-known metaheuristics of Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are employed to treat the problem which are also efficiently adjusted by the Taguchi design technique. The efficacy of our solution methods is appraised across various problem instances. The findings reveal that the developed model, in conjunction with the fine-tuned metaheuristics, successfully optimizes the configuration of the desired circular CLSC network. In conclusion, this research represents a significant step toward the establishment of a circular supply chain that combines the strengths of 3D printing technology and the repurposing of plastic bottle waste. This innovative approach holds promise for not only reducing waste and enhancing sustainability but also fostering economic and social well-being.
... Sustainability and reliability aspects of a SCND problem were discussed by Rajabi-Kafshgar et al. (2023) for a close-looped supply chain when total costs (fixed and variable costs) are aimed to be minimized. Besides, this study minimized the number of disruptions in the whole network when the emissions are maintained as low as possible. ...
In the current era emphasizing sustainability and circularity, supply chain network design is a critical challenge for making reliable decisions. The optimization of facility location-allocation inventory problems (FLAIPs) holds the key to achieving dependable product delivery with reduced costs and carbon emissions. Despite the importance of these challenges, a substantial research gap exists regarding economic, reliability, and sustainability criteria for FLAIPs. This paper aims to fill this gap by introducing a multi-objective mixed-integer linear programming model, focusing on configuring a reliable sustainable supply chain network. The model addresses three key objectives: minimizing costs, minimizing emissions, and maximizing reliability. A notable contribution of this research lies in elaborating on five levels of a supply chain network catering to the delivery of multiple products across various periods. Another novelty is the simultaneous incorporation of economic, environmental, and reliability objectives in the network design—a facet rarely addressed in prior research. Results highlight that varying demand levels for each facility lead to altered trade-offs between objectives, empowering practitioners to make diverse decisions in facility location allocation. The proposed mathematical model undergoes validation through numerical examples and sensitivity analysis of parameters. The paper concludes by presenting theoretical and managerial implications, contributing valuable insights to the field of sustainable supply chains.
... The proposed problem was addressed using a novel approach that combined the social engineering optimizer and Keshtel algorithm, resulting in a new hybrid metaheuristic solution. Similarly, Rajabi-Kafshgar et al. (2023) examined a CLSCND within the pistachio industry. They constructed a mathematical model with a single objective and devised three hybrid algorithms to address the problem effectively. ...
In this study, the design of a sustainable closed-loop supply chain network for agricultural
products with the goals of minimizing the cost and emission of greenhouse gases and maximizing the response to customer demand, and creating justice-based job opportunities, simultaneously, are aimed. The intended chain is based on the fruit supply chain study, which
includes fresh fruits, concentrate, and vermicompost fertilizer. A mixed-integer linear programming (MILP) model has been developed to achieve the triple bottom line. Due to the
nature of the NP-hard problem, the proposed model is solved using metaheuristic
approaches consisting of two renowned algorithms, NSGA-II and NRGA, and a relatively new
algorithm called NSGA-III. It is worth noting that the parameters of the algorithms are
adjusted to achieve the best performance in small, medium, and large-size problems exerting the Taguchi method. After comparing the results of the three algorithms based on the
well-known criteria, NRGA is introduced as the superior algorithm. Ultimately, the results of
sensitivity analysis indicate that appending the possibility of using vermicompost in gardens
and considering several vehicles in the proposed sustainable supply chain boosts the values
of economic and environmental objective functions to about 6.4 and 8.2%, respectively.
... Using their model, they were able to integrate the selection of facilities as a strategic decision with allocating customers, products, and warehouses as tactical decisions. In order to reduce the overall fixed and variable costs of the closed-loop supply chain, Rajabi-Kafshgar et al. [48] created a mixed linear mathematical model for an agriculture SCND. An integrated two-phase planning framework for the design of vaccine delivery network is studied by Habibi et al. [18]. ...
This study presents an integrated supply chain network with suppliers, manufacturers, assemblers, and customers. The proposed model considers a U-shaped assembly line with three sustainability objective functions. We consider assumptions considering different types of raw materials, multiple products, location selection of manufacturers, location selection of assemblers, and capacity of suppliers. The problem is formulated non-linear and then linearized as a multi-objective model. Some cost and demand parameters are considered uncertain and are represented by fuzzy sets and theory. The proposed uncertain model is first converted to a multi-objective crisp model by applying the modified robust possibilistic programming approach. Then, the obtained crisp multi-objective model is solved by an interactive-fuzzy optimization approach in the literature. For computational study, some test problems are generated and solved using an original deterministic formulation and the crisp
form of the uncertain formulation. The obtained results are analyzed and compared according to the objective function values. Finally, an extensive sensitivity analysis is performed on the parameters of the models.
... According to the findings, the model could cut greenhouse gas emissions costs by 17.29%, 22.82%, and 23.08% and transportation costs by 0.3% and 9.8%. A MILP mathematical model considering a closed-loop supply chain was developed by Rajabi-Kafshgar et al. (2023). The study aimed to reduce the supply chain cost in a network of agricultural supply chains. ...
This study contributes to dairy supply chain due to the lack of food waste and environmental pollutants. The objective of this study is to minimize the total system cost and reduce carbon emissions and food waste for a dairy supply chain with multi-echelons. The system consists of multiple farmers, single processing plant, multi-distributor, and multi-retail customer. This study presents a multi-objective mixed-integer linear programming (MOMILP). A MOMILP proposed model was validated to provide insights into dairy industries. A real case study of dairy milk problems in West Java (Indonesia) was solved by using optimization software. The optimal results show that applying the proposed model can minimize total costs, reduce food waste, and minimize environmental pollutants. Finally, the results of sensitivity analysis show that the total supply chain costs and food waste costs are significantly influenced by variance of temperature in fresh milk storage.
... They use techniques such as selection, crossover (recombination), and mutation to evolve a population of potential solutions over multiple generations. GAs maintain a diverse population, allowing them to explore a broad range of solutions and converge toward optimal or near-optimal solutions for various optimization problems [50][51][52]. ...
Reliable and accurate brain tumor segmentation is a challenging task even with the appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic Resonance Imaging (MRI) are necessary steps for correct diagnosis and treatment planning. There are different MRI sequence images (T1, Flair, T1ce, T2, etc.) for identifying different parts of the tumor. Due to the diversity in the illumination of each brain imaging modality, different information and details can be obtained from each input modality. Therefore, by using various MRI modalities, the diagnosis system is capable of finding more unique details that lead to a better segmentation result, especially in fuzzy borders. In this study, to achieve an automatic and robust brain tumor segmentation framework using four MRI sequence images, an optimized Convolutional Neural Network (CNN) is proposed. All weight and bias values of the CNN model are adjusted using an Improved Chimp Optimization Algorithm (IChOA). In the first step, all four input images are normalized to find some potential areas of the existing tumor. Next, by employing the IChOA, the best features are selected using a Support Vector Machine (SVM) classifier. Finally, the best-extracted features are fed to the optimized CNN model to classify each object for brain tumor segmentation. Accordingly, the proposed IChOA is utilized for feature selection and optimizing Hyperparameters in the CNN model. The experimental outcomes conducted on the BRATS 2018 dataset demonstrate superior performance (Precision of 97.41 %, Recall of 95.78 %, and Dice Score of 97.04 %) compared to the existing frameworks.
... Indeed, the objective of avoiding premature convergence and stagnation states can be achieved by selection, replacement, or diversity operators, which ensure the diversity of solutions in the search population or memory [74]. Furthermore, the alternation of the intensification and diversification phases enables profitable exploitation of solutions considered to be the best, as well as the exploration of new search regions [75]. The alternation between the two phases is very useful, as the first guarantees improved solution quality through local search, crossover, or hill climbing, while the second is responsible for generating new solutions due to mutation, perturbation, or restart phenomena [72]. ...
Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The MGO mimics the social life and hierarchy of mountain gazelles in the wild. The MGO was compared with well-recognized recent algorithms, which were the Grey Wolf Optimizer (GWO), the Squirrel Search Algorithm (SSA), the Differential Evolution (DE) algorithm, the Bat–Artificial Bee Colony Optimizer (BABCO), the Bat Algorithm (BA), Multiswarm Spiral Leader Particle Swarm Optimization (M-SLPSO), the Guaranteed Convergence Particle Swarm Optimization algorithm (GCPSO), Triple-Phase Teaching–Learning-Based Optimization (TPTLBO), the Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO), the quasi-Opposition-Based Learning Whale Optimization Algorithm (OBLWOA), and the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO). The experimental findings and statistical studies proved that the MGO outperformed the competing techniques in identifying the parameters of the Single-Diode Model (SDM) and the Double-Diode Model (DDM) PV models of Photowatt-PWP201 (polycrystalline) and STM6-40/36 (monocrystalline). The RMSEs of the MGO on the SDM and the DDM of Photowatt-PWP201 and STM6-40/36 were 2.042717 ×10−3, 1.387641 ×10−3, 1.719946 ×10−3, and 1.686104 ×10−3, respectively. Overall, the identified results highlighted that the MGO-based approach featured a fast processing time and steady convergence while retaining a high level of accuracy in the achieved solution.
... Circular economy experts suggest a combination of reducing virgin plastic resins with bioresins and waste collection recycling processes, which collectively account for a growing USD 600 billion annual market (Mauch 2016 [26]). However, similar to what has been tested in the industrial crops and products, medicine delivery, biofuel, and consumer product industries, circular CLSCs are the most efficient way to iterate meta-heuristic learning toward neutral production (Gholian-Jouybari et al., 2023 [35,38]; Momenitabar et al., 2022 [30]; Azevedo et al., 2019 [31]). ...
