Optimizing food logistics through a stochastic inventory routing problem under energy, waste and workforce concerns

The trend towards sustainable operations management makes it inevitable for companies to carry out their operations by considering environmental and social externalities. This tendency has implications also in the food logistics industry. This study addresses a single-period closed Inventory Routing Problem under environmental and social sustainability concerns in daily food logistics systems. In particular, the study focuses on reducing CO2 emissions in a refrigerated transportation system, collecting and disposing of waste, and offering employees more enticing work schedules, that have not been simultaneously addressed in the literature. The problem has been mathematically defined as a Mixed Integer Linear Programming model. A solution approach based on a clustering algorithm has been proposed to solve large-sized cases. The applicability of the proposed decision support models and the potential practical benefits obtained from their use are shown by performing numerical analyses on an industrial case and a set of larger instances. The results show that simultaneously respecting workforce constraints, waste collection/disposal, and demand uncertainty provide improved economic, environmental and social outputs for food logistics companies. Due to workforce constraints, the delivery time is shortened by 5.5 h, which allow the manufacturing to start later. Respecting waste collection and disposal as well as demand uncertainty enables cost reductions of %40.9 and %6, respectively.