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The reverse logistics network (RLN) design for sustainable supply chain management is a strategic decision in network configuration, and is higher influenced by uncertainty. This paper applies a bi-level stochastic multi-objective model to design an RLN for a disposable product recycling management system. The goal is to balance the overall network...
Citations
... Sadeghi Ahangar et al. (2021) put forward a sustainable CLSCN for municipal solid waste management using fuzzy MIP technique for minimizing total costs and emissions. Khoei et al. (2022) introduced a multi-objective bi-level stochastic framework for designing the recycling network of disposable products. ...
Purpose- The purpose of this study was to address waste management in the food supply chain (FSC) through the integration of inspection processes in production and distribution centers under uncertain conditions, aiming to enhance sustainability across environmental, economic, and social dimensions. The study introduces a sustainable forward and reverse FSC network using a closed-loop supply chain network approach to prevent the transfer of spoiled products, ultimately providing competitive advantages to stakeholders.
Design/methodology/approach- A robust multi-objective mathematical programming model is proposed, incorporating inspection processes to manage perishable products effectively. The model is solved using the Augmented Epsilon Constraint technique implemented in GAMS software, providing Pareto-optimal solutions tailored to decision-makers' preferences. Furthermore, the methodology is applied in a real-world case study and solved with the Benders Decomposition algorithm to validate its practicality and effectiveness.
Findings- The proposed methodology effectively minimizes waste and enhances sustainability in the FSC by optimizing decision-making processes under uncertainty. The illustrative examples and real case study demonstrate the efficiency of the model and solution approach, highlighting the significant role of inspection in improving all three dimensions of sustainability.
Practical implications- The study offers valuable insights and tools for food industry managers to make informed strategic and tactical decisions. By addressing waste management through advanced supply chain modeling, the research helps organizations reduce costs, improve sustainability, and gain a competitive edge in the market.
Originality/value-This research is novel in its focus on integrating inspection into the FSC network and addressing uncertainty through robust mathematical modeling. It contributes to the existing literature by demonstrating the impact of inspection on sustainability in FSCs and providing practical solutions for real-world implementation.
... In addition to increasing the storage rate of warehouses, three-dimensional warehouses can reduce the footprint and provide a favorable environment for waste disposal [79]. Due to the complex composition of CDW, the sorting accuracy should be high [81]. The latest research indicates that sorting clutter is feasible, and detailed research in this direction can enhance the possibility of applying AS/RS stereoscopic warehouse technologies in RL for the construction industry. ...
... Big data processing technology enables the rapid acquisition of valuable information on different types of data involved in RL, including the collection, preprocessing, storage and management, analysis, and mining, thereby providing data processing technology support for RL management decision-making, logistics network design, and RL inventory management [81,98,99]. ...
Implementing reverse logistics in the construction industry is considered a crucial method to achieve a circular economy. Despite a wealth of research focusing on improving reverse logistics systems, businesses still encounter challenges during the implementation process. Therefore, this study conducted a systematic literature review utilizing bibliometric methods to analyze 623 articles on reverse logistics in the construction industry published on Web of Science from 1995 to 2023. Additionally, a comprehensive review of 56 high-quality literature on obstacles to implementing reverse logistics in the construction industry and optimizing reverse supply chains was conducted. This review uncovered the current status and challenges of implementing reverse logistics in the construction industry and proposed potential solutions to address these issues. The main findings of this study include: (1) increasing academic interest in construction waste reverse logistics, with Chinese scholars leading the way and publications predominantly in environmental and construction journals, with limited coverage in logistics journals; (2) the primary obstacles to implementing reverse logistics in the construction industry lie in supply chain management, such as lacking deconstruction designs, incomplete recycling markets, difficulties in evaluating the quality of secondary materials, and insufficient supply chain integration; (3) proposing a framework for a construction industry reverse logistics supply chain ecosystem, aiming to establish a platform to facilitate online collection of construction waste, online transactions of secondary materials, end-to-end monitoring, and data analytics for consultation.
... Recently, network flexibility has been increasingly investigated (Shukla et al., 2022;Yu and Solvang, 2018). Incorporating new technologies, e.g., big data, has become another research spotlight in this field (Khoei et al., 2023;Mishra and Singh, 2020). ...
Remanufacturing, a crucial step of reverse logistics, focuses on restoring or enhancing the functionality of waste products. The challenge in planning an effective remanufacturing reverse logistics system lies in the uncertainties from various sources. In addition, the evolving industrial landscape in Industry 5.0 necessitates adaptability to technological advancements. This paper proposes an integrated and digitalized architecture for uncertain reverse logistics network design. A fuzzy optimization model is first formulated to identify potential network configurations under varying demand-satisfying and capacity constraints. These solutions are automatically converted and assessed in a dynamic simulation environment with practical operational logic under a set of real-world scenarios. Numerical experiments are performed to validate the method and show the advantages of integrating optimization with dynamic simulation on a digital platform for strategic network planning. The results, built upon previous research, indicate that while initial investments in technology might be substantial, they may lead to long-term reductions in both costs and emissions. Moreover, collaborative decision-making is essential to mitigate potential disruptions and cascading effects. Our research contributes to the development of a novel integrated decision-support architecture and underscores the role of digitalization and Industry 5.0 in future smart and sustainable reverse logistics planning.