Correcting inefficiencies in the supply chain requires us to reimagine manufacturing by recapturing processes—particularly material sourcing and end-use recycling, which create vast amounts of waste. Inefficiencies in the supply chain create massive waste and stifle innovation in manufacturing, both well-established concerns for the environment. Carbon-based fuels and products are detrimental to the land, air, and sea. Single-use products made from toxic materials flood the food and medical supply chains. Businesses are increasingly moving toward the single purchasing platform model (for example, Uber and Airbnb). Following that model, this paper proposes a platform as a service (PaaS) manufacturing sharing service that matches small- to mid-size manufacturers with production capacity as a solution to obtaining ethically sourced products at a competitive price while offering access to last-mile delivery locally on a single purchasing platform. The development of an Internet of Things (IoT) platform can achieve these four things: (1) provide better coordination of the sourcing and supply of materials, (2) ensure effective provisions of eco-friendly and recycled inputs, (3) provide efficient distribution of equipment and manufacturing resources, and (4) shorten the supply chain by centralizing and coordinating last-mile delivery.
... In agriculture, Salehi-Amiri et al. [10] designed a new closed-loop supply chain network for the walnut industry and established a new mixed integer linear programming which not only meets the needs of various markets but also makes preparations for the secondary use of returned products. Rajabi-Kafshgar et al. [11] considered the environmental impact of agricultural waste and the safety of agricultural products and established a new hybrid linear mathematical model for agricultural supply chain networks to minimize the fixed and variable total costs of closed-loop supply chains. In the e-commerce industry, Prajapati et al. [12] developed a sustainable framework and a mixed integer nonlinear programming model for the multi-level and multi-electronic product closed-loop supply chain in the e-commerce industry to reduce the total costs associated with the forward and reverse flow of goods in the closed-loop supply chain and maximize the revenue. ...
The recycling of waste products can bring enormous economic and environmental benefits to supply chain participants. Under the government’s reward and punishment system, the manufacturing industry is facing unfolded pressure to minimize carbon emissions. However, various factors related to the design of closed-loop logistics networks are uncertain in nature, including demand, facility capacity, transportation cost per unit of product per kilometer, landfill cost, unit carbon penalty cost, and carbon reward amount. As such, this study proposes a new fuzzy programming model for closed-loop supply chain network design which directly relies on fuzzy methods based on the necessity measure. The objective of the proposed optimization model is to minimize the total cost of the network and the sum of carbon rewards and penalties when selecting facility locations and transportation routes between network nodes. Based on the characteristics of the problem, a genetic algorithm based on variant priority encoding is proposed as a solution. This new solution encoding method can make up for the shortcomings of the four traditional encoding methods (i.e., Prüfer number-based encoding, spanning tree-based encoding, forest data structure-based encoding, and priority-based encoding) to speed up the computational time of the solution algorithm. Several alternative solution approaches were considered to evaluate the proposed algorithm including the precision optimization method (CPLEX) and priority-based encoding genetic algorithm. The results of numerous experiments indicated that even for large-scale numerical examples, the proposed algorithm can create optimal and high-quality solutions within acceptable computational time. The applicability of the model was demonstrated through a sensitivity analysis which was conducted by changing the parameters of the model and providing some important management insights. When external parameters change, the solution of the model maintains a certain level of satisfaction conservatism. At the same time, the changes in the penalty cost and reward amount per unit of carbon emissions have a significant impact on the carbon penalty revenue and total cost. The results of this study are expected to provide scientific support to relevant supply chain enterprises and stakeholders.
... On the other hand, the pressures of regulations and increasing customer information have led to increased attention to social and environmental issues in the SCs of many businesses (Hosseini Dehshiri & Amiri, 2024;Rajabi-Kafshgar, Gholian-Jouybari, Seyedi, & Hajiaghaei-Keshteli, 2023). In this regard, due to the importance of paying attention to economic issues and environmental and social concerns around products, the Closed-Loop Supply Chain (CLSC) has been taken into respect (Mirzaei, Goodarzian, Mokhtari, Yazdani, & Shokri, 2023). ...
Due to the significance of environmental and economic topics and limited resources, integrating Circular Economy (CE) principles is necessary for Supply Chain (SC) to improve sustainable competitive advantage. The integration of CE in SCs leads to a Closed-Loop Supply Chain Network Design (CLSCND) and a circular SC to achieve the economic, environmental, and social aspects of outputs and processes. On the other hand, the integration of CE in CLSCND faces hybrid uncertainties in different time horizons and scenarios due to the strategic and long-term nature of decisions. Due to the impact of random and cognitive uncertainties in the long term, it is necessary to consider these factors in the integration of CE in CLSCND. So, the aim of the present study is CLSCND to achieve the principles of CE and provide a robust scenario-based possibilistic-stochastic programming approach to consider cognitive and random uncertainties simultaneously. The contributions and innovations of the present study are the integration of CE in CLSCND, considering cognitive and random uncertainties at the same time, developing the Me criterion to achieve flexible solutions based on the convex combination of opinions of experts, and proposing the use of the absolute possible deviation to consider the possibilistic deviation. A case study was investigated for CLSCND in the paper industry to assess the presented approach, and the results indicated the accuracy and robustness of the solutions. The numerical simulation results demonstrated the appropriate performance of the proposed method, that the lowest average and standard deviation values of the constraints violation were 601 and 48, respectively, in the developed approach. Analytical findings show that implementing CE in the paper CLSCND provides insights for managers in demand and capacity constraint violations based on different risk levels. Also, this study offers a comprehensive framework for presenting robustness and flexible solutions in CLSC problems based on changes in robustness coefficients and sensitivity analysis of parameters of the CLSCND, which can create a trade-off between CE objectives in uncertain parameters.
The Food Supply Chain typically refers to the processes involved in producing and distributing
food, taking it from farms to consumers’ homes. This research presents a robust modular capacity model for designing a wheat supply chain network, incorporating multi-level, multi-product,
and multi-period considerations. The model incorporates key factors such as sleep periods, wheat
quality, inter-regional transport, and flour extraction rates. Unlike previous studies that primarily
focussed on national-level modelling, this study emphasises regional data for network design. This
approach enables more precise and practical decision-making, tailored to specific regional needs
and conditions. Regarding demand and supply fluctuations, the system optimises storage capacity
at varying levels throughout the planning horizon. This approach minimises surplus infrastructure
costs and avoids expenses associated with unused capacity. A modular capacity management strategy is employed to determine silo capacities effectively. The robust optimisation approach is applied
to handle uncertainties in demand and transportation costs. The study evaluates three different
approaches for conservatism levels in the optimisation process. Also through the real data, the
model demonstrates its applicability, and the model addresses challenges such as facility underutilisation, and fluctuating demand. Sensitivity analyses reveal the trade-offs between cost efficiency and robustness. The results highlight the importance of modular infrastructure, dynamic inventory
management, and resilience to uncertainties.
Purpose
The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM.
Design/methodology/approach
This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework.
Findings
This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance.
Originality/value
This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.
Modern industries use advanced technological solutions to efficiently manage waste and increase the sustainability of supply chain (SC) networks. Technologies in SC networks can achieve this by establishing a link between circular economy principles and Industry 4.0. Therefore, in this study, for the first time, a novel bi‐objective mixed‐integer linear programming model is developed to design a sustainable SC network by considering circular economy practices, the Internet of Things (IoT) technology, and the location‐routing problem with a simultaneous pickup‐and‐delivery (P&D) strategy in the electric vehicle (EV) battery industry. The proposed model locates distribution and recycling centers and considers the routing problem with the P&D strategy between the distribution centers and service providers. By applying the augmented epsilon‐constraint method, a balance is established between minimizing total costs and carbon emissions, and a set of Pareto‐optimal solutions is provided. It should be noted that the P&D strategy reduces the total costs and carbon emissions, and the IoT technology provides conditions for designing a secure and traceable network. The performance of the proposed model is evaluated using data from an EV battery module manufacturing company in the Far East. A sensitivity analysis of the parameters related to operational costs, carbon emissions, and demand demonstrated the accuracy of the performance of the proposed model.
The circular economy (CE) within the agricultural supply chains (SC) assumes efficient and sustainable closed-loop SCs. This means collecting, reusing, or recycling the loss and waste to create energy, fertilizer, and other applications. This chapter aims to analyze a closed-loop citrus SC by constructing a multi-objective system dynamics model, which will maximize the profits of the stakeholders while minimizing citrus losses and minimizing the released CO2 emissions of 1 kg of citrus from the farm and 1 L of juice from processing. Thereby, the model was tested and validated according to the data collected from the case study. The results from the simulation model revealed that returning citrus loss and waste (CLW) as fertilizer to close the loop in the farm stage would lead to an increase in farmers’ profit and a reduction in the CO2 emission of 1 kg of citrus from the farm and 1 L of juice from processing 29% and 26%, respectively. Furthermore, upcycling CLW at the processing stage by using it to extract essential oil increases processor profit by 21%. A closed-loop citrus SC would eliminate CLW, enhance the profitability of stakeholders, and cut CO2 emissions. However, collaboration between stakeholders is crucial to adopting a CE within citrus SC, especially if they would generate value from CLW. Future research would consider citrus SC from other countries to examine its applicability to other by-products.
