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Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models
Abstract
Agri-food sector performance strongly impacts global economy, which means that developing optimisation models to support the decision-making process in agri-food supply chains (AFSC) is necessary. These models should contemplate AFSC’s inherent characteristics and sources of uncertainty to provide applicable and accurate solutions. To the best of our knowledge, there are no conceptual frameworks available to design AFSC through mathematical programming modelling while considering their inherent characteristics and sources of uncertainty, nor any there literature reviews that address such characteristics and uncertainty sources in existing AFSC design models. This paper aims to fill these gaps in the literature by proposing such a conceptual framework and state of the art. The framework can be used as a guide tool for both developing and analysing models based on mathematical programming to design AFSC. The implementation of the framework into the state of the art validates its. Finally, some literature gaps and future research lines were identified.
... As opposed to other industries, the agri-food sector has diverse characteristics that determine innovation activities . Studies (Esteso et al., 2017;Esteso et al., 2018) have identified different types of crop-based uncertainty facing these businesses, such as shelf-life, deterioration rate, harvesting yield, supply lead time, market prices, weather, pests and diseases, regulations, etc. (Esteso et al., 2018). Therefore, the agri-food supply chains have been strongly urged to manage these sources of uncertainty and risk, whose precise evolution is unpredictable and can compromise the future sustainability of this kind of supply chain FAO, 2021). ...
... As opposed to other industries, the agri-food sector has diverse characteristics that determine innovation activities . Studies (Esteso et al., 2017;Esteso et al., 2018) have identified different types of crop-based uncertainty facing these businesses, such as shelf-life, deterioration rate, harvesting yield, supply lead time, market prices, weather, pests and diseases, regulations, etc. (Esteso et al., 2018). Therefore, the agri-food supply chains have been strongly urged to manage these sources of uncertainty and risk, whose precise evolution is unpredictable and can compromise the future sustainability of this kind of supply chain FAO, 2021). ...
... The intrinsic complexity of this system is apparent in the sources of uncertainty and the risks that supply chain firms face. Several studies have laid bare the agricultural sources of uncertainty (Esteso et al., 2017;Esteso et al., 2018). In the research by Esteso et al. (2018), four types of crop-based uncertainty have been identified: product (shelf-life, deterioration rate, lack of homogeneity, food quality and food safety), process (harvesting yield, supply lead time, resource needs and production), market (demand and market prices) and environment (weather, pests and diseases and regulations). ...
The paper investigates the ongoing digital transformation process in a firm operat-ing in the agri-food sector by analyzing the approach adopted, the barriers and challenges faced when implementing digital technologies, and the impact of the Covid-19 crisis on this process. The authors opted for a qualitative approach based on a single case study. The results suggest that the preferred approach of top management played a crucial role in supporting the changes brought about by the transformation. Specifically, it is a conscious, incremental, and critical approach that combines digitalization and craftsmanship. The research reveals four barriers to the implementation of digital technologies: resistance to change, a lack of digi-tal skills, an inadequate organizational structure, and financial constraints. Fur-thermore, the results show that the Covid-19 crisis has accelerated the implemen-tation of digital technologies, which was already in progress during the pre-pandemic period.
... Moreover, the unemployment rate in India has doubled over the last two years and reached 8 percent in Dec 2021 (Biswas, 2022). The above figures indicate that social sustainability issues such as farmers' economic welfare and growth, balanced economic development, job opportunities and malnutrition need special attention while designing food supply chain networks in developing economies (Esteso et al., 2018;Zhu et al., 2018;De and Singh, 2021). ...
... It is imperative to consider such sustainability while designing an agri-food supply chain network (Ghadge et al., 2017, Rohmer et al., 2019, Jonkman et al., 2019, Mogale et al., 2019, Mangla et al. 2018, Mohebalizadehgashti et al. 2020. However, the holistic consideration of all three dimensions of sustainability in agri-food supply chain network design has appeared in a limited number of studies (Esteso et al., 2018, Zhu et al., 2018, Banasik et al., 2019, Mohammed and Wang, 2017a, Govindan, 2018, Martins et al., 2019Ghadge et al., 2021). It is observed that economic and environmental dimensions are comprehensively (independent as well as combined) discussed in the extant literature, with limited consideration towards the social dimension. ...
... Various academic reviews, including Akkerman et al. (2010), Soto-Silva et al. (2016), Shukla andJharkharia (2013), Ahumada andVillalobos (2009), Esteso et al. (2018) and Zhu et al. (2018), discuss multiple decision support models for the FSC network design and identify the scarcities of model(s) with the integration of specific factors like the number of jobs created, balanced economic development, economic welfare of farmers, and public health levels. The importance of multi-objective mathematical models to tackle FSC problems in emerging economies was recently highlighted by Esteso et al. (2018) and Zhu et al. (2018). The various issues starting from the farmer through to the customer, need of sustainability, lack of consideration of all actors, amalgamation of the intrinsic features and complex network of FSCs are deliberated in these studies. ...
The food grain production in India has progressively risen in the past few decades, whereas the storage capacity has remained limited. The policymakers in India are attempting to close this capacity gap while addressing sustainability objectives. However, the quantification and integration of multiple social sustainability factors have remained a challenge. To improve the overall sustainability, the study attempts to develop a mathematical model considering procurement, transportation, inventory, and location-related issues. Several supply chain network factors are integrated and assessed while focussing on the social sustainability dimension. Three cases of India's largest food grain-producing and consuming states are analysed with the help of two Pareto-based algorithms. Multiple relationships between variations in supply, demand, and the capacity of silos with three defined objectives are evaluated. It is observed that, the demand significantly influences the economic and environmental objectives compared with the supply and silo capacity. The capacity of silos has a more significant impact on social objectives than economic and environmental objectives. Results reveal the importance of establishing a sufficient number of modernised silos, which reduces environmental impact and improves social factors such as farmers' economic condition and welfare, balanced economic development, number of jobs created, and public health level. The study supports policymakers in making sustainable decisions within food supply chains.
... Modeling perishability, considering supply and demand risks, multi-period modeling, resilient strategies [5] Propose a conceptual framework for AFSC designs and present a review of mathematical models ...
... Three crucial quality elements for AFSCs were identified as information, sustainability, and logistics management. Ref. [5] suggested a conceptual framework for the design of AFSCs addressing chain characteristics, uncertainty modeling, decision characteristics, and modeling approaches. Following that, a comprehensive review was conducted for mathematical programming models. ...
... A new classification scheme (Figure 2) is developed based on the studies of[2,5,10]. ...
This paper provides a comprehensive review of the research done on optimization models that focus on harvest and production planning for food crops. Optimization models have been used extensively in providing insights to decision-makers on issues related to harvest and production planning in agri-food supply chains. First, we conduct an extensive literature review on previous survey articles to distinguish our research from others. Based on the previous reviews, a new classification scheme is developed to classify articles systematically. Harvest and production planning problems in agri-food supply chains are analyzed through three sections: problem scope, model characteristics, and modeling approach. Neglected problem topics and several promising research directions are presented to stimulate research interest on agri-food supply chains specifically planning of harvest and production.
... The agri-food sector is the largest manufacturing sector in Europe. It employs more than four million people and produces a revenue of more than one trillion euro [1] . Up to 88 million tons of food are wasted every year in Europe, which accounts for 20% of production [2] . ...
... To reduce the AFSC environmental impact, the importance of considering product perishability in AFSC design is highlighted [7][8][9] . Consumer product perishability perceptions impact the economic, environmental and social aspects of AFSC and waste generation [1] . Additionally, one of the main goals in distributing agri-food products is to guarantee product freshness [10] , which is related to product perishability. ...
... This model combines strategical (selection and location of facilities to be opened, as well as their role) and tactical (planting, cultivating, harvest, labouring, packing, storage, operation and distribution of products) decisions by considering products' shelf life, which represents the real AFSC characteristics and improves AFSC performance in the long, mid and short terms [1] . ...
Perishability of agri-food products impacts the economic, environmental, and social aspects of agri-food supply chains (AFSCs). Product perishability is, usually, considered in tactical and operational decisions, but not in strategic ones, such as the design of the AFSC. The contribution of this paper is that it investigates the impact of product perishability on an AFSC design. To do so, first, a novel mixed-integer linear programming model is proposed to design entire AFSCs with multiple-products, which considers capacity, planting, harvesting, transporting and perishability constraints for a multiple-period horizon. A set of scenarios is generated by varying products’ shelf-life and analysed. The results show that product perishability is relevant when designing AFSCs, especially for products with a short shelf-life. The results show that an AFSC's economic performance improves when product perishability is considered. The model can also help in determining the investment needed extend products shelf-life while remaining profitable. Other uses could include tactical planning for chains already in place.
... Since these supply chains are responsible for providing sustainable, affordable, safe and sufficient food, feed, fibre and fuel to consumers, it is critical to ensure that they operate smoothly and successfully in the increasingly volatile business environment (KPMG 2013). However, designing such smooth and stable AFSCs is extremely difficult for operation managers due to the involvement of various risks and risk-driving factors (Diabat, Govindan, and Panicker 2012;Esteso, Alemany, and Ortiz 2018). ...
... The production process is associated with biological production, which is affected by weather variability, pests and diseases, seasonal factors, and price variability (Weintraub and Romero 2006). In the processing stage, there are special risks associated with food quality and safety (Esteso, Alemany, and Ortiz 2018). For example, contamination is the most serious of food safety-related risks that may occur in the production and processing stages, and may involve incidents that could constitute a public health emergency of domestic or international concern (Dani and Deep 2010). ...
... Although many studies (e.g. Wagner and Bode 2006;Tang and Tomlin 2008;Esteso, Alemany, and Ortiz 2018) have analysed the risk factors in the supply chain context from an empirical perspective, this study identified five new risk factors; these are oral contract or agreement with partners, skill shortage, lack of investment in promoting agri-food products, tax evasion, and rapid technological development. We extend existing studies that primarily focus on supply chain risk identification. ...
Agri-food supply chains (AFSCs) are becoming more complex in structure, and thus more susceptible to different vulnera-bilities and risks. Therefore, to enhance performance, we need to manage the risks in AFSCs effectively and efficiently. This study analyses various AFSC risks using a multi-method approach, including thematic analysis, total interpretive structural modelling (TISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis. Based on the empirical data collected from experienced AFSC practitioners and following thematic analysis, eight categories of risk and 16 risk factors were identified as important. Furthermore, the interrelationships among the identified risks were built using TISM. Finally, the identified risks were classified into various categories according to their dependence and driving power using fuzzy MICMAC analysis. The research results indicate that the weather-related and political risks have the highest driving power and are located at the lowest level in the TISM hierarchy. These risks have a high tendency to disturb the whole flow of AFSC and so should be managed effectively. This study advances existing literature on identifying risk factors , defining interrelations between different AFSC risks, and determining the key risks. The risk analysis results can help AFSC practitioners in AFSC to identify, categorise and analyse the risks.
