Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: A distributionally robust approach
•Proposing a bi-objective multi-echelon perishable food supply chain network.•Considering supplier selection, scheduling, and routing problems in an integrated model.•Applying a distributionally robust approach to address the uncertainty of demand.•Mitigating the uncertainty of supply by considering...
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| Vydané v: | Computers & industrial engineering Ročník 175; s. 108845 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.01.2023
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| ISSN: | 0360-8352, 1879-0550 |
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| Abstract | •Proposing a bi-objective multi-echelon perishable food supply chain network.•Considering supplier selection, scheduling, and routing problems in an integrated model.•Applying a distributionally robust approach to address the uncertainty of demand.•Mitigating the uncertainty of supply by considering the reliability of suppliers.•Assessing the impacts of the variation of influential parameters on the objectives.
Due to the global outbreak of COVID-19, the perishable product supply chains have been impacted in different ways, and consequently, the risks of food insecurity have been increased in many affected countries. The uncertainty in supply and demand of perishable products, are among the most influential factors impacting the supply chain networks. Accordingly, the provision and distribution of food and other perishable commodities have become much more important than in the past. In this study, a bi-objective optimization model is proposed for a three-echelon perishable food supply chain (PFSC) network with multiple products to formulate an integrated supplier selection, production scheduling, and vehicle routing problem. The proposed model aims to mitigate the risks of demand and supply uncertainties and reinforce the distribution-related decisions by simultaneously optimizing the total network costs and suppliers’ reliability. Using the distributionally robust modeling paradigm, the probability distribution of uncertain demand is assumed to belong to an ambiguity set with given moment information. Accordingly, distributionally robust chance-constrained approach is applied to ensure that the demands of retailers and capacity of vehicles are satisfied with high probability. Leveraging duality and linearization techniques, the proposed model is reformulated as a mixed-integer linear program. Then, the weighted goal programming approach is adopted to address the multi-objectiveness of the proposed optimization model. To certify the performance and applicability of the model, a real-world case study in the poultry industry is investigated. Finally, the sensitivity analysis is conducted to evaluate the impacts of influential parameters on the objective functions and optimal decisions, and then some managerial insights are provided based on the obtained results. |
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| AbstractList | •Proposing a bi-objective multi-echelon perishable food supply chain network.•Considering supplier selection, scheduling, and routing problems in an integrated model.•Applying a distributionally robust approach to address the uncertainty of demand.•Mitigating the uncertainty of supply by considering the reliability of suppliers.•Assessing the impacts of the variation of influential parameters on the objectives.
Due to the global outbreak of COVID-19, the perishable product supply chains have been impacted in different ways, and consequently, the risks of food insecurity have been increased in many affected countries. The uncertainty in supply and demand of perishable products, are among the most influential factors impacting the supply chain networks. Accordingly, the provision and distribution of food and other perishable commodities have become much more important than in the past. In this study, a bi-objective optimization model is proposed for a three-echelon perishable food supply chain (PFSC) network with multiple products to formulate an integrated supplier selection, production scheduling, and vehicle routing problem. The proposed model aims to mitigate the risks of demand and supply uncertainties and reinforce the distribution-related decisions by simultaneously optimizing the total network costs and suppliers’ reliability. Using the distributionally robust modeling paradigm, the probability distribution of uncertain demand is assumed to belong to an ambiguity set with given moment information. Accordingly, distributionally robust chance-constrained approach is applied to ensure that the demands of retailers and capacity of vehicles are satisfied with high probability. Leveraging duality and linearization techniques, the proposed model is reformulated as a mixed-integer linear program. Then, the weighted goal programming approach is adopted to address the multi-objectiveness of the proposed optimization model. To certify the performance and applicability of the model, a real-world case study in the poultry industry is investigated. Finally, the sensitivity analysis is conducted to evaluate the impacts of influential parameters on the objective functions and optimal decisions, and then some managerial insights are provided based on the obtained results. |
| ArticleNumber | 108845 |
| Author | Ji, Ran Ghorbani, Fahimeh Hashemi-Amiri, Omid |
| Author_xml | – sequence: 1 givenname: Omid surname: Hashemi-Amiri fullname: Hashemi-Amiri, Omid email: ohashem@gmu.edu – sequence: 2 givenname: Fahimeh surname: Ghorbani fullname: Ghorbani, Fahimeh email: fghorban@gmu.edu – sequence: 3 givenname: Ran surname: Ji fullname: Ji, Ran email: rji2@gmu.edu |
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| Keywords | Supplier selection Chance-constrained programming Production scheduling Perishable products Vehicle routing Distributionally robust optimization |
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