A scenario decomposition algorithm for strategic time window assignment vehicle routing problems. (November 2018)

• Record Type: Journal Article
• Authors: Subramanyam, Anirudh; Wang, Akang; Gounaris, Chrysanthos E.
• Is Part Of: Transportation research. Volume 117(2018)Part A
• Page Start: 296
• Page End: 317
• Publication Date: 2018-11
• Subjects: Vehicle routing under uncertainty -- Time window assignment -- Service consistency -- Stochastic programming -- Decomposition -- Branch-and-bound; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2018.09.008
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: We study the TWAVRP with both continuous and discrete sets of time windows and general scenario-based models of uncertainty for several routing-specific parameters. We propose a novel, decomposition algorithm for the TWAVRP that can be easily parallelized, can utilize any available vehicle routing solver as a black box, and can be readily modified as a heuristic. We propose a new class of path-based disjunctions for the TWAVRP. We conduct a comprehensive numerical study demonstrating that our algorithm outperforms all state of the art methods. Abstract: We study the strategic decision-making problem of assigning time windows to customers in the context of vehicle routing applications that are affected by operational uncertainty. This problem, known as the Time Window Assignment Vehicle Routing Problem, can be viewed as a two-stage stochastic optimization problem, where time window assignments constitute first-stage decisions, vehicle routes adhering to the assigned time windows constitute second-stage decisions, and the objective is to minimize the expected routing costs. To that end, we develop in this paper a new scenario decomposition algorithm to solve the sampled deterministic equivalent of this stochastic model. From a modeling viewpoint, our approach can accommodate both continuous and discrete sets of feasible time window assignments as well as general scenario-based models of uncertainty for several routing-specific parameters, including customer demands and travel times, among others. From an algorithmic viewpoint, our approach can be easily parallelized, can utilize any available vehicle routing solver as a black box, and can be readily modified as a heuristic for large-scale instances. We perform a comprehensive computational study to demonstrate that our algorithm strongly outperforms all existing solution methods, as well as to quantify the trade-off between computational tractability and expected cost savings when considering a larger number of future scenarios during strategic time window assignment.