... In this situation, an increasing number of recent research works recognize the necessity of implementing collaboration mechanisms among the members of fruit and vegetable SCs for achieving sustainability (Prima Dania, Xing, & Amer, 2018), increase revenues and customer satisfaction and reduce the negative impact of uncertainty (Esteso, Alemany, & Ortiz, 2018). In their review, Handayati, Simatupang, and Perdana (2015), identify mathematical modelling as one methodology used in agri-food supply chain coordination that has proven also its validity for the crop planning problem (Saranya and Amudha, 2017). ...
... As it can be seen in Table 3, four models include uncertainty: three of them, model uncertain parameters as stochastic (Ahumada, Villalobos, & Mason, 2012;Costa et al., 2014;Tan & Çömden, 2012) and only one model them as fuzzy (Miller, Leung, Azhar, & Sargent, 1997), but this last one not for planting decisions. Stochastic approaches imply that it is possible to estimate the probability distribution of random parameters (Esteso et al., 2018). Zeng, Kang, Li, Zhang, and Guo (2010) pointed out that for the cropping plan problem the estimation of proper distribution of uncertain parameters is not always possible due to difficulty in obtaining (i) historical data (Alemany, Grillo, Ortiz, & Fuertes-Miquel, 2015), and the estimation of variance and mean not being possible to apply stochastic programming. ...
Imbalance between supply and demand of crops frequently occurs in markets originating an excess or shortage of supply in relation to demand. This causes high volatility and uncertainty in market prices, unmet demand, and waste, especially for fresh crops due to their limited shelf-life. This imbalance is mainly due to the inherent uncertainty present in the agricultural sector, the perishability of fresh crops, and the lack of coordination among farmers when making planting and harvesting decisions. Despite farmers usually plan the planting and harvesting in an individual way, there is a scarcity of research addressing the crop planning problem in a distributed manner and, even less, assessing their impact on the supply chain (SC) as a whole. In this paper, we developed a set of novel mathematical programming models to plan the planting and harvest of fresh tomatoes under a sustainable point of view for multi-farmer supply chains under uncertainty in different decision-making scenarios: i) distributed, ii) distributed with maximum and minimum land area constraints to be planted for each crop, iii) distributed with information sharing, and iv) centralized. Then, for each distributed scenario, the individual solution per farmer as regards the planting and harvesting decisions per crop are integrated to obtain the overall supply to satisfy the markets demand. This allows the assessment of the farmers’ real performance and the impact of their individual decisions to the entire SC performance. We also compare the results obtained for each scenario with the centralized model in terms of economic, environmental, and social impact. The experimental design shows that, when integrating the solutions for the whole SC, significant differences between planned and real results are obtained in each scenario as regards the gross margin per hectare, unmet demand, waste, and unfairness between farmers, being the distributed model with information sharing the most similar to the centralized one. The results show that uncertainty consideration in models improves the gross margin and the unfairness among farmers in all scenarios for both, planned and real evaluation.
... Majority of the existing studies on food supply chains are carried out in the developed countries and the developing nations mainly focused on satisfying the need of rising population and ignored the environmental aspect (Shukla and Jharkharia 2013;Soto-Silva et al. 2016). The decision support models considering the finite planning horizon and SCND issues in emerging economies need to be formulated to improve the food supply chain performance (Esteso et al. 2018;Zhu et al. 2018). The finite number of mixed capacitated vehicles are included which overlooked in the work of Asgari et al. (2013). ...
... The finite number of mixed capacitated vehicles are included which overlooked in the work of Asgari et al. (2013). The limited number of studies conducted the comparative analysis of metaheuristic algorithms for food supply chain problems (Allaoui et al. 2018;Mohammed and Wang 2017;Esteso et al. 2018). The various members in food grain supply chain such as government agencies, railways, and other private service providers can obtain beneficial and essential managerial insights from this research work. ...
The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decision-making process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results.
... To achieve robust, resilient and sustainable agri-food supply chains is very complex because they face more sources of uncertainty and risks in comparison with other supply chains that give rise to serious questions and concerns about their economic, environmental and social performance. Several studies identify agricultural sources of uncertainty (Mundi et al., 2019;Esteso et al., 2017;Esteso et al., 2018) and how to model them (Grillo et al., 2019). In Esteso et al. (2018) four types of crop-based uncertainty are identified: Product (shelf-life, deterioration rate, lack of homogeneity, food quality and food safety), Process (harvesting yield, supply lead time, resource needs, production), Market (demand, market prices) and Environment (weather, pests & diseases and regulations). ...
... Several studies identify agricultural sources of uncertainty (Mundi et al., 2019;Esteso et al., 2017;Esteso et al., 2018) and how to model them (Grillo et al., 2019). In Esteso et al. (2018) four types of crop-based uncertainty are identified: Product (shelf-life, deterioration rate, lack of homogeneity, food quality and food safety), Process (harvesting yield, supply lead time, resource needs, production), Market (demand, market prices) and Environment (weather, pests & diseases and regulations). Poor management of these sources of uncertainty can have a very negative impact on safety, quality, quantity and waste of products as well as human, technological and natural resources. ...
... To achieve robust, resilient and sustainable agri-food supply chains is very complex because they face more sources of uncertainty and risks in comparison with other supply chains that give rise to serious questions and concerns about their economic, environmental and social performance. Several studies identify agricultural sources of uncertainty [117,118,119] and how to model them [120]. In [119] four types of crop-based uncertainty are identified: Product (shelf-life, deterioration rate, lack of homogeneity, food quality and food safety), Process (harvesting yield, supply lead time, resource needs, production), Market (demand, market prices) and Environment (weather, pests & diseases and regulations). ...
... Several studies identify agricultural sources of uncertainty [117,118,119] and how to model them [120]. In [119] four types of crop-based uncertainty are identified: Product (shelf-life, deterioration rate, lack of homogeneity, food quality and food safety), Process (harvesting yield, supply lead time, resource needs, production), Market (demand, market prices) and Environment (weather, pests & diseases and regulations). Poor management of these sources of uncertainty can have a very negative impact on safety, quality, quantity and waste of products as well as human, technological and natural resources. ...
The term “Agri-Food 4.0” is an analogy to the term "Industry 4.0", coming from the concept “agriculture 4.0”. Since the origins of the industrial revolution, where the steam engines started the concept of Industry 1.0 and later the use of electricity upgraded the concept to Industry 2.0, the use of technologies generated a milestone in the industry revolution by addressing the Industry 3.0 concept. Hence, Industry 4.0, it is about including and integrating the latest developments based on digital technologies as well as the interoperability process across them. This allows enterprises to transmit real-time information in terms behaviour and performance. Therefore, the challenge is to maintain these complex networked structures efficiently linked and organised within the use of such technologies, especially to identify and satisfy supply chain stakeholders dynamic requirements. In this context, the agriculture domain is not an exception although it possesses some specialities depending from the domain. In fact, all agricultural machinery incorporates electronic controls and has entered to the digital age, enhancing their current performance. In addition, electronics, using sensors and drones, support the data collection of several agriculture key aspects, such as weather, geographical spatialization, animals and crops behaviours, as well as the entire farm life cycle. However, the use of the right methods and methodologies for enhancing agriculture supply chains performance is still a challenge, thus the concept of Industry 4.0 has evolved and adapted to agriculture 4.0 in order analyse the behaviours and performance in this specific domain. Thus, the question mark on how agriculture 4.0 support a better supply chain decision-making process, or how can help to save time to farmer to make effective decision based on objective data, remains open. Therefore, in this survey, a review of more than hundred papers on new technologies and the new available supply chains methods are analysed and contrasted to understand the future paths of the Agri- Food domain.
... Indeed, the development of optimization and decision support tools is needed to obtain all the benefits of transactional information technology (IT), improving the economic performance and customer satisfaction of supply chains (Grossmann, 2005). Along these lines, mathematical programming models have been demonstrated to be powerful optimization tools to support decision makers in many supply chain processes such as: production planning , order promising (Alemany et al., 2018;Grillo et al., 2017), shortage planning (Esteso et al., 2018a(Esteso et al., , 2018b, supply chain production and transport planning (Mula et al., 2010), among others. The agriculture sector also faces many complex problems for optimization (Saranya & Amudha, 2017) as it has been reported in some recent works (Cid-Garcia & Ibarra-Rojas, 2019; Grillo et al., 2017;Liu et al., 2019). ...
... The agriculture sector also faces many complex problems for optimization (Saranya & Amudha, 2017) as it has been reported in some recent works (Cid-Garcia & Ibarra-Rojas, 2019; Grillo et al., 2017;Liu et al., 2019). Some revisions about mathematical programming models applied to different problems in agriculture can be found for supply chain design (Esteso et al., 2018b), fresh fuit supply chain management (Soto-Silva et al., 2016), agribusiness supply chain risk management (Behzadi et al., 2018) and crop planning (Jain et al., 2018), among others. ...
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... The agribusiness sector has distinctive characteristics; indeed, these companies must face various uncertainties, including crop yields, weather conditions, procurement time, shelf life and spoilage rates of products, market prices, pests, diseases, regulations, etc. [91,96,98,99]. These sources of uncertainty are completely unpredictable [96,100]. ...
COVID-19 has had a dramatic impact on the world. This study aims to investigate the possible implications of COVID-19 on sustainability and digitalization initiatives, by exploring a sample of 15 Italian coffee companies located in Northern, Central and Southern Italy, to highlight any differences and at the same time identify which are the main strands of their resilient behaviors. "Sustainab-lization" is our idea to define a business model in which sustainability and digitalization are closely related in companies' strategic initiatives. We have analyzed the various actions which have been undertaken to get out of the COVID-19 crisis, focusing on initiatives related to sustainable development and digitalization, critical also to fulfilling some of the 17 Sustainable Development Goals of the 2030 Agenda. Most of the companies have invested in sustainability and digitalization. The results show, for most of them, a resilient approach towards a sustainable business model, and also through increased digitalization.
... For producers, a timely forecast of yield can support the optimization of agronomic management [11,12]. At the same time, the agri-food industry is interested in yield forecasts to optimize food processing, storage, transport, and marketing [7,[13][14][15][16]. ...