This paper aims to showcase the potential application of the metaheuristic approach Quadratic Approximation based Jaya (JaQA) in addressing a single-objective uncertain Fixed Charge Transportation Problem with Damageable items (FCTPD). The incorporation of uncertainties and damage rates for items adds realism to the selected problem model. Subsequently, a comprehensive examination of the FCTPD model is conducted, considering both expected value and chance-constrained cases based on uncertainty theory. Equivalent deterministic formulations are considered for these scenarios. The performance of JaQA is comprehensively benchmarked on a set of ten popular benchmarks of single-objective fixed charge transportation problems (FCTP) from the OR library. In addition to this, it is also evaluated on the deterministic forms of the expected value and chance-constrained cases, and the computational results are compared with other widely recognized approaches such as Jaya algorithm, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for five numerical instances A thorough investigation of statistical, convergence, and sensitivity analysis are presented to shed light on the efficacy and stability levels of JaQA over its counterparts. The simulation outcomes and exhaustive statistical analysis show that for all the problems considered, JaQA performed better out of GA, PSO, and Jaya. More specifically, it indicated that the utilization of JaQA provided significant profit maximization of the transportation system, thereby ensuring a promising value of the optimal solution.
Extreme weather events such as droughts have posed a significant risk to the agricultural economy of the semi-arid region in the American Southwest. To address the potential drought scenarios, which impact the precipitation and water availability, a data-driven multistage stochastic optimization model is constructed for crop rotation and water irrigation scheduling, to maximize the expected farmers’ profits over a planning horizon. The optimal decisions will be made for crop rotations, deficit level for water irrigation, crop yield response, and multi-method irrigation system scheduling. To overcome solving the multistage stochastic large-scale mixed-integer optimization model with the exponentially growing number of scenarios, we employ the stochastic dual dynamic integer programming (SDDiP) method. Numerical experiments and sensitivity analysis on drought scenarios are performed to validate the proposed approaches in a case study in Arizona.
Nowadays, the urban cities are facing increasing strain due to the swift increase in population inside metropolitan areas. It is anticipated that the centered strategy for smart cities will address both the ecological environment and urban life. The food business is one of major IoT application areas in smart cities. In smart cities, IoT technology aid in the real-time monitoring, analysis, and management of the food business. In this study, an Internet of Things (IoT) based Dynamical Food Chain of Supply for Smart Cities using Dynamic Vehicle Routing (IFSCDVR) using Fire Hawk Optimization (FHO) technique is suggested, which guarantees food quality while also offering intelligent vehicle routing and the ability to identify the reasons for contamination of food. This strategy would increase the effectiveness and precision of the supply chain network with the smallest possible dataset size. The findings demonstrate that the suggested system works better than the current methodologies. The proposed IFSCDVR-FHO achieved overall performance of 91.34% of accuracy, 89.56% of precision, 89.68% of recall and 90.21% of f1. score.
The sine cosine algorithm (SCA) is a metaheuristic algorithm that employs the characteristics of sine and cosine trigonometric functions. SCA’s deficiencies include a tendency to get trapped in local optima, exploration–exploitation imbalance, and poor accuracy, which limit its effectiveness in solving complex optimization problems. To address these limitations, a multi-trial vector-based sine cosine algorithm (MTV-SCA) is proposed in this study. In MTV-SCA, a sufficient number of search strategies incorporating three control parameters are adapted through a multi-trial vector (MTV) approach to achieve specific objectives during the search process. The major contribution of this study is employing four distinct search strategies, each adapted to preserve the equilibrium between exploration and exploitation and avoid premature convergence during optimization. The strategies utilize different sinusoidal and cosinusoidal parameters to improve the algorithm’s performance. The effectiveness of MTV-SCA was evaluated using benchmark functions of CEC 2018 and compared to state-of-the-art, well-established, CEC 2017 winner algorithms and recent optimization algorithms. The results demonstrate that the MTV-SCA outperforms the traditional SCA and other optimization algorithms in terms of convergence speed, accuracy, and the capability to avoid premature convergence. Moreover, the Friedman and Wilcoxon signed-rank tests were employed to statistically analyze the experimental results, validating that the MTV-SCA significantly surpasses other comparative algorithms. The real-world applicability of this algorithm is also demonstrated by optimizing six non-convex constrained optimization problems in engineering design. The experimental results indicate that MTV-SCA can effectively handle complex optimization challenges.
An important stage of the agri-food supply chain is production (agricultural practices and harvesting), where relevant decisions are made in a short period of intensive activity. Regarding the major fruit supply chain, the complexity of these decisions increases because most fruit export companies manage several orchards with different fruit species and varieties. Moreover, the amount of labor and resources must be estimated at least two months in advance of the harvest so that they can be relied upon during the harvest season. In this study, a multi-objective modeling approach for supporting tactical harvest planning decisions of major fruits is developed, aiming to evidence its adequacy and to identify different harvest plans according to the prioritized objectives. Therefore, a multi-objective linear programming (MOLP) model, which considers three conflicting objectives, is proposed. The three objectives correspond to minimizing harvest costs, fruit loss and harvest days. For solving the MOLP model, five a posteriori multi-objective exact methods are compared using a synthetic instance based on a real case. The Pareto frontiers obtained by these methods are analyzed using three metrics related to accuracy, cardinality, and diversity criteria, so that the most suitable method can be selected. Thus, the selected method is applied to an actual case study of a Chilean fruit export company. In addition, the ε-constraint method is used in this case study to compare the obtention of non-dominated solutions in the convex and non-convex regions of the Pareto frontier. The results show that harvest plans vary significantly according to the prioritization of the objective functions. Thus, the percentage difference between each objective function’s worst and best values is around 497% when the harvest costs are minimized, around 1,448% when the fruit loss is minimized, and around 18% when the number of harvest days is minimized. In addition, the adequacy of the multi-objective approach rather than the mono-objective approach to support tactical harvest planning decisions is demonstrated. Furthermore, a scenario analysis for considering changes in the harvest periods is carried out by considering three possible scenarios. This analysis is required because these periods can change every year due to different factors, such as weather conditions and agricultural practices, among others. The results show that the harvest costs and fruit loss increase, on average, 87% and 100%, respectively, when the harvest period is short, that is, one day. Finally, managerial insights about the information obtained by the MOLP model are discussed.
In the past decade, the implementation of the circular economy has emerged as a compelling alternative to the linear model, proving effective in addressing critical global challenges such as climate change, population growth, and the depletion of non-renewable resources. Integrating the circular economy into supply chain networks results in the formation of a circular supply chain. Regrettably, many industries, including agriculture, have not fully embraced this valuable approach within their supply chain networks. This study addresses this gap by presenting an integrated circular supply chain network designed specifically for citrus fruits. The proposed model is grounded in the fundamental principles of the circular economy, leveraging both closed and open loops simultaneously. To address the complexities, a set of exact and metaheuristic methods is employed. In contrast to previous works, a comprehensive comparison of circular, closed-loop, and linear supply network structures is undertaken. Moreover, a sensitivity analysis is performed on key parameters. The model achieves significant milestones: firstly, a zero-waste outcome by eliminating waste in plastic crate usage and fruit residuals; secondly, an impressive 42% reduction in water consumption compared to traditional linear models. Environmental sustainability is further demonstrated by a 3% reduction in carbon emissions within the circular model. Importantly, these achievements do not compromise economic efficiency, as the circular supply chain showcases a nearly 1% decrease in total costs compared to its linear counterparts.
A closed-loop option for reusing, remanufacturing, and recycling waste products in the olive industry creates a high added value for this business network. This fact motivates the implementation of closed-loop supply chains for adopting an efficient and sustainable strategy in the olive industry in Iran. As one of the developing countries, Iran must redesign their supply chain networks to meet the standards of sustainable development goals. In this regard, the triple bottom line approach focuses on a sustainable design considering all economic, environmental, and social factors in supply chain networks. The main novelty of this paper is to merge the sustainable Closed-Loop Supply Chain Network (CLSCN) design and the olive industry. Hence, a multi-objective optimization framework is proposed to make location, allocation, and inventory decisions for the considered problem. Based on the triple bottom line approach, the objectives of the optimization model are to minimize the total cost and carbon dioxide (CO2) emissions and maximize job opportunities. In small instances, the model is solved by an epsilon-constraint method. To address the high complexity of large-scale networks, this study innovates new hybrid optimization algorithms. In this regard, a hybrid of Virus Colony Search Algorithm (VCS) and Simulated Annealing (SA) and a combination of Electromagnetism-like Algorithm (EMA) and Genetic Algorithm (GA) are proposed for the first time. To confirm their efficiency, an extensive comparison with individual algorithms is carried out by different multi-objective optimization metrics. Finally, some sensitivity analyses are performed to discuss some practical insights for the supply chain managers working in the olive industry in Iran.
Home Health Care Supply Chain (HHCSC) is an interesting and widespread topic in developed countries due to an aging population's care. Home care services aim to provide general hygiene care and nursing considering dependency for older people. One of the real circumstances in such systems is that the two parties, caregivers and patients, adopt their decisions at different decision levels. From the perspective of HHCSC companies, the position of pharmacies, scheduling and routing of nurses, and the quantity of releasing drugs have been important and open issues. Another real supposition in HHCSC networks is that the patients choose their company and its services according to its cost. Contrary to previous works, here, we firstly consider outsourcing for patients’ demand to receive direct services from a hospital as a real assumption. Besides, this work develops a bi-level programming model as a static Stackelberg game between nurses and patients within the framework of HHCSC. Another novelty of this work is to utilize capable meta-heuristics reported in the literature as well as hybrid ones. To validate the application of meta-heuristics in small sizes, a solution procedure combining a heuristic and exact method is utilized. Finally, comparative analysis of the algorithm, probing the model using a real-case study, and some sensitivity analyses are done.
