A Hybrid Genetic Approach for the Dynamic Vehicle Routing Problem with Time Windows

Abstract

SYNOPTIC ABSTRACTRecent progress in information technology brings new challenges in dealing with the dynamic vehicle routing problem with time windows (DVRPTW). However, few alternate methods to the well-known Tabu Search –based techniques have been proposed so far to solve the DVRPTW efficiently. In this paper, a new hybrid genetic approach (HGA-LCS) to address the DVRPTW is presented. The basic scheme consists in concurrently evolving two populations of solutions to minimize customer service denial, lateness or temporal constraint violation, and total traveled distance. Combining variations of key concepts inspired from routing techniques and search strategies to define new hybrid genetic operators, the proposed approach also exploits a least commitment routing policy in servicing scheduled customers to potentially improve solution quality. The strategy consists in delaying customer visits and therefore premature route construction as long as possible to deal with a larger number of customers all at once when building a solution. Comparative results from a computational experiment over a common set of benchmark problems show the proposed procedure to match or outperform some of the best heuristic routing methods while being fast and highly competitive.