Timely yield prediction is crucial for the agri-food supply chain as a whole. However, different stakeholders in the agri-food sector require different levels of accuracy and lead times in which a yield prediction should be available. For the producers, predictions during the growing season are essential to ensure that information is available early enough for the timely implementation of agronomic decisions, while industries can wait until later in the season to optimize their production process and increase their production traceability. In this study, we used machine learning algorithms, dynamic and static predictors, and a phenology approach to determine the time for issuing the yield prediction. In addition, the effect of data reduction was evaluated by comparing results obtained with and without principal component analysis (PCA). Gaussian process regression (GPR) was the best for predicting maize yield. Its best performance (nRMSE of 13.31%) was obtained late in the season and with the full set of predictors (vegetation indices, meteorological and soil predictors). In contrast, neural network (NNET) and support vector machines linear basis function (SVMl) achieved their best accuracy with only vegetation indices and at the tasseling phenological stage. Only slight differences in performance were observed between the algorithms considered, highlighting that the main factors influencing performance are the timing of the yield prediction and the predictors with which the machine learning algorithms are fed. Interestingly, PCA was instrumental in increasing the performances of NNET after this stage. An additional benefit of the application of PCA was the overall reduction between 12 and 30.20% in the standard deviation of the maize yield prediction performance from the leave one-year outer-loop cross-validation, depending on the feature set.
... Recent studies explicitly take the identification of changes in the agricultural food supply chain as the premise of establishing the model in order to obtain more applicable solutions. Esteso, Alemany, and Ortiz (2018) proposed to consider the inherent characteristics of the supply chain and the source of uncertainty when developing the optimisation model. Flores et al. (2019) designed a cross regional agricultural product production and distribution system considering climate differences. ...
The application of new information technologies such as the Internet of Things (IoT) has caused a deep impact on production and operations management in various fields. In this paper, a mixed-integer programming model is proposed to generate integrative decision-making in the IoT-enabled fresh agri-products supply chains. The designed model integrates three key stages, that is, planting, storage, and distribution, to help growers make the optimal decisions for maximising revenue. Decisions are made after comprehensive consideration of market factors such as price and demand as well as agricultural characteristics such as crop yield and shelf life. Results of numerical experiments show that significant improvement of benefits can be obtained through the overall decision-making of planting, storage, and distribution. Additionally, it may be most beneficial for growers to keep the warehouse’s storage time and storage capacity at a medium level. The IoT-based integrative decision-making method explored in this study can be applied to other fields including manufacturing to achieve more efficient production and operations management.
... There are numerous frameworks in the literature that design mathematical programming modeling to solve supply chain management and optimization problems, covering stages in the farm-to-forks journey: production, processing, distribution, retailing and marketing. Based on the metrics that policy makers wish to optimize for, these existing optimization models can be classified as network design, facility location, resource and capacity allocation, market and supply allocation, vehicle routing, transport mode, transport capacity allocation, and so on (Esteso et al., 2018). The objectives of these models are to minimize costs, improve performance, or better achieve customer goals and expectations. ...
Facility locations are crucial determinants of supply chain efficiency for aggregating and distributing products. The multi-disciplinary nature of the facility location problem requires multiple complementary approaches, at different levels of aggregation, to accommodate the salient features of location determinants. This study examines the facility location problem for the U.S. fresh produce supply chain. We present a model that incorporates an empirical scenario into a facility location problem in order to capture much of the information required to make an optimal location decision. Our results suggest that the efficiency of facility locations can be improved without significantly increasing the operating costs. This study sheds light on how the application of complementary modeling approaches improves the effectiveness of facility location solutions.
... Furthermore, agri-food products are perishable and seasonal, and annual production variations make it difficult to control the quality and quantity of outputs. All these factors pose threats to AFSCs, making distribution networks extremely vulnerable to various risks (Esteso, Alemany, and Ortiz 2018;Pereira, Scarpin, and Neto 2021;Roth and Zheng 2021). Thus, AFSCs' resilience to environmental volatility requires re-evaluation (Stone and Rahimifard 2018;Zhao et al. 2018;Drozdibob et al. 2022). ...
Smooth, efficient agri-food supply chain (AFSC) operations are becoming ever more difficult due to more intense and frequent natural disasters and man-made disruptions. Helping AFSCs to survive disturbances requires reconsideration of how to build their resilience. This study addresses this issue through a crosscountry comparative analysis involving interviews with AFSC practitioners, thematic analysis to generate agri-food supply chain resilience (AFSCRes) capability factors, total interpretive structural modelling (TISM) to establish interrelationships among the factors, cross-impact matrix multiplication applied to classification (MICMAC) analysis to categorise the factors, and comparative analysis. The results reveal that contractual restraints regulating farmers' opportunistic behaviour and regular interactions are key factors for building AFSCRes in France and Argentina, respectively. This study also confirms the critical role of farmers' associations and coordinated activities amongst all AFSC stake-holders to build AFSCRes. For triggering AFSCRes, farmers' resilience must be particularly prioritised, as they are the least resilient point in AFSCs. ARTICLE HISTORY
... The management of food supply chains has received increased attention (Amorim, Curcio, Almada-Lobo, Barbosa-Póvoa, & Grossmann, 2016;Maiyar & Thakkar, 2019b;Mogale, Kumar, Kumar, & Tiwari, 2018). Prior research highlights the need for addressing issues related to integrating supply chain functions, sustainability aspects, and the development of decision support models (Esteso et al., 2018;Goswami et al., 2020;Govindan, 2018;Zhu et al., 2018). The bulk of previous research focussing on modelling food supply chains are scenario specific and there is a lack of generic models (De et al., 2019;Mogale et al., 2018. ...
Food supply chains encompass multiple actors and simultaneously produce multiple products that require transportation using various modes or networks before arriving on consumers’ tables. Transportation costs and related carbon emissions along a supply chain, however, can be high, prompting a search for efficient management solutions. This paper proposes a mathematical formulation in the form of a mixed-integer linear programming model, drawing on evidence from a Norwegian salmon supply chain network. The model addresses environmental aspects by aiming to minimize the fuel cost component from various transportation modes and considers carbon emissions related restrictions. Testing using various problem instances highlights the robustness of the proposed mathematical formulation and models. Moreover, a real-world case study of a Norwegian salmon exporter helps understand the applicability of the proposed model. The paper discusses the impact of different supply chain arrangements regarding their overall cost, including fuel cost, and carbon emissions to understand the need for holistic optimization of food supply chains. Sensitivity analysis regarding demand variability allows the proposed mathematical model to restructure the Norwegian salmon supply chain network to meet fluctuating retail demand. Transportation scenario analysis emphasizes the importance of shifting from road to maritime transportation for certain routes to achieve financial and environmental gains.
... PSO was used for the distribution part. Researchers also used the LINGO + PSO model to optimize the production and transport cost of the agri-food supply chain network (Esteso et al., 2018). To optimize the multi-objective routing problem PSO has been used. ...
Bio-inspired optimization techniques (BOT) are part of intelligent computing techniques. There are several BOTs available and many new BOTs are evolving in this era of industrial revolution 4.0. Genetic algorithm, particle swarm optimization, artificial bee colony, and grey wolf optimization are the techniques explored by researchers in the field of food processing technology. Although, there are other potential methods that may efficiently solve the optimum related problem in food industries. In this review, the mathematical background of the techniques, their application and the potential microbial-based optimization methods with higher precision has been surveyed for a complete and comprehensive understanding of BOTs along with their mechanism of functioning. These techniques can simulate the process efficiently and able to find the near-to-optimal value expeditiously.
... The AFCS is a multilevel supply chain (SC) network consisting of primary production, production of semi-product by plants, production of finished products and distribution centers with multiple products like cereal, grains, fruits, and other vegetables (Fuchigami et al., 2019). The role of the ASC is to deliver agricultural products from farms to the consumer (Esteso et al., 2018). ASCs are one of the largest production sectors in Europe, with 4.25 million employees and 1 trillion dollars of financial turnover in the economy. ...
Due to the nature of the agricultural and food industry, the management of production, storage, transportation, waste disposal and environmental effects of their production, are of great importance. To deal with the sustainability issues linked to their supply chains, we propose in this study a mathematical model to design a sustainable supply chain of highly perishable agricultural product (strawberry). The model is a multiperiod, multiproduct multiobjective MINLP mathematical program that takes into consideration economic, social and environmental objectives to cover all aspects of sustainability. In addition, a G/M/S/M queuing system is developed for the transportation of harvested products between facilities for the first time. Since real-world problems related to industries such as food and agriculture are inherently uncertain, in this model, the important parameters of the problem are considered uncertain using fuzzy sets theory and a hybrid robust possibilistic programming model is developed. In addition, the Epsilon constraint approach converts the multiobjective mathematical model into a single-objective one and the Lagrangian relaxation method is used to effectively solve the model on a large scale. A case study in Iran is provided to investigate the results and discuss the solutions. Finally, a sensitivity analysis is performed to identify the impacts of important parameters on the solution. According to the analysis, equipping greenhouses with drip irrigation system and using solar panels in greenhouses, respectively, have the greatest impact on improving all target functions.
Recommendations for Resource Managers
• Multiobjective optimization shows trade-offs among conflicting objective function and assists decision-making to enhance sustainable agriculture industry.
• Focus on transportation system in fresh product will lead to less waste.
• The use of solar panels and drip irrigation helps to minimize water and energy consumption and CO2 emission.
... The complexity of the food grain supply chain network has prompted researchers to study it from different perspectives during the last decade (Ahumada & Villalobos, 2009;Akkerman et al., 2010;Amorim et al., 2016;Brandenburg et al., 2014;Esteso et al., 2018;Soysal et al., 2014). Most of the reported research on the modeling of food grain supply chain networks is generic, and hence, there is a need to develop specific food grain supply chain networks. ...
The procurement of food grains from farmers is one of the biggest challenges under the COVID-19 outbreak due to country-wise lockdowns. The present study aims to reconfigure the existing food grain supply chain network. The study advances the extant literature by proposing a novel mathematical model that considers the government guidelines issued to procure food grains from farmers under the COVID-19 situation. The model includes personal distancing, a key parameter relevant in the COVID-19 crisis, and has remained unaddressed in the existing literature. The proposed model is tested in India. The effect of different parameters like personal distancing cost, carbon emission cost, fixed cost, and transportation cost is also investigated under a given set of procurement centers. Finally, the procurement schedule for each procurement center is generated, which is especially useful for managing its activities and is also helpful to farmers to streamline the process. Results indicate that the proposed model is highly effective under pandemic emergencies like the current COVID-19 crisis. Policymakers and the government will find this model helpful in drafting relevant policies regarding food grain procurement under emergencies such as the COVID-19 outbreak. The distribution segment of the supply chain network is not part of the present research work. In future studies, this part could be then added to the whole of the procurement process, and both procurement and distribution can be assessed together again.