The sugarcane industry is technologically pioneering in the area of food production. On the other side, this industry produces a huge amount of by-products. Proper handling of these by-products has remained a challenge. An efficient multi-echelon Sugarcane Supply Chain Network (SSCN) is designed and proposed in this paper to handle the by-products produced by the sugarcane industry that can be utilized further with little modification. It helps to reduce the overall working cost of the network. Usually, the supply chain problems are complex in nature, and complexity further increases with increasing problem instances. Metaheuristics techniques are, in general, applied to handle such NP-hard problems. This work proposes three hybrid metaheuristics algorithms, namely H-GASA, a hybrid of Genetic Algorithm with Simulated Annealing, H-KASA, a hybrid of Keshtel Algorithm with Simulated Annealing, and H-RDASA, a hybrid of Red Deer Algorithm with Simulated Annealing to handle the complexity of the problem. The algorithms’ performance is probed using the Taguchi experiments, and the best combinations of parameters are identified. This hybrid algorithms’ efficacy is compared with their basic version of the algorithms, i.e. GA, KA, RDA, and SA using different criteria. A set of test problems is generated to ensure the capability of the presented model. The obtained results suggest that H-KASA significantly outperforms in small-sized problems, while the H-RDASA significantly outperforms in medium- and large-sized problem instances. In addition, the sensitivity analysis confirms that by adopting this proposed multi-echelon SSCN, decision-makers can achieve a significant cost reduction of 8.3% in terms of the total cost.
Recent developments in food industries have attracted both academic and industrial practitioners. Shrimp as a well-known, rich, and sought-after seafood, is generally obtained from either marine environments or aquaculture. Central prominence of Shrimp Supply Chain (SSC) is brought about by numerous factors such as high demand, market price, and diverse fisheries or aquaculture locations. In this respect, this paper considers SSC as a set of distribution centers, wholesalers, shrimp processing factories, markets, shrimp waste powder factory, and shrimp waste powder market. Subsequently, a mathematical model is proposed for the SSC, whose aim is to minimize the total cost through the supply chain. The SSC model is NP- hard and is not able to solve large-size problems. Therefore, three well-known metaheuristics accompanied by two hybrid ones are exerted. Moreover, a real-world application with 15 test problems are established to validate the model. Finally, the results confirm that the SSC model and the solution methods are effective and useful to achieve cost savings.
A smart city (SC) is a sustainable and efficient urban center that provides a high quality of life to its inhabitants through optimal management of its resources that nowadays have been wider and wider. In modern societies, municipal solid waste management (MSWM) is an important part of SCs, the main problem of MSWM is the cost that it generates and must be reduced. To solve this situation in this paper are considered two sub-models. The first sub-model uses vehicle routing problem (VRP) for routing fleet among generation waste to separation facilities. The second sub-model is designed to allocate resources from separation facilities to set of recovery plants or landfill centers. From the best of our knowledge, most of the past studies related to this topic have focused only on deterministic implementations. Also, recent studies usually focus on uncertain parameters in the area of waste generation. In addition, a few related studies have developed the uncertain parameter which has focused on facilitating separation. This study considers the uncertain parameters in the output rate of separation facilities as well as the importance of value recovery from each bin; the aim is to enhance the efficiency of operations. The purpose of this study is to minimize the total transportation cost and to maximize recycled revenue. Chance-constrained programming has been used to deal with stochastic optimization model. Four metaheuristic algorithms are employed to identify the best solution. Besides, the performance of the proposed algorithms is evaluated. Finally, sensitivity analyses along with number of scenarios have developed to measure the tightness of the proposed problem. The results of the study illustrate the optimized number of vehicles that can help the managers and decision-makers in various tightness conditions.
Current national and international regulations, along with growing environmental concerns, have deeply influenced the design of supply chain networks. These decisions stem from the fact that decision-makers try to design the supply chain network to align with their economic and environmental objectives. In this paper, a new closed-loop supply chain network with sales agency and customers is formulated. The proposed model has four echelons in the forward direction and five echelons in the backwards direction. The model not only considers several constraints from previous studies, but also addresses new constraints in order to better explore real-life problems that employ different transportation modes and that rely on sale agency centers. The objective function is to maximize the total profit. In addition, this study firstly considers distinct cluster of customers based on the product life cycle. These customers are utilized in different levels of the proposed network in order to purchase the final products, returned products, and recycled products. The structure of the model is based on linear mixed-integer programming, and the proposed model has been investigated through a case study regarding the manufacturing industry. To verify the model efficiency, a set of metaheuristics and hybrid algorithm are applied in various test problems along with a data from a real-world case study in a building construction industry. The findings of the proposed network illustrated that using the attributes of sale agency centers and clusters of customers both increase the problem total revenue and the number of the collected returned products.
Supply chain network design in perishable product has become a challenging task due to its short life time, spoilage of product in degradation nature and stochastic market demand. This paper focused on designing and optimizing model for perishable product in stochastic demand, which comprises multiple levels from producer, local collector, wholesaler and retailers. The ultimate goal is to optimize availability and net profit of all members in supply chain network model for avocado fruit under stochastic demand. The network model has considered the quality deterioration rate of the product with increased order of transportation time. The validity of developed model was tested with data collected from avocado supply chain network in Ethiopian market.
Most cities, notably major and agricultural ones, are faced with environmental and waste problems. Distribution and collection of agricultural crops can be challenging duties as world demand and production are substantially increased. Accordingly, resource depletion, environmental concern, and the importance of the circular economy have convinced this research group to focus on a Closed-Loop Supply Chain (CLSC) network design. In this study, a new mixed linear mathematical model for a CLSC was developed which minimizes the CLSC’s total costs and which tackles and controls air pollution. Contrary to previous works about supply chain network design, we firstly consider citrus fruits’ crates in our model. To solve the model, two leading algorithms, Genetic Algorithm and Simulated Annealing, are employed and a third recently successful method, Keshtel Algorithm, is utilized. Further, two hybridization algorithms stemmed from mentioned ones are applied. Finally, the results are assessed by different criteria and compared, and then the two best algorithms are chosen in this case. Consequently, in order to achieve the most effective result, a real case study of crates was conducted. The results obviously presented applicability and efficiency of the proposed model. Thus, the most suitable network for CLSC of citrus fruits’ crates was designed in which the costs and emissions were reduced.
High-fat diet (HFD) induces inflammation and microbial dysbiosis, which are components of the metabolic syndrome. Nutritional strategies can be a valid tool to prevent metabolic and inflammatory diseases. The aim of the present study was to evaluate if the chronic intake of pistachio prevents obesity-associated inflammation and dysbiosis in HFD-fed mice. Three groups of male mice (four weeks old; n = 8 per group) were fed for 16 weeks with a standard diet (STD), HFD, or HFD supplemented with pistachios (HFD-P; 180 g/kg of HFD). Serum, hepatic and adipose tissue inflammation markers were analyzed in HFD-P animals and compared to HFD and STD groups. Measures of inflammation, obesity, and intestinal integrity were assessed. Fecal samples were collected for gut microbiota analysis. Serum TNF-α and IL-1β levels were significantly reduced in HFD-P compared to HFD. Number and area of adipocytes, crown-like structure density, IL-1β, TNF-α, F4-80, and CCL-2 mRNA expression levels were significantly reduced in HFD-P subcutaneous and visceral adipose tissues, compared to HFD. A significant reduction in the number of inflammatory foci and IL-1β and CCL-2 gene expression was observed in the liver of HFD-P mice compared with HFD. Firmicutes/Bacteroidetes ratio was reduced in HFD-P mice in comparison to the HFD group. A pistachio diet significantly increased abundance of healthy bacteria genera such as Parabacteroides, Dorea, Allobaculum, Turicibacter, Lactobacillus, and Anaeroplasma, and greatly reduced bacteria associated with inflammation, such as Oscillospira, Desulfovibrio, Coprobacillus, and Bilophila. The intestinal conductance was lower in HFD-P mice than in the HFD mice, suggesting an improvement in the gut barrier function. The results of the present study showed that regular pistachio consumption improved inflammation in obese mice. The positive effects could be related to positive modulation of the microbiota composition.
In this paper, we propose a solution to the sustainable closed-loop supply chain (SCLSC) design problem. Three factors (economic, environmental, and social) are considered for the problem and the three following requirements are addressed while satisfying associated constraint conditions: (i) minimizing the total cost; (ii) minimizing the total amount of CO2 emission during production and transportation of products; (iii) maximizing the social influence. Further, to ensure the efficient distribution of products through the SCLSC network, three types of distribution channels (normal delivery, direct delivery, and direct shipment) are considered, enabling a reformulation of the problem as a multi-objective optimization problem that can be solved using Pareto optimal solutions. A mathematical formulation is proposed for the problem, and it is solved using a hybrid genetic algorithm (pro-HGA) approach. The performance of the pro-HGA approach is compared with those of other conventional approaches at varying scales, and the performances of the SCLSC design problems with and without three types of distribution channels are also compared. Finally, we prove that the pro-HGA approach outperforms its competitors, and that the SCLSC design problem with three types of distribution channels is more efficient than that with a single distribution channel.
Closed-loop supply chain (CLSC) as one of the important configurations of the circular economy (CE) has received considerable attention in sustainability matters. It is composed of characteristics that, when identified, studied, and categorized, help not only to a better understanding of the current contributions in the literature but also lead to formulating new models. This research presents one of the first in-depth studies to investigate factors influencing CLSCs. It concerns the investigation of the models which are designed based on the game theory (GT). Therefore, the reviewed works focused on cooperation and competition among the game participants. A systematic literature review is implemented as a four-step process consisting of material collection, a descriptive analysis, category selection, and evaluation stage to review and discuss the works that focus on CLSC and use GT simultaneously. A content-based analysis is carried for the final works, which include 215 papers. The identified characteristics of these papers are classified into 12 main categories. Moreover, they are divided into subcategories to highlight the contribution of each paper. Accordingly, results are derived, and gaps are explained.
