Enhancing food supply chain in green logistics with multi-level processing strategy under disruptions

ABSTRACT

Food supply chains (FSCs) have long been exposed to environmental variability and shock events caused by various economic, political, and infrastructural factors. The outbreak of the COVID-19 pandemic has further exposed and identified the vulnerability of FSCs, and promoted integrated optimization approaches for building resilience. However, existing works focusing on general supply chains (SCs) and FSCs have not been fully aware of the distinct characteristics of FSCs in green logistics, i.e., the expiration of fresh products. In reality, perishable food materials can be processed into products of different processing levels (i.e., multi-level processing) for longer shelf lives, which can serve as a timely and economic strategy to increase safety stocks for mitigating disruption risks. Motivated by this fact, we study the problem of enhancing FSC with a multi-level processing strategy. An integrated location, inventory, and distribution planning model for a multi-echelon FSC under COVID-19-related disruptions is formulated to maximize the total profit over a finite planning horizon. Specifically, a two-stage stochastic programming model is presented to hedge against disruption risks, where scenarios are generated to characterize geographical impact induced by source-region disruptions. For small-scale problems, the model can be solved with commercial solvers. To exactly and efficiently solve the large-scale instances, we design an integer L-shaped method. Numerical experiments are conducted on a case study and randomly generated instances to show the efficiency of our model and solution method. Based on the case study, managerial insights are drawn.