... Awad et al. (2020) have reviewed the research in cold food supply chain and emphasized on the need for integration of quality and environmental consideration in vehicle and route planning. Esteso et al. (2018) reviews the literature in Agri-food supply chain with a special focus on the inherent characteristics of agricultural produce and the sources of uncertainty. It is important to consider these two important factors while designing food supply chain networks in order to improve the chain performance and maintain food quality. ...
The global concern to ensure the availability of food for the growing world’s population draws urgent attention towards the inefficiencies in agri-food supply chains. Agri-food supply chains are inherently complex to manage than other supply chains mainly because of their multi-echelon structure, deteriorating product quality with time and changes in storage conditions which leads to significant amount of food loss and wastage. Additionally, any natural or man-made disaster further disrupts the chain and leads to high food loss, high supply chain costs, reduced food availability and poor food quality. Hence, there is a need to design resilient and efficient agri-food supply chain network for optimal multi-echelon storage and distribution to reduce food loss and quality degradation. For this purpose, a Fuzzy Multi Objective Linear Program (FMOLP) is proposed in this paper for integrated food procurement, storage and distribution under cost, resilience and quality considerations. The proposed model integrates the short-term operational objective of cost optimization with the long-term sustainable objectives of food loss minimization and resilience maximization. The proposed FMOLP is illustrated using a realistic case of Public Distribution System using the data benchmarked with the numbers reported by the Food corporation of India. The detailed computational analysis carried out in the paper in investigates three categories of problem sizes to compare and contrast the decisions using different strategies and to provide organizational, operational and policy insights on the trade-off between cost, food loss and resilience.
... The qualitative approach proved to devise a policy to find an optimum location for agricultural residues. Esteso et al. [74] proposed a decision support framework for the agri-food sector. Studying multiple studies in the literature, the authors proposed a strategy to design and analyze mathematical programming in this area. ...
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.
... When reviewing the bullwhip effect in perishable products, [8] indicates that research does not look at the level of waste, interaction with similar products, and impact over multiple periods. This process [9] states that it is better to focus on improving forecasting methods than clearly defining safety inventory. ...
The bullwhip effect results from inefficiencies in the supply chain; in perishable products, the inefficiencies are quality in the supply chain and product waste. We carried out a literature review to determine the causes of the bullwhip effect and the supply chain’s quality factors of this phenomenon’s perishable products. Update the demand, the level of deterioration of the product, and the number of intermediaries is the causes of the bullwhip effect most investigated. On the other hand, the product’s safety and the quality of the information are the quality factors of the chain of supplies of perishable products more researched. Future research should address the causes of human behavior that affect the bullwhip effect in the perishable goods supply chain.
... It must be mentioned that, traditionally, determining positions and number of actors, amount of product flow and best transportation costs are handled as a network design problem in supply chain management. In this sense, there are some typical models to assist the decision-making process in AFSCs, as described in Esteso et al. (2018a); they broadly lie in the area of mathematical programming, ranging from linear programming to stochastic and fuzzy programming. The role of mathematical programming is pre-eminent in the context of fresh agri-product supply chain (Fuchigami et al. 2019). ...
A survey on the technologies employed in the modern agriculture and agri-food supply chains lately appeared, but only one paper using a fuzzy-based approach was cited. The aim of the present mini-review is to complement the above-mentioned survey and to show the application of different fuzzy-based approaches for agri-food supply chains. Agri-food supply chains represent linked events in the agricultural production of food, where all the stages of production, processing, trading, distribution and consumption are involved. These supply chains are expected to provide sustainable, affordable, safe and sufficient food and other derivatives to the consumers. Hence, it is critical to ensure that they operate properly and successfully in the volatile business environment. A first concern is to assess the service ability of the whole supply chain to preserve agri-food quality, eliminate deterioration and meet the demands of customers. Due to their complex structure, agri-food supply chains are susceptible to several vulnerabilities and risks, such as breakdowns, operational difficulties, and credit loss and economic losses due to various uncertain factors. A risk analysis can help to identify and categorize the risks. In this era characterized by the rapid industrialization of the agriculture and the increased global food demand, the sustainability and transparency of supply chains have become key factors. The new focus on sustainability emphasizes the issue of striking a balance between ecological and economic aspects in the agri-food business. In this context, problems such as the green supplier selection gained special attention.
... A mathematical model was produced for the problem and was solved using an augmented e-constraint approach. Similarly,Esteso et al. (2018) suggested a model for designing agro-food supply chains. In the study, development and analysis of models constructed on mathematical programming was performed for network design.Mangla et al. (2018) identified and investigated the potential enablers for successful sustainable plans in the domain of agro-food supply chains. ...
Purpose
Due to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain.
Design/methodology/approach
A mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set.
Findings
The model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time.
Research limitations/implications
In literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries.
Originality/value
The suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.
... Table 1 shows that several research studies have used either stochastic programming or robust optimisation. Vlajic, Van der Vorst, and Haijema (2012) and Stone and Rahimifard (2018) proposed a conceptual framework to design robust FSCs (see also Esteso, Alemany, and Ortiz 2018). An and Ouyang (2016) studied post-harvest loss minimisation and profit maximisation of a food company through a bilevel robust optimisation model. ...
Given the inevitable globalisation in the food sector and the specific security challenges this industry faces, designing food supply chains has become a substantial topic for academics and practitioners. The integration of food product-specific characteristics and potential disruptions has continuously gained importance because it better reflects real-world problems and responds to a crucial need for resilience, robustness, and competitiveness. In this article, a generic two-stage mixed-integer mathematical model is developed to integrate key features of location-allocation and inventory-replenishment decisions. Then, food-specific disruptions with ripple effects are incorporated through plausible scenarios. For such a setting, three resiliency strategies – namely, readiness, flexibility, and responsiveness – are used to deal with uncertainties. Based on extensive numerical experiments, the solutions obtained highlight behaviour of different design models to hedge against ripple effects as well as the importance of incorporating food-specific assumptions and risk aversion attitudes.
... This section analyses the changes RAS bring to the food supply chains. The analysis considers major aspects, which were identified in recent works, including food quality, food safety, food waste, supply chain efficiency, and supply chain analysis (Esteso et al., 2018;Zhong Farm 1940 informs 717 energy 529 Products 1856 operators 716 inventory 522 data 1754 control 707 supplying 520 foods 1727 plants 668 case 519 agriculture 1644 impact 662 lands 513 adapt 1477 industry 660 effects 507 changing 1351 performs 656 measuring 503 technology 1338 computing 651 water 498 robots 1284 approach 650 sensors 494 process 1279 methods 625 automations 488 crops 1201 relation 614 rates 483 managing 1111 order 591 implements 483 imaging 1025 Sustainability 585 ...
Background.
An increasing global population means resource utilisation and food security remain the critical global challenges. Robotics and autonomous systems (RAS) have been applied to improve productivity, and their role in enhancing supply chain operations is explored here.
Scope and approach.
Researchers have studied ways to adopt and integrate RAS into the food industry. However, most of the current literature focuses on the technological impact of RAS. In contrast, this paper discusses the adoption of RAS in the food industry from the supply chain perspective with regard to the supply chain operations.
Key findings and conclusions.
The study has selected 54 papers using a nine-step systematic review process. This research analysed the use of RAS across five major themes of the food supply chain, including food quality, food safety, food waste, supply chain efficiency, and supply chain analysis. The role of RAS in data availability, cybersecurity, skill capability, and financial costs are discussed in the context of adopting RAS in food supply chains. Future research directions are proposed with the aim of enhancing the application of RAS in food supply chain operations.
... El sector agrícola está caracterizado por una gran cantidad de incertidumbres que impactan sobre su comportamiento y que generan la necesidad de diseñar y gestionar CS resilientes. Además, la sociedad está especialmente concienciada con respecto a la sostenibilidad del sector agroalimentario, donde se incluye el componente social de la perspectiva de género (Esteso, Alemany, and Ortiz 2018). Por ello, se decide aplicar la herramienta propuesta para evaluar la situación de la perspectiva de género en CSs a una CS del sector agrícola. ...
Para mejorar la igualdad de género, reducir daños de reputación en las organizaciones y mejorar su resiliencia, es necesario analizar su situación real y participar en un diálogo constructivo. Sin embargo, a menudo éstas carecen de la formación necesaria para implementar políticas exitosas con ese fin. Este artículo trata de cubrir dicho vacío proponiendo una herramienta para evaluar la inclusión de género actual en las CS. Se evalúan tres objetivos: 1) garantizar negocios con organizaciones que respeten la igualdad de género, 2) promover el emprendimiento femenino y 3) divulgar políticas de igualdad de género a lo largo de la CS.
... As the food manufacturing industry embraces sustainability, the increasing scarcity of natural resources that have been experienced globally causes a serious concern on the environment and continue to serve as a major constraint on future food production, contributing to reduced quantity, quality and affordability of food in many countries ( Forster, 2013 ). At the supply chain level, the food industry is also facing sustainability issues ( Beske et al., 2014 ;Gold et al., 2017 ;Zhu et al., 2018 ), robustness to vulnerabilities and disruptions ( Vlajic et al., 2012 ), uncertainty modeling ( Esteso et al., 2018 ), among others. These developments are highly associated, in one way or another, with product design. ...
With quality function deployment (QFD) as its basis, the product design team is instrumental in promoting sustainability by incorporating relevant requirements in the early stages of the design process. The domain literature, however, contains significant gaps, especially for food products. First, the current literature fails to consider the requirements of all relevant stakeholders, which are crucial to sustainability. Second, while some offer various models of fuzzy QFD – multiple-attribute decision-making (QFD-MADM), they fail to comprehensively address the underlying interdependencies of decision parameters in the QFD. Furthermore, the majority of the works on QFD-MADM limit themselves to product planning while losing control over other subsequent phases of product development. Thus, this work attempts to advance these gaps by proposing an integrated multiphase fuzzy QFD-MADM framework that combines QFD, analytic hierarchy process (AHP), decision-making trial and evaluation laboratory (DEMATEL), and analytic network process (ANP) along with fuzzy set theory. A case study in a Philippine meat processing industry was implemented to demonstrate the proposed approach. The results of the case study show the crucial decision parameters for all phases, which would serve as inputs to design teams. Unlike previous models, the proposed framework preserves the transition of the priorities flow along with all four phases of product development. Thus, the stakeholder requirements are integrated into all product development stages, which is a strong indication that these requirements are addressed in each phase. Also, the proposed framework ensures that the uncertainty and the underlying complexities of interdependencies among decision parameters of the four phases of product design and development are addressed. The proposed framework contributes to sustainable product design literature in a manner that is comprehensive and analytical.
