LAUSR

Abstract Modern service platforms are characterized by heterogenous products collected and re-distributed from different suppliers to many buyers. Based on the recent findings from the food science/engineering literature, we identify a new phenomenon of deterioration rate variation risk in the context of sustainable cross-docking service. A perishable product’s deterioration rate may vary due to the unstable logistic environment that is typically found on the cross-docking service platform. Products’ deterioration and on-site environmental information is collected by the IoT and sensor system. A mixed-integer programming model is formulated to model the perishable product truck scheduling problem on the cross-docking platform. Based on a customized genetic algorithm, the numerical analyses are conducted on 4 scenarios and 20 instances. Results show significant benefits of incorporating the deterioration rate variation risk analysis on a multi-supplier multi-buyer multi-product cross docking service platform.