Sustainable supply chain planning plays an important role to achieve sustainable operations and logistics.Sustainable supply chain performance is based on economic, environmental and social impacts. In this paper, we present a multiobjective decision making framework for sustainable supply chain optimization. We consider a supply chain network consisting of production plants, distribution centers and retailers (customers). A multi-product and multi-period planning model is proposed. Sustainability is evaluated based on three performances: cost, GHG emissions, and service level. We use this model in test case of Frozen Food industry.Preliminary experimentation demonstrates that the three objectives are conflicting. However, just in time distribution might increase total cost but reduce GHG emissions due to the best control of inventories at distribution centers and retailers.Indeed, the energy consumption caused by storing pallet of frozen food decrease. On the other hand, total cost optimization lead to service level decrease with more efficient transportation (Full track Load Delivery) and reduce GHG emission of transportation activities. Finally, the decision making model helps to identify the trade-off between the three conflicting objectives, and take the best decisions to achieve sustainability objectives of the supply chain.
In alignment with the ever-growing interest in adopting sustainable practices, this paper devises a cold supply chain (CSC) planning model that integrates the three pillars of sustainability into the decision-making process while accounting for the shift towards clean energy sources. Interrelated decisions pertaining to production-distribution strategy, backorder and inventory levels, choice of truck type, and selection of third-party logistics (3PLs) providers are jointly optimized. For global CSCs in specific, such decisions are particularly sensitive to the energy sources of the refrigerated facilities and the accompanying levels of CO2 emissions generated. As such, a multi-objective mixed-integer non-linear programming (MINLP) model is developed and then solved via the weighted-sum method. In essence, the model seeks to operationalize sustainability goals by considering the rapidly evolving transition in energy sources across different regions when deciding on which 3PLs to engage in a contractual agreement with while adjusting the production and distribution strategy accordingly. The practical relevance of the model is illustrated using a case study drawn from the North American frozen food industry. The conducted trade-off analysis indicates the possibility of obtaining a drastic improvement of 86% in jobs’ stability levels (social measure) with a maximum cost increase of around 9% as compared to the economic measure. Furthermore, the analysis reveals that it is possible to reduce 71% of CO2 emissions while attaining 63% reduction in worker variations at the expense of only 4.47% cost increase once compared to solely optimizing the economic objective.
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