... Therefore, a more integrated view of the ASC phases is encouraged (see Ahumada and Villalobos 2009), with a particular attention devoted to avoid undesired perishability costs and a dangerous quality loss of the products. Esteso et al. (2018) propose conceptual frameworks to design ASC performance through mathematical programming modelling while considering their inherent characteristics and sources of uncertainty. Banasik et al. (2019) propose a bi-objective stochastic programming model for a real-life case arising in the ASC. ...
In this paper, we propose a dynamic and stochastic approach for an inventory routing problem in which products with a high perishability must be delivered from a supplier to a set of customers. This problem falls within the agri-food supply chain (\({\mathcal {ASC}}\)) management field, which includes all the activities from production to distribution. The need for high-quality products that are subject to perishability is a critical issue to consider in the \({\mathcal {ASC}}\) optimization. Moreover, the demand uncertainty makes the problem very challenging. In order to effectively manage all these features, a rolling horizon approach based on a multistage stochastic linear program is proposed. Computational experiments over medium-size instances designed on the basis of the real data provided by an agri-food company operating in Southern Italy show the effectiveness of the proposed approach.
... He of models increases with food product characteristics and advanced algorithms are needed to solve them(Esteso et al. 2018, Golini et al. 2017Yakovleva et al. 2012). A comprehensive review of nature-inspired metaheuristic algorithms employed for partition clustering and automatic clustering is presented byNanda and Panda (2014) and José-García and Gómez-Flores (2016). ...
The procurement of food grains from farmers and their transportation to regional level has become decisive due to increasing food demand and post-harvest losses in developing countries. To overcome these challenges, this paper attempts to develop a robust data-driven supply chain model for the efficient procurement of food grains in India. Following the data collected from three leading wheat producing Indian regions, a mixed-integer linear programming model is formulated for minimising total supply chain network costs and determining number and location of procurement centres. The NK Hybrid Genetic Algorithm (NKHGA) is employed to cluster the villages, along with a novel density-based approach to optimise the supply chain network. Sensitivity analysis indicates that policymakers should focus on creating an adequate number of procurement centres in each surplus state, well before the start of the harvesting season. The study is expected to benefit food grain supply chain stakeholders such as farmers, procurement agencies, logistics providers and government bodies in making an informed decision.
Purpose
This paper investigates the overall system risk for a foodgrains supply chain capturing the interrelationship among the risk factors and the effect of risk mitigation strategies.
Design/methodology/approach
This paper first calculates the weight of risk factors using an integrated approach of failure mode, effects analysis and fuzzy VIKOR technique. Next, the weights are utilized as input for the weighted fuzzy Petri-net (WFPN) approach to calculate the system risk.
Findings
Two different WFPN models are developed based on the relationships among the risk factors, and both models demonstrate a higher risk value for the overall system.
Originality/value
The proposed methodology will help practitioners or managers understand the complexity involved in the system by capturing the interrelationship behaviour. This study also considers the concurrent effect of risk mitigation strategies for calculating the overall system risk, which helps to improve the system’s performance.
The agri-food sector is subject to various sources of uncertainty and risk that can have a negative impact on its supply chain performance if not properly managed. In order to determine what actions the supply chain (SC) should take to protect itself against risks, it is necessary to analyze whether the supply chain is robust to them. This paper proposes a tool based on a system dynamics model to determine the robustness of an already designed five-stage fresh agri-food supply chain (AFSC) and its planting planning to disruptions in demand, supply, transport, and the operability of its nodes. The model is validated using the known behavior replication test and the extreme conditions test. In order to guide decision-makers in the different uses of the above system dynamic model, a methodology for the improvement of the AFSC robustness is presented and applied to a case study. As a result, the SC robustness to the defined disruptions is provided. For critical disruptions, protective actions are defined. Finally, the model is re-run to evaluate the impact of these proactive strategies on the AFSC in order to finally select the most beneficial for improving its robustness.
As a result of rapid industrialisation, rising food demand globally, and, increasing concerns associated with food safety and quality, the implementation of sustainable supply chain concepts is becoming critically important to the agro-food sector. This paper introduces an integrated first-mile pickup and last-mile delivery logistics problem, where agro-food grains are available at multiple farmer’s locations and are in demand by businesses like e-retailers, supermarkets, grocery shops, restaurants, hotels, etc. In addition, this work addresses a sustainable framework for agro-food grains supply chain (AFGSC) in urban and rural areas for e-commerce in developing countries. The proposed optimisation model considers costs related to first-mile pickup, transportation with last-mile delivery, carbon emission tax, inventory holding, vehicle and food damage due to accidents, and penalties on late pickup and delivery. This model also takes environmental and social (due to accidents) sustainability aspects into consideration, along with the economic aspects of sustainability. To solve the large complex practical scenarios by using four nature-inspired algorithms. The obtained results of this study are used to recommend significant managerial insights for implementing AFGSC in the e-commerce industry in considering practical conditions. Moreover, policy implications in terms of economic, social, and environmental aspects of sustainability are also discussed.
Purpose
The purpose of the paper is to present research trends in the food supply chain in the context of changes in food systems due to globalization, urbanization, environmental concerns, technological changes and changes in food consumption patterns in the world.
Design/methodology/approach
The present investigation was performed by bibliometric analysis using the VOSviewer software, visualization software developed by Nees and Waltman (2020). In this work we performed co-citation, bibliographic coupling and keyword evolution analyses.
Findings
The results show that research in the food supply chain is rapidly changing and growing. By applying co-citation analysis, The authors found that the intellectual structure of the food supply chain has evolved around six clusters, namely, (a) collaboration and integration in the supply chain (b) sustainable supply chain management, (c) food supply chain management (FSCM), (d) models for decision-making in the food supply chain, (e) risk management in the supply chain and (g) quality and food logistics in the supply chain. However, based on bibliographic coupling analysis, The authors find that new or emerging research niches are moving toward food supply market access, innovation and technology, food waste management and halal FSCM. Nevertheless, the authors found that the existing research in each of the thematic clusters is not exhaustive.
Research limitations/implications
The limitation of the research is that the analysis mainly relates only to the bibliometric approach and only one database, namely, Scopus. Broader inclusion of databases and deeper application of content analysis could expand the results of this research.
Originality/value
There are limited studies that have examined research trends in food supply chains in both developed and developing countries using bibliometric analysis. The present investigation is novel in identifying the thematic research clusters in the food supply chain, emerging issues and likely future research directions. This is important given the dynamics, consumer demand for quality food, technological changes and environmental sustainability issues in food systems.
This study configures various carbon regulation mechanisms to control carbon emissions following clean technology strategies in engine oil production. Considering clean technology strategies for designing a sustainable supply chain (SSC) in the engine oil industry, two carbon reduction policies, namely, carbon capacity and carbon emissions tax, are discussed to study the effects of environmental factors. A mixed-integer linear programming model that examines demand, technology, budget, carbon policies, and capacity constraints under several uncertainties is proposed for engine oil production from petrochemical resources, refinery plant production, and distribution system capacities. This study controls and mitigates risk and timing decisions for output decisions from a hybrid robust-heuristic-based method, wherein a modified scenario-based GA is used to eliminate the effect of uncertainties. The results indicate high-quality convergence of solutions for different strategic scenarios. We successfully apply the introduced model to address a real-world supply chain (SC) of the engine oil industry. The proposed model improves the state-of-the-art models for the engine oil SC. Finally, the study finding shows that managers can improve technologies with the lowest possible cost, maximum product profitability, and minimum possible losses in the production process and product quality through the carbon tax policy to reduce the environmental effects.
Management of agricultural supply chains is a very complex task due to some critical factors like perishability, demand- and price-variability, yield uncertainty. Motivated by a large agricultural firm that deals with planting, growing, harvesting and distributing cauliflowers, an optimization model is introduced for the simultaneous management of storage and shipment of agricultural products, aiming for profit maximization, while accounting for product perishability. In view of a contractual agreement, the firm has to ensure, along the entire season, the delivery of at least a given share of harvested products to the main contract-customer. Nonetheless, the firm can catch more profitable opportunities on the daily spot-market. The model embeds a hybrid fresh/old-first policy to account for the firm priority policy that aims to balance quality of products delivered to the main customer. The model has been adopted at both the tactical and the operational decision level. At the tactical level, the model has been used to select the fleet size and the maximum in-stock time of products, while at the operational one, the model has been used for the day-by-day planning of storage and shipment. Computational experiments show the possibility for the company to profitably upgrade its current operation practices.
Recent market studies showed that the demand for organic and local agrifood products is increasing despite their higher prices. The agribusiness actors should therefore rethink the supply chain configuration to cope with new market trends characterized by the rise of the organic segment and the increase of consumers' preference to more local products. This study focuses on the olive oil sector and proposes a mixed-integer non-linear optimization model for the design of olive oil supply chains while incorporating organic and conventional market segments and considering, for each segment, a supply chain proximity- and price-sensitive demand. The model is developed with the collaboration of olive oil producers in the Mediterranean area. Thanks to this industrial collaboration, we account for real-world practices and constraints and apply the model to a realistic case study. We first linearize the model and show that it can be efficiently solved with commercial optimization softwares. Based on numerical experiments, we derive a series of managerial insights that are applicable to the considered case study, some of them are not intuitive. For instance, we show that an increase in consumers’ preference to more local products may lead the producer to offer products with a more global supply chain. The conventional product variety may be produced with a more local supply chain than the organic (premium) variety. Finally, offering a mix of organic and conventional varieties instead of only one variety would lead to implementing a more local supply chain.
The latest shift in the industry, known as industry 4.0, has introduced new challenges in manufacturing. The main characteristic of this transformation is digital technologies’ effect on the way production processes occur. Due to the technological growth, knowledge and skills on manufacturing operations are becoming obsolete. Hence, the need for upskilling and reskilling individuals urges. In collaboration with other key entities, educational institutions are responsible for raising awareness and interest of young students to reach a qualified and equal workforce. Drawing on a thorough literature review focused on key empirical studies on learning factories and fundamental industry 4.0 concepts, trends, teaching approaches, and required skills, the goal of this paper is to provide a gateway to understand effective learning factories’ approaches and a holistic understanding of the role of advanced and collaborative learning practices in the so-called education 4.0.