The truck scheduling problem is one of the most challenging and important types of scheduling with a large number of real-world applications in the area of logistics and cross-docking systems. This problem is formulated to find an optimal condition for both receiving and shipping trucks sequences. Due to the difficulty of the practicality of the truck scheduling problem for large-scale cases, the literature has shown that there is a chance, even with low possibility, for a new optimizer to outperform existing algorithms for this optimization problem. Already applied successfully to solve similar complicated optimization problems, the Social Engineering Optimizer (SEO) inspired by social engineering phenomena, has been never applied to the truck scheduling problem. This motivates us to develop a set of novel modifications of the recently-developed SEO. To validate these optimizers, they are evaluated by solving a set of standard benchmark functions. All the algorithms have been calibrated by the Taguchi experimental design approach to further enhance their optimization performance. In addition to some benchmarks of truck scheduling, a real case study to prove the proposed problem is utilized to show the high-efficiency of the developed optimizers in a real situation. The results indicate that the proposed modifications of SEO considerably outperform other existing algorithms in this research area and provide very competitive results.
In recent decades, reverse logistics has garnered considerable attention since it recovers value of returning products, satisfies environmental requirements, and pays attention to customers’ rights. Suppliers, as the first layer of the supply chain network, pose a great impact on environmental pollution. Therefore, in this paper a hybrid approach of fuzzy analysis network process (FANP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL), and multi-objective mixed-integer linear programming (MOMILP) models are developed for circular supplier selection and order allocation in a multi-product circular closed-loop supply chain (C-CLSC) considering multi-depot, capacitated green routing problem using heterogeneous vehicles. In this regard, a mathematical model concerning an inventory-location-routing problem is developed that minimises cost and shortage simultaneously and also deals with imposed uncertainties. A fuzzy solution approach is proposed to simultaneously incorporate uncertainty and to change the multi-objective model into a single-objective model. To motivate the practical aspect of the proposed model in real world applications, we applied the model to an automotive timing belt manufacturer. The obtained results indicate that the proposed model is cost efficient and environmentally friendly for CLSC network designs.
Nowadays, there is a great deal of interest in developing supply chain networks with the goal of optimisation. This paper addresses a new stochastic optimisation model by using a two-stage stochastic programming for a closed-loop supply chain network design problem. To control the uncertainty circumstances of manufacturing of products, customers’ demand, prices of products and its returned rates, the financial risk is considered as a distinguished objective function to find a robust solution. To address the developed model, a new recent nature-inspired algorithm, namely, Whale Optimisation Algorithm (WOA) and also Particle Swarm Optimisation (PSO) as a popular method in swarm intelligence algorithms are utilised in this study. In addition to this development, Genetic Algorithm (GA) and Simulated Annealing (SA) as the well-known metaheuristics used repeatedly among the literature are employed to have a comprehensive comparison. In order to be faired, a popular method for calibration called Response Surface Method (RSM) is applied to tune the algorithms’ parameters. Finally, some evaluation metrics to assess the Pareto optimal fronts of algorithms’ quality are considered to conduct a comparative study using statistical analyses and some sensitivity analyses are performed through an industrial example.
Aflatoxin B 1 , produced by Aspergillus flavus Link ex Fries and A. parasiticus Speare on maize (Zea mays L.), is a potent carcinogen that is linked to liver cancer in animals and humans. Normally, genetic materials are field-inoculated and evaluated by conducting aflatoxin assays in the laboratory to develop host resistance (sporophytic selection). These procedures are costly and time-consuming and limit the amount of genetic material that can be evaluated. Microgametophytic selection (pollen selection) in maize has not been explored for screening genetic material for resistance to Aspergillus spp. and aflatoxin. The objective of this investigation was to examine in vitro pollen germination in the presence of A. flavus and A. parasiticus conidia, and aflatoxin B 1 in the F 2 generation of a cross between Mp313E (resistant inbred) and SC212M (sus-ceptible inbred). A. flavus conidia significantly inhibited pollen grain germination in comparison with the control and the other two treatments. A. parasiticus and aflatoxin B 1 treatments did not differ significantly from each other but differed from the control, suggesting that A. flavus conidia, instead of afla-toxin, could be used for screening germplasm for resistance to Aspergillus and/or aflatoxin. We concluded that pollen grains containing genes for susceptibility to A. flavus can be eliminated in a segregating population and that microgametophy-tic screening could greatly facilitate recurrent selection and screening of germplasm for resistance to aflatoxin at a much less cost and in a shorter period than field testing. Pollen grains carrying resistance genes can be manipulated to develop recombinant inbred lines to accelerate development of parental inbred lines to produce hybrids possessing tolerance to A. flavus and/or aflatoxin B 1.
Cloud computing is an Internet-based approach in which all applications and files are hosted in a cloud consisting of thousands of computers that are linked in complex ways. The major challenge of cloud data centers is to show how the millions of requests of final users are correctly and effectively being investigated and serviced. Load-balancing techniques are needed to increase the flexibility and scalability of cloud data centers. Load-balancing technique is one of the most significant issues in the distributed computing system. Since there are large-scale resources and a lot of user demands in cloud computing load-balancing problem, it could be the main reason that many researchers considered and addressed that as an NP-hard problem. Therefore, some heuristics algorithms such as imperialist competitive algorithm (ICA) and firefly algorithm (FA) had been proposed by previous researchers to solve the mentioned problem. Although ICA and FA could get an approximate satisfying result in solving the cloud computing load-balancing problem, obtaining the better result means to make improvements in makespan, CPU time, load balancing, stability and planning speed. The motivation of this research is proposing an intelligent meta-heuristic algorithm based on the combination of ICA and FA to get the mentioned required result. Local search ability of FA can reinforce ICA algorithm. The obtained result of this research showed dramatic improvements in makespan, CPU time, load balancing, stability and planning speed.
This study investigates the optimization of non-permutation flow-shop scheduling problems and lot-sizing simultaneously. Contrary to previous works, we first study the energy awareness of non-permutation flow-shop scheduling and lot-sizing using modified novel meta-heuristic algorithms. In this regard, first, a mixed-integer linear mathematical model is proposed. This model aimed to determine the size of each sub-category and determine each machine's speed within each sub-category to minimize makespan and total consumed energy simultaneously. In order to optimize this model, Multi-objective Ant Lion Optimizer (MOALO), Multi-objective Keshtel Algorithm (MOKA), and Multi-objective Keshtel and Social Engineering Optimizer (MOKSEA) are proposed. First, the validation of the mathematical model is evaluated by implementing it in a real case of the food industry using GAMS software. Next, the Taguchi design of the experiment is applied to adjust the meta-heuristic algorithms' parameters. Then the efficiency of these meta-heuristic algorithms is evaluated by comparing with Epsilon-constraint (EPC), Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO) using several test problems. The results demonstrated that the MOALO, MOKA, and MOKSEO algorithms could find optimal solutions that can be viewed as a set of Pareto solutions, which means the used algorithm has the necessary validity. Moreover, the proposed hybrid algorithm can provide Pareto solutions in a shorter time than EPC and higher quality than NSGA-II and MOPSO. Finally, the model's key parameters were the subject of sensitivity analysis; the results showed a linear relationship between the processing time and the first and second objective functions.
In recent decades, the rapid growth of the global population has caused a significant increase in agricultural and food product demands. Thereby, the production of various items in the agricultural food supply chain network has increased to diminish food security concerns. On the other hand, the excessive production of products has led to various issues, such as greenhouse gas emissions and increased water consumption in farmlands, followed by supply chain-related challenges affecting the intermediaries in the next network levels. In this study, an agricultural food supply chain network under marketing practices is firstly probed by developing a stochastic multi-objective programming model to effectively improve three main pillars of sustainability. A convex robust optimization approach addresses the uncertainty of the farm production capacity and the saffron demands in the supply network. The effectiveness of the proposed mathematical model is certified by a case study on saffron business using the LP-metric method. A metaheuristic-oriented methodology comprising a modified Keshtel Algorithm is adapted to deal with the NP-hardness of the problems. The performance of the proposed solution methods is evaluated by two strategies, a statistical comparison and a supportive tool developed based on multi-criteria decision-making (MCDM) methods. The results validate the capability of the applied algorithms to solve the problem in different dimensions. Moreover, the MCDM method approves that MOKASEO outclassed in small, medium, and large-sized problems compared to other algorithms.
Pharmaceutical warehouses are among the centers that play a critical role in the delivery of medicines from the producers to the consumers. Especially with the new drugs and vaccines added during the pandemic period to the supply chain, the importance of the regions they are located in has increased critically. Since the selection of pharmaceutical warehouse location is a strategic decision, it should be handled in detail and a comprehensive analysis should be made for the location selection process. Considering all these, in this study, a real-case application by taking the problem of selecting the best location for a pharmaceutical warehouse is carried out for a city that can be seen as critical in drug distribution in Turkey. For this aim, two effective multi-criteria decision-making (MCDM) methodologies, namely Analytic Hierarchy Process (AHP) and Evaluation based on Distance from Average Solution (EDAS), are integrated under spherical fuzzy environment to reflect fuzziness and indeterminacy better in the decision-making process and the pharmaceutical warehouse location selection problem is discussed by the proposed fuzzy integrated methodology for the first time. Finally, the best region is found for the pharmaceutical warehouse and the results are discussed under the determined criteria. A detailed robustness analysis is also conducted to measure the validity, sensibility and effectiveness of the proposed methodology. With this study, it can be claimed that literature has initiated to be revealed for the pharmaceutical warehouse location problem and a guide has been put forward for those who are willing to study this area.
