Resilient route design for collection of material from suppliers with split deliveries and stochastic demands

Abstract

The efficient design of routes and schedules for moving materials into manufacturing or assembly plants is a central element of freight logistics systems. However, such route design faces many challenges in practice, including uncertain supplies and operational effectiveness. The work in this paper addresses both the uncertainty in supplier quantities and the need for operational constraints on supplier splitting in the route design process. The model developed here accomplishes this resilient design of routes using a stochastic version of a capacitated clustering formulation. The model is constructed as a two-stage stochastic mixed integer program, and an effective solution method is developed using progressive hedging. An application to logistics operations in the automotive industry is used to validate the model, and the efficiency and stability of the proposed algorithm has been verified by computational experiments.