The crop planning problem consists in defining the crop and acreage to be planted at each farm. There are several centralized mathematical programming models to support crop planning in literature. However, centralized solutions often produce economic unfairness among the members of the supply chain, being especially relevant among the farmers in the agri-food sector. To solve it, this paper tries to answer the following research question: is it possible to reduce inequalities among the farmers through a collaborative plan? A centralized multi-objective mathematical programming model to support crop planning and the next decisions up to the sale of vegetables through a collaborative plan is proposed to answer this question. To show the validity of the proposed collaborative plan, results obtained are compared against those obtained without collaboration. The analysis of results shows that inequalities among the supply chain members can be highly reduced in a centralized decision-making approach by implementing the proposed collaborative plan, reducing a bit the supply chain profit.
Agri-food production must increase while food waste needs to be reduced for improving the position of farmers. To do so it is necessary to sustainably manage agri-food supply chains beginning with the crop planning decisions. Although the centralized approach has usually been adopted for this purpose, it can lead to unfair solutions due to inequitable distribution of profits among farmers causing their unwillingness to collaborate in the implementation of decisions made. To solve this, in this paper a novel centralized multi-objective mathematical programming model is proposed to support the sustainable crop planning definition for a region that jointly optimize three objectives aligned to the sustainability aspects: supply chain profits maximization (economic objective), waste minimization (environmental objective) and unfairness among farmers minimization (social objective), being the last two objectives novel in the crop planning literature. It has also shown the conflicting nature of the three objectives finding trade-offs among them. Other novelties of this proposal are: (1) anticipation of operative decisions (such as harvest, transport, sale, clearance sale, waste and unmet demand) when defining the crop planning, (2) possibility of clearing the oversupply of crops as a means of increasing the farmers’ profits and reducing waste, and (3) the modelling of a agri-food supply chain characterized by the lack of intermediaries between farmers and retailers, fostering the freshest product delivery and farmers’ power position. The model is solved by applying the weighted sum method concluding that the crop waste generated along the chain and the unfairness among farmers can be considerably reduced by little decreasing the optimal SC profits.
Operations planning is an important step in any activity as it aligns resources to achieve economic production value. In agriculture operations where uncertainty is present, planners must deal with biological and environmental factors, among others, which add variability and complexity to the production planning process. In this work, we consider operations planning to harvest grapes for wine production where uncertainty in weather conditions will affect the quality of grapes and, consequently, the economic value of the product. In this setting, planners make decisions on labor allocation and harvesting schedules, considering uncertainty of future rain. Weather uncertainty is modeled following a Markov Chain approach, in which rain affects the quality of grapes and labor productivity. We compare an expected value with a multi-stage stochastic optimization approach using standard metrics such as Value of Stochastic Solution and Expected Value of Perfect Information. We analyze the impact of grape quality over time, if they are not harvested on the optimal ripeness day, and also consider differences in ability between workers, which accounts for the impact of rain in their productivity. Results are presented for a small grape harvest instance and we compare the performance of both models under different scenarios of uncertainty, manpower ability, and product qualities. Results indicate that the multi-stage approach produces better results than the expected value approach, especially under high uncertainty and high grape quality scenarios. Worker ability is also a mechanism for dealing with uncertainty, and both models take advantage of this variable.
In recent years, agriculture has faced many challenges, from a growing global population to be fed, the work power evasion in the sector, to sustainability requirements and environmental constraints. To satisfy the increasingly demanding stakeholders, the agricultural sector has looked for new ways to tackle these issues. In this context, Information and Communications Technologies (ICTs) have been applied to help the agricultural sector overcome these challenges. This article investigates how two ICTs-connectivity and cloud computing-can leverage and traverse other ICTs, such as Internet of Things and artificial intelligence, enabling the entire productive sector to be supported by decision-making systems, which in turn are based on data-driven models. Moreover, a successful case study on how cloud computing has helped one of SiDi’s biggest customers-a global company-improve its operational performance by obtaining insights from its data is presented.
Food supply chains are nowadays perturbed by an increased supply and demand uncertainty, and more and more suffering from unexpected disruptions. In the specific context of food supply chains (FSC) for perishable products, these could be linked to natural hazards, industrial accidents or epidemics and their impact could lead to huge economic losses. The case of epidemic events has been little studied in the existing literature, although there are numerous cases reported in practice. At the strategic level, this requires a novel risk modeling approach to tackle the correlation and propagation features and advanced stochastic multi-period models to design the FSC network. Our interest in this research is to propose a comprehensive two-stage scenario-based mathematical model to design a resilient food supply chain under demand uncertainty and epidemic disruptions. In order to adequately characterize epidemic disruptions, they are modeled as a compound stochastic process and a Monte Carlo procedure is developed to generate plausible scenarios. The modeling approach covers the special characteristics of FSC, such as products perishability in time and discount prices based on product's age. In addition, a number of resiliency strategies are incorporated into the core model to enhance the resilience level of the FSC network design. The developed models are solved through an efficient solution approach relying on scenario reduction technique and Benders decomposition. Numerous problem instances are used to validate the modeling approach and to derive managerial insights.
A plant factory is an environmentally controlled facility that can sustain stable crop cultivation while ensuring fast production and better crop quality by manipulating temperature, humidity, lighting, nutrient supply, and other cultivation factors. It requires better cultivation planning to fully utilise the facility since the set up and operating costs are high. This study aims to schedule crops in a commercial plant factory to maximise revenue by determining which crops are cultivated, the quantity, and at what time. The model considers not only crop market prices but also crop properties such as cultivation duration, volume change, multiple periods of harvests, and yield rates under different environmental settings. The problem is formulated as a mixed integer programming problem to find an optimal schedule. For a large size problem, Lagrangian relaxation with surrogate subgradient method is applied to obtain a good solution in a short time. The numerical results show that, compared to the integer program solver, the proposed method provides faster solutions with more than 80% efficacy when longer planning periods and multiple cultivation rooms are considered.
Successful pumpkins production requires the use of varieties that jointly with other factors yield well and produce pumpkins of the size, shape, color, and quality demanded by the market. But not only these issues are important. The perishable nature of pumpkins makes other issues such as how to prevent deterioration after harvest to become also relevant. In this paper the pumpkins plantation, harvest and storage (PHS) process is described and how some decisions affect certain goals, such as yield or conservation time. Additionally, some decision-making insights in a supply chain collaborative scenario made up of two stages: plantation/harvest and storage are given, where yield and conservation time trade-offs are outlined to develop win-win strategies. A real case using data analysis tools is analyzed. Results provide guidelines not only to make decisions independently on each stage but also to collaboratively work.
Crop-based Agri-food Supply Chains (AFSCs) are complex systems that face multiple sources of uncertainty that can cause a significant imbalance between supply and demand in terms of product varieties, quantities, qualities, customer requirements, times and prices, all of which greatly complicate their management. Poor management of these sources of uncertainty in these AFSCs can have negative impact on quality, safety, and sustainability by reducing the logistic efficiency and increasing the waste. Therefore, it becomes crucial to develop models in order to deal with the key sources of uncertainty. For this purpose, it is necessary to precisely understand and define the problem under study. Even, the characterisation process of this domains is also a difficult and time-consuming task, especially when the right directions and standards are not in place. In this chapter, a Conceptual Framework is proposed that systematically collects those aspects that are relevant for an adequate crop-based AFSC management under uncertainty.
Purpose
This paper presents the concerns in agri-food supply chain. Further the research investigates the role of information and communication technology (ICT) in agri-food supply chain and determines the impact of supply chain management (SCM) practices on firm performance.
Design/methodology/approach
The theoretical framework was proposed for the study on the basis of existing literature. Data for the study was collected with the help of structured questionnaire from 121 executives and officers of the public food distribution agency. Partial least square (PLS)–structured equation modeling was employed to test the framework and hypotheses.
Findings
The results indicate that ICT and SCM practices (logistics integration and supplier relationships) have a significant relationship. Furthermore, SCM practices (information sharing, supplier relationship and logistics integration) have a significant and positive impact on performance of the organization.
Research limitations/implications
Further research could be carried out to test the moderation effect of SCM practices between ICT and organizational performance (OP). Extending the research study to the companies operating in other sectors can enhance the external validity of the study and improve the accuracy of parameters examined.
Practical implications
This study can be of interest to the agri-food industry as well as other industry practitioners interested in improving the performance of the organization from the view of supply chain.
Originality/value
The outcomes of this study have important implications that translate into a series of recommendations for the management of public food distribution as well as other agri-food-based supply chains.
Mathematical programming models are invaluable tools at decision making, assisting managers to uncover otherwise unattainable means to optimize their processes. However, the value they provide is only as good as their capacity to capture the process domain. This information can only be obtained from stakeholders, i.e., clients or users, who can hardly communicate the requirements clearly and completely. Besides, existing conceptual models of mathematical programming models are not standardized, nor is the process of deriving the mathematical programming model from the concept model, which remains ad hoc. In this paper, we propose an agile methodology to construct mathematical programming models based on two techniques from requirements
engineering that have been proven effective at requirements elicitation: the language extended lexicon (LEL) and scenarios. Using the pair of LEL + scenarios allows to create a conceptual model that is clear and complete enough to derive a mathematical programming model that effectively captures the business domain. We also
define an ontology to describe the pair LEL + scenarios, which has been implemented with a semantic mediawiki and allows the collaborative construction of the conceptual model and the semi-automatic derivation of mathematical programming model elements. The process is applied and validated in a known fresh tomato packing optimization problem. This proposal can be of high relevance for the development and implementation
of mathematical programming models for optimizing agriculture and supply chain management related processes in order to fill the current gap between mathematical programming models in the theory and the practice.
Share link: https://authors.elsevier.com/a/1aXLqcFCSJOLQ
Faced with the challenges associated with sustainably feeding the world’s growing population, the food industry is increasingly relying on operations research (OR) techniques to achieve economic, environmental and social sustainability. It is therefore important to understand the context-specific model-oriented applications of OR techniques in the sustainable food supply chain (SFSC) domain. While existing food supply chain reviews provide an excellent basis for this process, the explicit consideration of sustainability from a model-oriented perspective along with a structured outline of relevant SFSC research techniques are missing in extant literature. We attempt to fill this gap by reviewing 83 related scientific journal publications that utilise mathematical modelling techniques to address issues in SFSC. To this end, we first identify the salient dimensions that include economic, environmental and social issues in SFSC. We then review the models and methods that use these dimensions to solve issues that arise in SFSC. We identify some of the main challenges in analytical modelling of SFSC as well as future research directions.