With the ever-growing data and computing requirements, more and more scientific and business applications represented by workflows have been moved or are in active transition to cloud platforms. Therefore, the cloud workflow scheduling has become a hot topic. As a well-known NP-hard problem, many heuristic or metaheuristic algorithms/methods have been proposed. However, the heuristic method is problem-dependent which fits only a particular of problems, while the metaheuristic method has the problems of incomplete search space or low search efficiency in the complete space. To fill these gaps, a novel adaptive decoding biased random key genetic algorithm for cloud workflow scheduling is proposed. In this algorithm, the improved real number coding based on random key with limited value range is employed, and some novel schemes such as the population initialization based on level and heuristics including dynamic heterogeneous earliest finish time, the dynamic adaptive decoding, the load balance with communication avoidance and iterative forward–backward scheduling are designed for population initialization, chromosome decoding and improvement. To evaluate the performance, extensive experiments have been conducted on various real and random workflow applications, which demonstrates that the proposed algorithm outperforms the conventional approaches.
This paper deals with optimizing the multi-door cross-docking scheduling problem for incoming and outgoing trucks. Contrary to previous studies, it first considers the simultaneous effects of learning and deteriorating on loading and unloading the jobs. A mixed-integer linear programming (MILP) model is developed for this problem, in which the basic truck scheduling problem in a cross-docking system is strongly considered as NP-hardness. Thus, in this paper, meta-heuristic algorithms namely genetic algorithm, imperialist competitive algorithm, and a new hybrid meta-heuristic algorithm resulting from the principal component analysis (PCA) and an imperialist competitive algorithm (ICA) called PCICA are proposed and used. Finally, the numerical results obtained from meta-heuristic algorithms are examined using the relative percentage deviation and time criteria. Results show that the hybrid PCICA algorithm performs better than the other algorithms in terms of solution quality. Computational results indicate when the learning rate increases, its decreasing effect on processing time will growth and the objective function value is improved. Finally, the sensitivity analysis also indicates when the deterioration rate is reduced, its incremental effect is decreased over time.
Undoubtedly, metals are the basis of the sustainable development of all human societies. In the last century, the role of copper, as the third most widely used metal, after steel and aluminum, has been crucial. Copper is a recyclable metal. It has many applications such as industrial electricity, plumping, wiring, electronic equipment, transportation, and infrastructure. Today, with the growth of the industry in societies, the demand for copper has increased. This motivated us to study its supply chain network design firstly. To the best of our knowledge, there is no research reported about copper supply chain network design. This paper aims to maximize the profit of the copper closed-loop supply chain. We formulate this network design problem as a Mixed Integer Programming model. The model is considered as single-objective and multi-product. The exact solution of the model is found by using GAMS software. Sensitivity analysis results provide useful results that managers can use them in decisions. © 2020 Materials and Energy Research Center. All rights reserved.
This paper addresses a flexible flow shop scheduling problem considering limited buffers and step-deteriorating jobs, where there are multiple non-identical parallel machines. A mixed integer programming model is proposed, with the criterion of minimizing the makespan and total tardiness simultaneously. To handle this problem, an effective hybrid meta-heuristic algorithm, named GVNSA, is developed based on genetic algorithm (GA), variable neighborhood search (VNS) and simulated annealing (SA). In the algorithm, with a two-dimensional matrix encoding scheme, the NEH (Nawaz–Enscore–Ham) heuristic and bottleneck elimination method are implemented to determine the initial population. A three-level rolling translation approach is designed for decoding. To balance the exploration and exploitation abilities, three effective steps are executed: 1) partial matching crossover and mutation strategy based on multiple neighborhood search structures are imposed on the GA operators; 2) a VNS with SA is introduced to re-optimize some individuals from GA, where four neighborhood structures are constructed; 3) a modified CDS (Campbell–Dudek–Smith) heuristic is embedded to disturb population in the mid-iteration. Numerical experiments are carried out on test problems with different scales. Computational results demonstrate that the proposed GVNSA can obtain higher quality solutions in comparison with other heuristics and meta-heuristics existing in literature.
In recent years, many industries in developed countries have integrated the important process of reverse logistics into their supply chain for different reasons, including growing environmental concerns. Given fish as perishable food, re-employing unused products and waste in each step of the chain constitute a major concern for the decision-makers. The present study is conducted to maximize responsiveness to customer demand and minimize the cost of the fish closed-loop supply chain (CLSC) by proposing a novel mathematical model. To solve this model, the epsilon-constraint method and Lp-metric were employed. Then, the solution methods were compared with each other based on the performance metrics and a statistical hypothesis. The superior method is ultimately determined using the TOPSIS method. The model application is tested on a case study of the trout CLSC in the north of Iran by performing a sensitivity analysis of demand. This analysis showed the promising results of using the proposed solution method and model. © 2021 Materials and Energy Research Center. All rights reserved.
Recently, the closed-loop supply chain (CLSC) and its application to various fields have been an area of great interest. Despite the importance of CLSC, there remains a paucity of evidence on agriculture in this area. In this work, a CLSC network for the avocado industry is firstly designed by developing a bi-objective model considering the costs of the avocado industry and the social factor of job employment opportunities. The two objectives are the total costs minimization and job employment maximization in various opened locations. To validate the proposed model, a real case study in Puebla, Mexico, is addressed. The GAMS software and its CPLEX solver are utilized to find the best optimum solutions and determine the best locations to open different centers. The applicability of the proposed network is verified by conducting several sensitivity analyses on the important parameters of the problem. According to the obtained results, demand has the most effect on this network in which that a 25 percent decrease in demand can increase the total cost (the first objective) up to 40 percent and improve employment efficiency (the second objective) up to around 30 percent, simultaneously.
The global epidemic caused by novel coronavirus continues to be a crisis in the world and a matter of concern. The way the epidemic has wreaked havoc on the international level has become difficult for the healthcare systems to supply adequately personal protection equipment for medical personnel all over the globe. In this paper, considering the COVID-19 outbreak, a multi-objective, multi-product, and multi-period model for the personal protection equipment demands satisfaction aiming to optimize total cost and shortage, simultaneously, is developed. The model is embedded with instances and validated by both modern and classic multi-objective metaheuristic algorithms. Moreover, the Taguchi method is exploited to set the metaheuristic into their best performances by finding their parameters’ optimum level. Furthermore, fifteen test examples are designed to prove the established PPE supply chain model and tuned algorithms’ applicability. Among the test examples, one is related to a real case study in Iran. Finally, metaheuristics are evaluated by a series of related metrics through different statistical analyses. It can be concluded from the obtained results that solution methods are practical and valuable to achieve the efficient shortage level and cost.
Obviously, the Covid-19 pandemic has huge impact on most businesses and has caused serious and countless problems for them. Therefore, providing solutions for affected businesses to recover and improve their activities during pandemic times is inevitable. In this regard, ecotourism centers are one of the businesses that went through this problem and have faced significant dilemmas in their activities. Also, reportedly, there is no related research focusing on the recovery approaches to address these obstacles relating to these kinds of businesses during the pandemic. Therefore, all of these exhorted us to do the current research. In this paper, some practical and useful action plans for ecotourism centers are firstly developed to help these businesses. To obtain the action plans, some brainstorming sessions were held consisting of tourism experts, university professors, managers, owners, and some personnel of eco-tourism centers. In order to prioritize the defined action plans, four criteria are considered. Firstly, we compute the weights of the considered criteria by the Fuzzy DEMATEL and then they are prioritized using the Fuzzy VIKOR. The findings of the current study divulge that the AP2 “Standardization of the centers” and AP3 “Estimating demand number and increasing the capacity” and AP7 “Identifying other natural tourist attractions of the region” have the highest and lowest priority to be implemented.
Recently, supply chain network design has been a demanding question and attracted great interest in a wide range of fields, including the medical industry. The heart of the entire discipline of the medical industry is the concept of blood management which nowadays brought striking attention among managers and decision-makers. Thus, a bi-objective and sustainable blood supply chain network is introduced in this study by considering both social and environmental factors of blood decomposition. In addition, some aspects of uncertainty are firstly considered to the problem in terms of both the amount of gathered blood from blood transfusion centers and the decomposition ratio in the blood decomposition center. Considering bi-objective programming in the developed network, the problem simultaneously considers optimizing the CO2 emission, balancing the flow of all utilized vehicles, penalty coefficient of non-visited centers, total costs of using all vehicles, and more importantly the social and environmental factors of blood decomposition. Therefore, the total cost is minimized by the first objective function along with the environmental factor of greenhouse gas emission while maximizing the social factor of blood decomposition is done by the second objective function which gathers and produces the maximum amount of blood sub-products for patients using stochastic programming. Several sensitivity analyses are conducted for a better implication of the problem in various conditions. The findings of the study suggest that expecting more social factors convey more costs in most cases. Therefore, blood supply chain managers must adjust their aims and scope to achieve the best-desired results for society.
