Resilient route design for collection of material from suppliers with split deliveries and stochastic demands. (October 2022)

• Record Type: Journal Article
• Authors: Sasha Dong, Zhijie; Xie, Chi; Dai, Rui; Hu, Shaolong
• Is Part Of: Computers & operations research. Volume 146(2022)
• Publication Date: 2022-10
• Subjects: Freight transportation -- Clustering -- Stochastic programming -- Split delivery -- Progressive hedging; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2022.105902
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: Studying a routes and schedules design problem with uncertain supplier quantities. Studying the problem as a stochastic clustering problem. Presenting a two-stage stochastic programming model. Developing an adaptive progressive hedging algorithm for large-size problems. 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.