Contexto: el problema de la localización de instalaciones en cadenas de suministros de productos agrícolas perecederos no ha sido suficientemente estudiado, sin embargo, el tema en los últimos años ha tomado interés debido a la preocupación por el consumo de alimentos frescos y saludables, aspecto relevante en países con potencial productor en alimentos hortofrutícolas.
Método: se realizó una revisión sistemática del estado del arte del problema de localización de instalaciones para alimentos agrícolas con énfasis en perecederos; se consultaron las bases de datos: Science Direct, IEEE Xplore, SpringerLink, JSTOR, Scopus y Google Scholar, de 135 artículos de localización en alimentos, finalmente se seleccionaron 31 donde contemplan los modelos de localización en cadenas de suministro de alimentos, con énfasis en perecederos, basados en el criterio de la última década 2004-2015.
Resultados: en la revisión se propone una tipología para la clasificación de variables contempladas en los modelos, se establecieron las variables no contempladas en los modelos, pertinentes para países con las características de Colombia. Se identificaron las funciones objetivo, comúnmente usadas, así como las restricciones específicas para las Cadena de Suministro Alimentaria (CSA) y los drivers logísticos más utilizados en estos modelos. Se identifican líneas de futuras investigación, respecto a variables a incluir en modelos de localización para CSA de perecederos (CSAP) en contexto de países montañosos.
Conclusiones: decisiones estratégicas relevantes como la localización de instalaciones son de importancia durante el diseño de la CSA y de su red de distribución, dado el carácter perecedero de los alimentos frescos. Sin embargo, el tema ha sido poco tratado debido especialmente a la dificultad para el modelamiento y al número de variables a considerar en las CSAP. Se requieren modelos que contemplen la perecibilidad, las pérdidas y el comportamiento estocástico de algunas variables. Países montañosos como Colombia abren puertas a investigaciones que modelen el cambio en los climas, pisos térmicos, condiciones de viaje y su incidencia en la perecibilidad de los alimentos.
Supply chain managers and scholars recognise the importance of managing supply chain risk, especially in fresh food supply chain due to the perishable nature and short life cycle of products. Supply chain risk management consists of supply chain risk assessment, risk evaluation and formulation and implementation of effective risk response strategies. The commonly adopted qualitative methods such as risk assessment matrix to determine the level of risk have limitations. This paper proposes a hybrid model comprising both fuzzy logic (FL) and hierarchical holographic modelling (HHM) techniques where risk is first identified by the HHM method and then assessed using both qualitative risk assessment model (named risk filtering, ranking and management Framework) and fuzzy-based risk assessment method (named FL approach). The risk assessment results by the two different approaches are compared, and the overall risk level of each risk is calculated using the Root Mean Square calculation before identifying response strategies. This novel approach takes advantage of the benefits of both techniques and offsets their drawbacks in certain aspects. A case study in a fresh food supply chain company has been conducted in order to validate the proposed integrated approach on the feasibility of its functionality in a real environment.
Designing Green Supply Chains (GSCs) requires complex decision-support models that can deal with multiple dimensions of sustainability while taking into account specific characteristics of products and their supply chain. Multi-Criteria Decision Making (MCDM) approaches can be used to quantify trade-offs between economic, social, and environmental criteria i.e. to identify green production options. The aim of this paper is to review the use of MCDM approaches for designing efficient and effective GSCs. We develop a conceptual framework to find relevant publications and to categorise papers with respect to decision problems, indicators, and MCDM approaches. The analysis shows that (1) the use of MCDM approaches for designing GSCs is a rather new but emerging research field, (2) most of the publications focus on production and distribution problems, and there are only a few inventory models with environmental considerations, (3) the majority of papers assume all data to be deterministic, (4) little attention has been given to minimisation of waste, (5) numerous indicators are used to account for eco-efficiency, indicating the lack of standards. This study, therefore, identifies the need for more multi-criteria models for real-life GSCs, especially with inclusion of uncertainty in parameters that are associated with GSCs.
The study proposes a cold chain location-allocation configuration decision model for shippers and customers by considering value deterioration and coordination by using big data approximation. Value deterioration is assessed in terms of limited shelf life, opportunity cost, and units of product transportation. In this study, a customer can be defined as a member of any cold chain, such as cold warehouse stores, retailers, and last mile service providers. Each customer only manages products that are in a certain stage of the product life cycle, which is referred to as the expected shelf life. Because of the geographical dispersion of customers and their unpredictable demands as well as the varying shelf life of products, complexity is another challenge in a cold chain. Improved coordination between shippers and customers is expected to reduce this complexity, and this is introduced in the model as a longitudinal factor for service distance requirement. We use big data information that reflects geospatial attributes of location to derive the real feasible distance between shippers and customers. We formulate the cold chain location-allocation decision problem as a mixed integer linear programming problem, which is solved using the CPLEX solver. The proposed decision model increases efficiency, adequately equates supply and demand, and reduces wastage. Our study encourages managers to ship full truck load consignments, to be aware of uneven allocation based on proximity, and to supervise heterogeneous product allocation according to storage requirements.
This paper addresses an integrated framework for deciding about the supplier selection in the processed food industry under uncertainty. The relevance of including tactical production and distribution planning in this procurement decision is assessed. The contribution of this paper is three-fold. Firstly, we propose a new two-stage stochastic mixed-integer programming model for the supplier selection in the process food industry that maximizes profit and minimizes risk of low customer service. Secondly, we reiterate the importance of considering main complexities of food supply chain management such as: perishability of both raw materials and final products; uncertainty at both downstream and upstream parameters; and age dependent demand. Thirdly, we develop a solution method based on a multi-cut Benders decomposition and generalized disjunctive programming. Results indicate that sourcing and branding actions vary significantly between using an integrated and a decoupled approach. The proposed multi-cut Benders decomposition algorithm improved the solutions of the larger instances of this problem when compared with a classical Benders decomposition algorithm and with the solution of the monolithic model.
The supply chain of agricultural products has received a great deal of attention lately due to issues related to public health. Something that has become apparent is that in the near future the design and operation of agricultural supply chains will be subject to more stringent regulations and closer monitoring, in particular those for products destined for human consumption (agri-foods)to follow the rhythm of electronic technology. This work is concerned with the planning of a real agri-food supply chain for poultry products (chicken and turkey-cock meat) with a subcontractor in the city of Tlemcen (Algeria). More precisely, the aim of this work is coordination of decisions for location, allocation and transportation of products toachieve an efficient and green logistic network design and distribution planning. LINGO optimization solver (Version12) has been used to get the solution to the problem.
The floricultural sector is facing market developments that have forced a redesign of the European logistics network. Via workshops and interviews with key stakeholders the main developments and industry needs are identified. These are then summarised in three central themes that require further investigation, i.e. decision problems (e.g. network design and control), context factors (e.g. demand uncertainty and product perishability), and objectives (e.g. efficiency and product quality). Thereafter, 17 articles that review Supply Chain Management (SCM) research are analysed to obtain more insight into the state-of-the-art on these themes and to identify the main issues within the themes and their interrelationships. This resulted in a conceptual research framework in which particular attention is given to how decision problems could be modelled and solved in order to get quantitative insights into the impact of logistics network redesign. Successively, 71 SCM articles were analysed in depth to classify current SCM research and to determine research gaps and challenges. Results show that Floricultural SCM research challenges can be found in integrated, quality-driven and responsive network design and control using hybrid optimisation and simulation.
The amount of energy necessary to cultivate, process, pack and bring the food to European citizens’ tables
accounts for 17 % of the EU's gross energy consumption, equivalent to about 26 % of the EU's final energy consumption in 2013.
Challenges and solutions for decreasing energy consumption and increasing the use of
renewable energy in the European food sector are presented and discussed.
Population growth creates a challenge to food availability and access. To balance supply with growing demand, more food has to move from production to consumption sites. Moreover, demand for locally-grown food is increasing and the U.S. Department of Agriculture (USDA) seeks to develop and strengthen regional and local food systems. This article examines wholesale facility (hub) locations in food supply chain systems on a national scale to facilitate the efficient transfer of food from production regions to consumption locations. It designs an optimal national wholesale or hub location network to serve food consumption markets through efficient connections with production sites. The mathematical formulation is a mixed integer linear programming (MILP) problem that minimizes total network costs which include costs of transporting goods and locating facilities. A scenario study is used to examine the model's sensitivity to parameter changes, including travel distance, hub capacity, transportation cost, etc. An application is made to the U.S. fruit and vegetable industry. We demonstrate how parameter changes affect the optimal locations and number of wholesale facilities.
This paper addresses the wholesale hub location problem in food supply chains. The paper aims to design an optimal hub location network to serve food consumption markets through efficient connections with production sites. These optimal locations can be compared with the current locations of hubs to determine whether changes could lead to greater efficiencies. The model is mathematically formulated as a mixed-integer programming problem. The model minimizes the total network costs, which include the transportation of goods and the construction of hubs. The mathematical program considers several constraints on travel distance, hub capital cost and capacity, road condition, and transportation cost. Several experiments are conducted to test the sensitivity of the model to changes in parameters such as the food's average travel distance, the maximum hub capacity, and the transportation cost. Then, a real-world application is made to the Northeast United States livestock industry. Finally, the results show the effect of the changes in model parameters on the optimal hub network design (i.e., the number of hubs and the selection of hub locations).
The supply chain of agricultural products has received a great deal of attention lately due to issues related to public health. Something that has become apparent is that in the near future the design and operation of agricultural supply chains will be subject to more stringent regulations and closer monitoring, in particular those for products destined for human consumption (agri-foods). This work is concerned with the planning of a real agri-food supply chain for chicken meat for the city of Tlemcen in Algéria. The agri-food supply chain network design is a critical planning problem for reducing the cost of the chain. More precisely the problem is to redesign the existing supply chain and to optimize the distribution planning. As mentioned in our paper, the entire problem is decomposed into two problems, and each problem is solved in sequential manner, to get the final solution. LINGO optimization solver (12.0) has been used to get the solution to the problem.
p>ENGLISH ABSTRACT: This research presents an integrated multi-objective distribution model for use in simultaneous strategic and operational food supply chain (SC) planning. The proposed method is adopted to allow use of a performance measurement system that includes conflicting objectives such as distribution costs, customer service level (safety stock holding), resource utilisation, and the total delivery time, with reference to multiple warehouse capacities and uncertain forecast demands. To deal with these objectives and enable the decision makers (DMs) to evaluate a greater number of alternative solutions, three different approaches are implemented in the proposed solution procedure. A detailed case study derived from food industrial data is used to illustrate the preference of the proposed approach. The proposed method yields an efficient solution and an overall degree of DMs’ satisfaction with the determined objective values.