Flexible job shop scheduling problems have been extensively investigated in the past decade; however, transportation, sequence-dependent setup times (SDST) and energy efficiency are seldom incorporated together in flexible job shop. In this paper, energy-efficient flexible job shop scheduling problem (EFJSP) with transportation and SDST is considered and an imperialist competitive algorithm with feedback (FICA) is developed to minimize makespan, total tardiness and total energy consumption simultaneously. Assimilation and adaptive revolution are newly implemented by feedback and a new imperialist competition is presented by solution transferring among empires and the reinforced search. Extensive experiments are conducted and the computational results demonstrate that FICA provides promising results for EFJSP with transportation and SDST.
The COVID-19 pandemic is viewed as the most basic worldwide disaster that humankind has observed since the second World War. There is no report of any clinically endorsed antiviral medications or antibodies that are successful against COVID-19. It has quickly spread everywhere, presenting tremendous well-being, financial, ecological, and social difficulties to the whole human populace. The COVID flare-up is seriously disturbing the worldwide economy. Practically all the countries are battling to hinder the transmission of the malady by testing and treating patients, isolating speculated people through contact following, confining huge social affairs, keeping up total or incomplete lockdown, etc. Proper scheduling of nursing workers and optimal designation of nurses may significantly affect the quality of clinical facilities. It is delivered by eliminating unbalanced workloads or undue stress, which could lead to decreased nurse performance and potential human errors., Nurses are frequently asked to leave while caring for all sick patients. However, regular scheduling formulas are not thought to consider this possibility because they are out of scheduling control in typical scenarios. In this paper, a novel model of the Hybrid Salp Swarm Algorithm and Genetic Algorithm (HSSAGA) is proposed to solve nurses’ scheduling and designation. The findings of the suggested test function algorithm demonstrate that this algorithm has outperformed state-of-the-art approaches.
The current universally challenging SARS-COV-2 pandemic has transcended all the social, logical, economic, and mortal boundaries regarding global operations. Although myriad global societies tried to address this issue, most of the employed efforts seem superficial and failed to deal with the problem, especially in the healthcare sector. On the other hand, the Internet of Things (IoT) has enabled healthcare system for both better understanding of the patient’s condition and appropriate monitoring in a remote fashion. However, there has always been a gap for utilizing this approach on the healthcare system especially in agitated condition of the pandemics. Therefore, in this study, we develop two innovative approaches to design a relief supply chain network is by using IoT to address multiple suspected cases during a pandemic like the SARS-COV-2 outbreak. The first approach (prioritizing approach) minimizes the maximum ambulances response time, while the second approach (allocating approach) minimizes the total critical response time. Each approach is validated and investigated utilizing several test problems and a real case in Iran as well. A set of efficient meta-heuristics and hybrid ones is developed to optimize the proposed models. The proposed approaches have shown their versatility in various harsh SARS-COV-2 pandemic situations being dealt with by managers. Finally, we compare the two proposed approaches in terms of response time and route optimization using a real case study in Iran. Implementing the proposed IoT-based methodology in three consecutive weeks, the results showed 35.54% decrease in the number of confirmed cases.
In the history of sustainable development, logistics has been thought of as one of the key factors in the supply chain network. In the meantime, the issue of Closed-loop Supply Chain (CLSC) has received considerable attention as it ensures many diverse industries toward sustainability. But many industries including agricultural section often fail to address and fulfill these requirements. In this study, a new CLSC network is designed for the walnut industry as a part of agricultural crop by conducting a complete review of the past studies. Therefore, a new Mixed Integer Linear Programming (MILP) for the proposed network is developed minimizing the overall costs of the walnut industry. The designed network considers both forward and reverse flow not only to meet the demands of various markets, but also to prepare the returned products for the second use. In order to solve the proposed model, a set of exact, metaheuristics, and hybrid metaheuristics are employed. Finally, the best solutions are obtained by assessing the finest initial answers using Taguchi method. The results notoriously illustrated the excellent consistency between the proposed network and the employed algorithms along with its applicability and efficiency.
Nowadays, in the pharmaceutical industry, a growing concern with sustainability has become a strict consideration during the COVID-19 pandemic. There is a lack of good mathematical models in the field. In this research, a production-distribution-inventory-allocation-location problem in the sustainable medical supply chain network is designed to fill this gap. Also, the distribution of medicines related to COVID-19 patients and the periods of production and delivery of medicine according to the perishability of some medicines are considered. In the model, a multi-objective, multi-level, multi-product, and multi-period problem for a sustainable medical supply chain network is designed. Three hybrid meta-heuristic algorithms, namely, ant colony optimization, fish swarm algorithm, and firefly algorithm are suggested, hybridized with variable neighborhood search to solve the sustainable medical supply chain network model. Response surface method is used to tune the parameters since meta-heuristic algorithms are sensitive to input parameters. Six assessment metrics were used to assess the quality of the obtained Pareto frontier by the meta-heuristic algorithms on the considered problems. A real case study is used and empirical results indicate the superiority of the hybrid fish swarm algorithm with variable neighborhood search.
As a valuable nut which is rich in minerals and vitamins, the pistachio is now popular with millions of people throughout the world. Since its major producer-exporter countries are limited, paying attention to its supply chain seems quite necessary. The pistachio SC management can play an important role in its more economic development because it involves all the activities related to the product management from supply to the customer delivery. Also, due to the recently changing environmental regulations affecting manufacturing operations, increasing attention is given to developing environmental commitments for the supply chain. The present study has designed a supply chain for the pistachio nut, its by-products, and carbon production. The proposed mathematical model considering a robust possibilistic programming approach to face the uncertainties inherent in this supply chain as regards the supply, products’ price, and demand of the type multi-period MILP. The results of data realizations show that the proposed model is less sensitive to the changes in the uncertain parameters than the exact model. The proposed model’s performance has been verified with a real case study in Iran through which a managerial and practical insight is achieved. The research finally compares the proposed RPP with an exact programming model and shows its advantages; in the standard deviation, its performance has been better by about 36.67%.
In this paper, a bi-objective mixed-integer linear optimization model for Closed-loop Supply Chain Network Design Problem (CLSCND) is developed. The proposed model includes both the forward and reverse directions and includes different types of facilities, namely, manufacturing/remanufacturing centers, warehouses, and disassembly centers. The first objective function tried to minimize the total cost of the supply chain, while the second one was aimed at maximizing the responsiveness of the network in both forward and reverse directions, simultaneously. To solve the proposed bi-objective model, an augmented ε-constraint method was implemented by which a set of Pareto-optimal solutions for the problem were generated. An illustrative numerical example is given in the study to show the applicability and efficiency of the presented optimization model.
Recent developments in business network have heightened the need for the issue of cost reduction while increasing the productivity. Supply chain network is a fundamental property of industrial practitioners and researchers. Achieving a practical objective, this research study has an attempt to enhance the efficiency of a supply chain by considering simultaneous pick up and split delivery. This attempt would result in total costs minimization and customer service maximization in the form of multi-products and multi-period. In addition, environmental aspect of this system is considered as well as green vehicle routing problem (GVRP). We propose a mixed-integer linear programming model by a ε-constraint approach. This study not only utilizes exact method from GAMS software, but also employs four hybrid and meta-heuristics including GAKA, GAPSO, SA and RDA to develop and evaluate the solutions. In addition, the validation of model is presented for a real-life data. Besides, a case study has been done in order to accumulate the parameters data. This case study is utilized in a food industry located in the north of Iran.
Generally, in Home Health Care (HHC) logistics, caregivers which are started from a pharmacy are scheduled and routed to do different care services at patients' home. At the end, they go to their laboratory to update the patients' health records. In addition to scheduling and routing of the caregivers, there are some other optimization decisions which can increase the competitive advantages of HHC organizations as a supply chain network. The location decisions of the pharmacies and laboratories and the assignment of patients to the nearest pharmacies are two of the several important logistics factors for an HHC organization. The literature shows that the green emissions and sustainability achievements for HHC logistics are still scarce. To cover more logistics and sustainability factors and make the HHC more practical, this study contributes a Green Home Health Care Supply Chain (GHHCSC) for the first time by a bi-objective location-allocation-routing model. Already applied successfully to this research area, the Simulated Annealing (SA) is also employed in this study. Another main innovation of this paper is to propose a set of new modified SA algorithms to better solve the proposed NP-hard problem. As a bi-objective optimization model, the epsilon constraint method is also utilized to check the algorithms' results in small sizes. By using some multi-objective assessment metrics, the algorithms are compared with each other and their performance is evaluated. As such, some sensitivity analyses are performed to reveal the efficiency of the developed model. Finally, some managerial insights are deployed to achieve the sustainability for the HHC organizations.
The concern about environmental and social impacts of business activities has led to introducing a new paradigm called, sustainable development. It can help to build a low-carbon high-growth global economy and guarantee the global well-being of people. In this paper, three pillars of sustainable development, i.e., economic, environmental, and social, are considered and discussed to design a supply chain network. The proposed model tries to maximize profit primarily while capturing societal community development by prioritizing the less developed regions. Moreover, the model ensures that the environmentally friendly facilities can operate in the supply chain network while others have to be repaired. Furthermore, quantifying the benefits of transportation decisions in terms of both cost and environmental impact savings to improve the sustainability of logistics systems is considered. In addition, the model is regarded as robust programming for the problem to approximate real situations. The proposed model is implemented in some numerical examples and in a real case study. Numerical results and computational analysis are indicative of the significance of the model and through conducting the case study, it is demonstrated that the proposed model can be implemented successfully in practice and it would be beneficial to all the three pillars of sustainable development. Moreover, the managerial insights for the managers of the supply chain networks are provided to make the most appropriate decisions.