... They developed a two-level nonlinear model with uncertain parameters and devised a heuristic polynomial GA to solve the proposed model, contributing to advancing supply chain optimization techniques. Khoei et al. (2022) utilized a bi-level stochastic multi-objective model to design a recycling logistics network (RLN) for managing disposable product recycling, employing an LP-metric-based sample average approximation for optimization. In the research by Sadeghi et al. (2023), the prioritization of requirements for implementing blockchain technology in the CSC was conducted, focusing on circular economy attributes. ...
Recently, the benefits of employing philosophical principles to optimize supply chains in the construction sector have been delved into by many researchers. This comprises three key segments: customers or employers, design, and construction. To address challenges and enhance the viability and competitiveness of the construction sector, the adoption of supply chain management has been proposed. This study develops a multi-objective mathematical model to design a construction supply chain, which includes a primary supplier and multiple customer projects. The primary goal is to ensure that the necessary materials are delivered at different intervals, considering each product's specific technical requirements and life cycles. It is crucial to prevent late deliveries, as they can lead to product wastage and substantially impact management decisions. Additionally, it is important to highlight that this study addresses the uncertainty in the supply chain, which further complicates the planning and decision-making processes. Accordingly, some related sensitivity analyses were done to reveal the profound influence of uncertainty on the proposed model, and its important uncertain parameters were specified. Next, the study employs the robust programming approach introduced by Bertsimas and Sim to tackle the uncertainty. To validate the proposed approach, it is implemented in a real-world case study. For small-size problems, the modified weighted Chebyshev method is used for solving the model. Then, the best–worst multi-criteria decision-making technique assesses the suggested model's ability to solve various problems in larger sizes. Subsequently, the study applies meta-heuristic algorithms like MOGA-II, MORDA, and MOSA to solve large-size problems. According to the outputs analysis, the MORDA algorithm executed better than the others. Ultimately, the study derives managerial insights based on the findings from the sensitivity analysis.
... It is estimated that global retail e-commerce sales reached $5.7 billion in 2020, comprising 19.7% of total retail and showing a 9.7% increase over 2021 (eMarketer reports, 2020). Manufacturers have access to a tremendous amount of data on individual customers for identifying the unique requirements of consumers (Feng and Shanthikumar, 2018;Ban and Rudin, 2019;Khoei et al., 2023). Furthermore, advanced technologies, e.g. ...
Purpose
The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.
Design/methodology/approach
In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.
Findings
Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.
Originality/value
Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.
... Three echelons supply chain may consist of manufacturer-warehouse-retailer [6,14] or supplier-manufacturer-retailer [15][16][17][18]. Various objective functions are proposed, such as maximizing profit [16], minimizing total cost [4,6,14,17,[20][21][22], and minimizing environmental impacts [18,19]. The decision variable mainly covers material flow between echelons and facilities selection. ...
... Current research trends on SCND consider environmental impacts in optimizing the network. The network analyzed can be a forward supply chain [4,6,16], backward (reverse) supply chain [20,22,23], or closed-loop supply chain [24][25][26][27]. Most research considers the environmental impact as the emission from supply chain activities. ...
... Most research considers the environmental impact as the emission from supply chain activities. These activities include transportation operations [20,22,24,25,28], production activities [20,24,25], inventory holding and handlings [16,29], or opening and operating facilities [24]. ...
This research answers three current supply chain issues: demand variability, disruptions, and sustainability. We develop a multi-objective supply chain network model integrating inventory decisions for each echelon under demand uncertainty and limited production capacity. The model describes the optimal productions and allocations in normal and disrupted conditions. The objective function is to minimize the total supply chain cost and emissions. To handle the complexity, we proposed a priority-based Non-dominating Sorting Genetic Algorithm II (pb-NSGA-II) and priority-based Multi-Objective Particle Swarm Optimization (pb-MOPSO) with four novel decoding procedures to accommodate the priority: ordering cost, carbon emission, backtrack priority-based decoding, and adaptive decoding. The experiments indicate that at low disruption duration, the supply chain network design (SCND) is not affected by the disruption due to the existence of safety stock. However, the SCND starts rescheduling and reallocating its demands at medium and high disruption durations.
... Effective management of manufacturing supply chains requires attention to system and parts reliability to ensure optimal efficiency [9,39]. Achieving this reliability entails optimizing system and parts reliability based on operational objectives such as cost, availability, and serviceability [35]. ...