AFRIKAANSE OPSOMMING: Die navorsing behandel ’n geïntegreerde multidoelwit distribusiemodel vir strategiese beplanning van ’n voedseltoevoerketting. Om met die model doelmatig te werk, moet ’n versameling van randvoorwaardes hanteer word om die saamgestelde optimiseringsdoelwit te bereik teen ’n agtergrond van uiteenlopende sienings.</p
The supply planning of agricultural products and in particular perishable products is a critical issue in the supply chain management field due to high safety and quality risks associated with the delays in the products delivery. This work is concerned with the planning of a real agri-food supply chain for poultry products. More precisely the problem is to redesign the existing supply chain and to optimize the distribution planning. To this aim a clustering-based location-routing model is applied in a sequential manner. Furthermore, environmental costs of road transportation in terms of CO2 emissions are taken into account in the computations. The proposed integrated approach permits to minimise the total costs of the agri-food supply chain not only in terms of economy but also in terms of ecology.
En este trabajo se aborda el problema de localización de plantas de producción con
restricción de capacidad. Los datos de demanda, número de clientes, número de plantas y capacidad de éstas son conocidos. El problema es solucionado mediante la aplicación de Relajación Lagrangeana y Búsqueda Tabú los cuales brindan buenas soluciones para este tipo de problemas. La programación realizada se prueba con instancias encontradas en la literatura que permiten encontrar resultados factibles con tiempos computacionales aceptables.
The supply chain of agricultural products has received a great deal of attention lately due to issues related to public health. Something that has become apparent is that in the near future the design and operation of agricultural supply chains will be subject to more stringent regulations and closer monitoring, in particular those for products destined for human consumption (agri-foods). The supply chain of agri-foods, as any other supply chain, is a network of organizations working together in different processes and activities in order to bring products and services to the market, with the purpose of satisfying customers' demands.
After years of emphasis on leanness and responsiveness businesses are now experiencing their vulnerability to supply chain disturbances. Although more literature is appearing on this subject, there is a need for an integrated framework to support the analysis and design of robust food supply chains. In this paper we present such a framework. We define the concept of robustness and classify supply chain disturbances, sources of food supply chain vulnerability, and adequate redesign principles and strategies to achieve robust supply chain performances. To test and illustrate its applicability, the research framework is applied to a meat supply chain.
The growing concern of society about food issues like food quality, safety or sustainability, has increased the OR publications related to agri-food supply chains (AFSC). In turn, the associated literature reviews have greatly increased. It is necessary to organize this excess of information, to know where eh are and where we want to go. In this context, the aim of this paper is threefold: 1) to analyse the literature treviews and the problems they addressed; 2) to conceptualize the relevant dimensions employed by the reviews to analyse and characterize the AFSCs and, 3) to identify which of their future research lines remain valid.
Carbon cap-and-trade regulation is widely adopted to reduce carbon emissions. Under this regulation, we propose a carbon trading mechanism considering refrigerated logistics services in a fresh food supply chain. In addition to supplying fresh food, the supplier offers refrigerated logistics services and overstocked carbon emission permits to the retailer. We study the decisions on the price of emission permits traded within the supply chain, the retail price and the price of refrigerated logistics services in different carbon trading options, without carbon trade, inner carbon trade, inner and outer carbon trade. Pricing strategies for fresh food, emission permits and refrigerated logistics services are provided for supply chain members. We also reveal the relationship between carbon trading and refrigerated logistics services, and investigate their joint influence on the supplier–retailer cooperative relationship. In addition, it is shown that with the implementation of a transfer payment mechanism, supply chain members are motivated to participate in the carbon trading mechanism, which has advantages including improved resource utilisation and more competitive supply chains.
With the development of e-commerce, in agriculture supply chain, online auction is adopted as an inventory clearing tool. Comparing to mathematical models studying inventory control over online sequential auctions, our agent-based simulation model could systematically describe the complexities of bidders’ information interactions and behaviour preferences caused from financial and production perspectives, and by other supply chain members. In addition, we take into account the complex and dynamic market environment, which will impact the operation effect of auction policies. With identical auction items, the profit-maximising firm must decide auction lot-size, which is the number of units in each auction, minimum initial bid, and the time interval between auctions. To obtain the optimal solution, nested partitions framework and optimal expected opportunity cost algorithm are integrated to improve computation accuracy and efficiency. A case study based on real data is conducted to implement and validate the proposed approach. Furthermore, based on the model, the paper studies the sensitivities of the decision variables under different supply and demand scenarios.
The paper proposes a decision support system (DSS) for the supply chain of packaged fresh and highly perishable products. The DSS combines a unique tool for sales forecasting with order planning which includes an individual model selection system equipped with ARIMA, ARIMAX and transfer function forecasting model families, the latter two accounting for the impact of prices. Forecasting model parameters are chosen via two alternative tuning algorithms: a two-step statistical analysis, and a sequential parameter optimisation framework for automatic parameter tuning. The DSS selects the model to apply according to user-defined performance criteria. Then, it considers sales forecasting as a proxy of expected demand and uses it as input for a multi-objective optimisation algorithm that defines a set of non-dominated order proposals with respect to outdating, shortage, freshness of products and residual stock. A set of real data and a benchmark – based on the methods already in use – are employed to evaluate the performance of the proposed DSS. The analysis of different configurations shows that the DSS is suitable for the problem under investigation; in particular, the DSS ensures acceptable forecasting errors and proper computational effort, providing order plans with associated satisfactory performances.
Agri-food supply chains are subjected to many sources of uncertainty. If these uncertainties are not managed properly, they can have a negative impact on the agri-food supply chain (AFSC) performance, its customers, and the environment. In this sense, collaboration is proposed as a possible solution to reduce it. For that, a conceptual framework (CF) for managing uncertainty in a collaborative context is proposed. In this context, this paper seeks to answer the following research questions: What are the existing uncertainty sources in the AFSCs? Can collaboration be used to reduce the uncertainty of AFSCs? Which elements can integrate a CF for managing uncertainty in a collaborative AFSC? The CF proposal is applied to the weather source of uncertainty in order to show its applicability.
This paper proposes a mixed integer mathematical programming model to support the complex order promising process in fruit supply chains. Due to natural factors, such as land, weather or harvesting time, these supply chains present units of the same product that differ in certain relevant attributes to customers (subtypes). This becomes a managerial problem when customers require specific subtypes in their orders. Additionally, the deterioration of the original characteristics of subtypes over time generates waste and gives rise to a shelf life-based pricing policy. Therefore, the developed model should ensure that customers are served not only the quantities and dates, but also the required homogeneity and freshness. The model aims to maximise two conflicting objectives: total profit and mean product freshness. The novelty of the model derives from considering both homogeneity in subtypes as a requirement in customer orders and the traceability of product deterioration over time. Different scenarios are defined according to the weight assigned to each objective, shelf-life length and pricing policy in a rolling horizon scheme. The numerical experiments conducted for a real orange and tangerine supply chain, show the model’s validity and the conflicting behaviour of the two objectives. The highest profit is made at the expense of the lowest mean freshness delivered, which is reinforced by the narrower the price variation with freshness. Finally, the positive impact of prolonging the product’s shelf life on both objectives is shown.
Purpose
The purpose of this paper is to present a study in developing a cost-effective meat supply chain network design aiming to minimizing the total cost of transportation, the number of transportation vehicles and the delivery time of meat products. The developed model was also used for determining the optimum numbers and allocations of farms and abattoirs that need to be established and the optimal quantity flow of livestock from farms to abattoirs and meat products from abattoirs to retailers.
Design/methodology/approach
A multi-objective possibilistic programming model was formulated with a focus on minimizing the total cost of transportation, the number of transportation vehicles and the delivery time of meat products. Three sets of Pareto solutions were obtained using the three different solution methods. These methods are the LP-metrics method, the ɛ-constraint method and the weighted Tchebycheff method, respectively. The TOPSIS method was used for seeking a best Pareto solution as a trade-off decision when optimizing the three conflicting objectives.
Findings
A case study was also applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. The research concludes that the ɛ-constraint method has the superiority over the other two proposed methods as it offers a better solution outcome.
Research limitations/implications
This work addresses as interesting avenues for further research on exploring the delivery planner under different types of uncertainties and transportation means. Also, environmentalism has been increasingly becoming a significant global problem in the present century. Thus, the presented model could be extended to include the environmental aspects as an objective function.
Practical implications
The developed design methodology can be utilized for food supply chain designers. Also, it could be applied to realistic problems in the field of supply chain management.
Originality/value
The paper presents a methodology that can be used for tackling a multi-objective optimization problem of a meat supply chain network design. The proposed optimization method has the potential in solving the similar issue providing a compromising solution due to conflicting objectives in which each needs to be achieved toward an optimum outcome to survive in the competitive sector of food supply chains network.
Purpose
In this paper, the authors investigated a proposed radio-frequency identification (RFID)-based meat supply chain to monitor quality and safety of meat products we purchase from supermarkets. The supply chain consists of farms, abattoirs and retailers. The purpose of this paper is to determine a cost-effective trade-off decision obtained from a developed multi-criteria optimization model based on three objectives. These objectives include customer satisfaction in percentage of product quantity as requested by customers, product quality in numbers of meat products and the total implementation cost. Furthermore, this work was aimed at determining the number and locations of farms and abattoirs that should be established and quantities of products that need to be transported between entities of the proposed supply chain.
Design/methodology/approach
To this aim, a tri-criteria optimization model was developed. The considered criteria were used for minimizing the total implementation cost and maximizing customer satisfaction and product quality. In order to obtain Pareto solutions based on the developed model, four solution approaches were employed. Subsequently, a new decision-making algorithm was developed to select the superior solution approach in terms of values of the three criteria.
Findings
A case study was applied to examine the applicability of the developed model and the performance of the proposed solution approaches. The computational results proved the applicability of the developed model in obtaining a trade-off among the considered criteria and solving the RFID-based meat supply chain design problem.
Practical implications
The developed tri-criteria optimization model can be used by decision makers as an aid to design and optimize food supply chains.
Originality/value
This paper presents a development of first, a cost-effective optimization approach for a proposed RFID-based meat supply chain seeking a trade-off among three conflicting criteria; and second, a new decision-making algorithm which can be used for any multi-criteria problem to select the best Pareto solution.