Recently, social awareness, governmental legislations and competitive business environment have spurred researchers to pay much attention to closed-loop supply chain network design. In order to support the arising trend, this paper presents a comprehensive mathematical model for a multi-period, multi-product, multi-modal and bi-objective green closed-loop supply chain. The objective of the model is to minimize the total cost and environmental emissions through making the best decisions on facility location, transportation amounts and inventory balances. According to the inherent complexity of the problem and considering multi-product, multi-period and multi- modality assumptions makes it hard to handle, and as for the solution approach, an effective accelerated benders decomposition algorithm is implemented. Then, computational results for a set of numerical example are discussed. Besides, the model and solution approach are applied on a wire-and-cable industry. Then, a sensitivity analysis is implemented in an effort to validate the model. Results reveal applicability of the proposed mathematical model and presented solution approach. Following the obtained results, it can be validly concluded that the suggested solution approach leads to more than 13 percent reduction in total cost for the studied case, and can be even employed for larger and more complex real-world industrial applications.
The lack of industrialization, inadequacy of the management, information inaccuracy, and inefficient supply chains are the significant issues in an agri-food supply chain. The proposed solutions to overcome these challenges should not only consider the way the food is produced but also take care of societal, environmental and economic concerns. There has been increasing use of emerging technologies in the agriculture supply chains. The internet of things, the blockchain, and big data technologies are potential enablers of sustainable agriculture supply chains. These technologies are driving the agricultural supply chain towards a digital supply chain environment that is data-driven. Realizing the significance of a data-driven sustainable agriculture supply chain we extracted and reviewed 84 academic journals from 2000 to 2017. The primary purpose of the review was to understand the level of analytics used (descriptive, predictive and prescriptive), sustainable agriculture supply chain objectives attained (social, environmental and economic), the supply chain processes from where the data is collected, and the supply chain resources deployed for the same. Based on the results of the review, we propose an application framework for the practitioners involved in the agri-food supply chain that identifies the supply chain visibility and supply chain resources as the main driving force for developing data analytics capability and achieving the sustainable performance. The framework will guide the practitioners to plan their investments to build a robust data-driven agri-food supply chain. Finally, we outline the future research directions and limitations of our study.
According to the recent gigantic development in the agricultural section, Agricultural Supply Chain (ASC) management has attracted both researchers and agronomic practitioners. In this regard, rice as one of the important agricultural products is generally cultivated by rural farmers in small farmlands. Due to the high demand, high price, type of products, and also wide geographic range of production and consumption, the rice supply chain has special characteristics in ASC. In this regard, this paper not only firstly considers rice supply chain and but also proposes a bi-level optimization model for rice supply chain. The proposed model aims to minimize total cost with respect to the two decision makers' opinions. Since, the bi-level programming is NP-hard, to solve the proposed model, two well-known meta-heuristic algorithms including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) along with two hybrid algorithms (PSO-GA and GA-PSO) and a modified algorithm (GPA) are utilized. In order to fill the literature gaps and to get closer to real-world applications, an applicable example in Iran is studied. Based on the results and managerial insights, the GPA is chosen as the best method and its allocated value of the variable are reported. The results show that the proposed model and solution methods are valid, practical, and effective. Also, in order to provide an insight to the functionality of the model and the results of the case, some sensitivity analyses on the major model parameters such as the demand are provided.
Disruption in the electric power system network can have dramatic effects on supply chain performance in terms of economic and environmental objectives, especially for perishable products. This paper addresses the design of a resilient green-closed loop supply chain network for perishable products under the risk of electric power network disruption. The research has presented an integrated model for the interdependent two-layer network structure as a strategy to mitigate the power disruption risk in order to minimize the expected total network cost and the expected total amount of carbon emissions. Integration of two networks on both objectives along with the effect of products’ lifetime has been analyzed in a real case study of the dairy industry. The results designate that the integration of the two networks leads to improvements in both objective functions. The average total network cost and total carbon emissions have been reduced by 21% and 25% compared to the non-integrated layers, respectively. Furthermore, by increasing the products lifetime, the projected model enjoys higher performance being compared with the non-integrated layers model. This paper concludes that the integrated decision making about the infrastructure networks e.g. power network with the supply chain enjoys economic and environmental benefits for both networks layers under power network disruptions.
One of the important concerns in the world is electronic waste (e-waste). Ending up e-waste in the landfill and inappropriate disposing of it are hazardous to the environment. The goal of this research is to design and optimize a multi-period, multi-product, multi-echelon, and multi-customer Closed-Loop Supply Chain (CLSC)network for a mobile phone network considering different types of product returns. Commercial, end of life, and end-of-use returns are well-known in practice. In this research, a multi-objective mixed-integer linear programming formulation with stochastic demand and return is proposed to maximize the total profit in the mobile phone CLSC network, alongside maximizing the weights of eligible suppliers which are estimated based on a fuzzy method for efficient supplier selection and order allocation. The goal is to determine the appropriate location and number of different facilities including suppliers, manufacturers, retailers, drop-off centres, and consolidation centres as well as the amount of required materials to produce a mobile phone, and the number of products that should be transported between various facilities. The application of the proposed mathematical model is illustrated in Toronto, Canada using real maps.
Lack of homogeneity in the product (LHP) appears in some production processes that confer heterogeneity in the characteristics of the products obtained. Supply chains with this issue have to classify the product in different homogeneous subsets, whose quantity is uncertain during the production planning process. This paper proposes a generic framework for reviewing in a unified way the literature about production planning models dealing with LHP uncertainty. This analysis allows the identification of similarities among sectors to transfer solutions between them and gaps existing in the literature for further research. The results of the review show: (1) sectors affected by LHP inherent uncertainty, (2) the inherent LHP uncertainty types modelled, and (3) the approaches for modelling LHP uncertainty most widely employed. Finally, we suggest a conceptual model reflecting the aspects to be considered when modelling the production planning in sectors with LHP in an uncertain environment.
Due to the increasing awareness of climate change, depletion of natural resources, and increasing world population, companies in the agri-food sector need to redesign their existing supply chains and take into account both the economic and environmental impact of their operations. In practice not all the required information is available in advance due to various sources of uncertainty in agri-food supply chains. In this research a multi-objective two-stage stochastic programming model is proposed to analyse and evaluate the economic and environmental impacts to account for uncertainty in agri-food supply chains. A mushroom supply chain in the Netherlands is presented as an illustrative case study. Optimal production planning decisions calculated with a two-stage stochastic programming model are compared with the results of an equivalent deterministic model. The results of the optimizations show that accounting for stochasticity in important model parameters can reduce the difference between expected and realized economic performance by approximately 4% on average. Moreover, this paper demonstrates that including stochastic model parameters can reduce the environmental impact without compromising the current economic performance. Given the assumptions in the setup of the case study and the available information, it is concluded that applying a 2-stage stochastic programming approach for production planning decisions can lead to improved economic and environmental performance in an agri-food supply chain. New findings in real-life case studies are needed to get profound insights and understanding on the impact of uncertainty on production planning decisions in sustainable agri-food supply chains.
The aim in new agricultural investment projects is to achieve a proper balance between customer value creation and investor economic benefits. Several authors have highlighted the importance of integrating agroindustrial supply chain operations, as a way to improve competitiveness. In the specific case of sugarcane, most research only integrates harvest and transport operations, lacking simultaneous analysis of sowing, growing and harvesting. Also, from the perspective of sugarcane supply chain planning, few contributions undertake tactical and strategic decisions. This paper proposes an optimization model for sugarcane supply chain planning, integrating several agricultural decisions from a strategic-tactical planning perspective. The uncertainty effects generated by weather conditions were also considered. As a main contribution, the model establishes a set of agricultural decisions that maximize cane yield, in order to optimize the Net Present Value (NPV) of expected profits. The proposed model is utilized to evaluate the feasibility of a new biofuel production plant in Colombia. Results allowed for the identification of critical variables to control, in order to reduce investment risk.
Closed-Loop Supply Chain (CLSC) network design plays a significant role in supply chain performance. The CLSC network design is recognized as a strategic problem which ensures a useful and efficient supply chain management providing an optimal platform. The CLSC network design problem includes two types of decisions, strategic and tactical. This paper aims to determine the location of facilities which is recognized as a strategic decision. In addition, tactical decisions such as the amount of supplied raw material, the level of production, and shipments among the network entities are made through the proposed model. This paper is distinctive by introducing a Mixed Integer Linear Programming (MILP)-based model which simultaneously optimizes the both forward and reverse chains. The model is implemented on a glass manufacturing industry to highlight the importance and applicability of the framework. Moreover, the study provides a comprehensive sensitivity analysis to investigate the effect of parameters such as demand and return rates on strategic and tactical decisions in supply chain network.
Optimal energy management of microgrids with different objectives such as operation cost, air pollution and wider use of renewable energy sources is investigated in many research works. In this paper, the effect of different operation modes of microgrids to solve environmental/economic dispatch problem is evaluated. Different modes of operation of microgrid include islanding operation, grid-connected and unilateral or bilateral exchange of power with main grid. The imperialist competitive algorithm (ICA) is one of the evolutionary algorithms that can model the competing imperialists to absorb colonies and uses it to find optimum solution. In this paper, a new method to improve the ICA algorithm is proposed in which the movement radius of the colonies toward the imperialist is adaptively adjusted in accordance with their position by means of adding direction and velocity parameters to the colonies. Thus, the colonies are attracted and repelled more intelligently by imperialists. The proposed method can create a trade-off between exploitation (i.e. local search) and exploration (i.e. global search) and prevents premature convergence while minimizing trapping in the local minima. Simulation results and comparison with other algorithms confirm high performance of the proposed algorithm.