Vehicle routing and scheduling problem with two deployment strategies to handle urgent orders in a vaccine supply chain

In this paper, we study a vehicle routing and scheduling problem considering two deployment strategies for handling urgent orders in a vaccine supply chain. In the addressed problem, customers regularly or urgently order various vaccine products delivered by homogeneous vehicles. During the delivery, we consider storage temperature, due date, and shelf life, which are characteristics of vaccine products. Furthermore, we propose two deployment strategies to handle urgent orders. We formulate a mixed integer linear programming model to minimize the total cost for the addressed problem. In the model, we must simultaneously determine the acceptance of urgent orders, the deployment strategies for the accepted urgent orders, and the routing and scheduling of each vehicle. We present a genetic algorithm and particle swarm optimization to efficiently and effectively solve large-size instances. To evaluate the performance of the proposed algorithms, we conduct numerical experiments for large-size instances. The genetic algorithm shows a smaller average relative percentage deviation value than that of the particle swarm optimization in a reasonable CPU time. Additionally, we present managerial insights for two proposed deployment strategies by conducting a sensitivity analysis.