This paper presents a framework for designing a closed-loop green supply chain network (CLGSCN) that incorporates a redundancy strategy for maximum reliability and eco-friendliness. The network consists of production centers, repairs, and spare parts, with maintenance outsourced to ensure that spare parts circulate within the network for as long as possible. The proposed multi-objective mixed-integer program considers environmental considerations, service costs, routing decisions, cycle times, and assignments, with active and cold standby strategies for maximum reliability. A hybrid heuristics algorithm and multi-choice meta-goal programming with utility function are applied to solve the multi-objective model. The case study demonstrates the applicability of the model in real-world scenarios, offering valuable insights for optimized spare-part supply for maintenance and delivery. Sensitivity analyses show that the objectives are highly sensitive to the parameters, including the failure rate, demand, and reliability of the components, and results show an approximate decrease of 15.3% in the total cost and an increase of 2.83% in eco-friendly parts and finally increase of 11.25% in reliability with active standby strategy. Overall, this paper contributes to the field of supply chain management for advanced manufacturing systems both theoretically and practically, with potential benefits for businesses and society.
... Since the nonlinear mathematical models need high computational efforts to solve the problem, linearization of these nonlinear constraints and objective enhances the efficiency of our proposed model to find the optimum solutions. Using the following method, these nonlinear constraints can be simplified to the linearized forms [72][73][74]. Property 1 Let Z1 ¼ X 1 :X 2 is a nonlinear equation where X 1 denotes a binary variable and X 2 shows a binary variable. Glover proposed the following method to linearize this equation: ...
In project portfolio selection (PPS) management, one of the main goals is the optimal management of projects with the least risk and the highest commercial value under risk considerations. Hence, this study considers the weight of each decision criterion, their impacts, and also the uncertainty in decision making. By taking into account all those assumptions, this paper seeks to conduct a PPS with aiming of maximizing the average value as the performance of each project, the rate of development of each project and minimizing the risk of interruption in the implementation of selected projects. The strategic goal of this study is to select robust project portfolios in the long run for less replacement. Accordingly, for attaining all goals, a combined method developed in three stages of PPS; first the weight of criteria from the F-AHP method is determined, next the F-TOPSIS method is used to calculate the relative scores for the projects, and finally a scenario-based robust multi-objective mathematical programming model is considered. This paper has been encountered with two challenges and complexity which is solved by the hybrid method based on the Multi-Choice Goal Programming with Utility Function (MCGP-UF) and the particle swarm optimization (PSO) algorithm (hybrid PSO-MCGP-UF). The results show an improvement in the solution time and the quality of the responses of the proposed method, which helps decision-makers at all stages of the PPS to achieve robustness portfolios in less time.
This study investigates the sustainability and supply chain challenges in the African renewable energy sector, offering solutions rooted in green supply chain management principles and Industry 4.0 technologies. Employing an interpretive research strategy, the study gathers insights from mini-grid companies, regulatory authorities, and a global financial institution that fund renewable energy projects across Africa. The findings reveal various SC issues related to importation, economic policies, regulations, logistics, skill gaps, and corruption. Sustainability related challenges such as lack of environmental awareness and poor end-of-life management practices were also obtained. Proposed solutions include GSCM practices like recycling, responsible sourcing, and carbon footprint assessment, and leveraging Industry 4.0 technologies such as Internet of things, blockchain, and big data analytics for smart metering and energy management. The study highlights practical implications, advocating for robust approaches to resolving supply chain and sustainability issues, policy enactment favourable to the renewable energy sector, and synergy among government departments and law enforcement agencies.
Steel plays a pivotal role in today’s world and serves as the backbone of modern societies. Its versatility, strength, and durability make it a widely used material in various industries. Steel contributes to economic development, innovation, and progress worldwide. In this thesis, a comprehensive bi-objective data-driven robust optimization model is introduced, aimed at crafting a sustainable forward-reverse steel supply chain network. Sustainable development goals are studied by examining total costs, occupational safety measures, and environmental consequences of water and energy resources. Also, based on to the circular economy principles, the collection of steel scrap facilitated by a take-back regulation, is investigated. Moreover, steel slag, a by-product of steel production whose benefits are often overlooked, is included. To tackle uncertainties effectively, we employ a data-driven optimization method, which leverages historical data to construct an uncertainty set through support vector clustering. This approach enables us to account for various potential scenarios and develop robust solutions that can withstand fluctuations and unforeseen challenges. Furthermore, by integrating state-of-the-art technologies within Fourth Industrial Revolution (Industry 4.0) frameworks, we not only optimize decision-making processes but also bolster adaptability and resilience in manufacturing operations, paving the way for enhanced efficiency and competitiveness in today's dynamic industrial landscape. The proposed model undergoes practical implementation through a real-world case study within Iran's steel sector, offering tangible insights into its functionality and applicability. The results underscore the efficacy of the proposed method, shedding light on its capacity to address complex challenges within the steel industry, and the findings highlight the significance of integrating environmental measures and promoting the sustainable utilization of energy and water resources. Moreover, on average 5% more investment can reduce environmental impacts by 7%.
