An effective metaheuristic for the last mile delivery with roaming delivery locations and stochastic travel times. (July 2020)

• Record Type: Journal Article
• Authors: He, Yandong; Qi, Mingyao; Zhou, Fuli; Su, Jiafu
• Is Part Of: Computers & industrial engineering. Volume 145(2020)
• Publication Date: 2020-07
• Subjects: Last mile delivery -- Stochastic travel times -- Roaming delivery locations -- Sampling strategy -- Metaheuristic; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106513
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: Highlights: A new variant of last mile delivery (LMDRDL-STT) is proposed. An effective metaheuristic is proposed to solve this issue. Our algorithm has advantages about solution quality and computational time. The solutions obtained are better and more stable under stochastic environment. Abstract: We consider the last mile delivery system with roaming delivery locations and stochastic travel times. The problem is formulated as a two-stage stochastic programming model with recourses, its objective is to minimize the total travel time of serving a set of customers with roaming delivery locations under stochastic travel times. We propose an effective metaheuristic integrating a sampling strategy (sample average approximation, SAA) to solve stochastic model, our metaheuristic consists of random selection-greedy insertion (RSGI) and hybrid iterated greedy algorithm with route reoptimization and simulated annealing (HIGRR-SA). The computational study shows that our solution method has an advantage in terms of solution quality and computational time. The comparison of the stochastic and deterministic solutions shows that a significant reduction in expected travel time can be achieved as well as a smaller fluctuation considering stochastic information.