Sustainable food closed-loop supply chain resilience improvement problem

The transition to Industry 5.0 is reshaping supply chain management by emphasizing resilience, sustainability, and human-centricity. However, studies on food supply chains have not simultaneously examined returnable transport items and human-centricity under disruptions. To fill this gap, this paper examines a novel resilience improvement problem for the food closed-loop supply chain, considering two resilience strategies under disruptions. The objective is to minimize the total expected operational cost under worst-case scenarios. For the problem, we develop a distributionally robust chance-constrained programming model and, by exploiting its structural properties, derive an approximate mixed-integer linear programming reformulation. A real-world case study and experiments on randomly generated instances validate the robustness of the proposed strategies and solution approach. Key results demonstrate that resilience requires the synergy of the proposed temporary worker recruitment and returnable transport item inventory redundancy strategies, as isolated approaches are ineffective.