Tackling Dynamic Vehicle Routing Problem with Time Windows by means of Ant Colony System

TL;DR

This paper introduces an ant colony optimization algorithm for the dynamic vehicle routing problem with time windows, effectively updating routes in real-time as new requests arrive, and improves solution quality and vehicle utilization.

Contribution

It proposes a novel joint solution construction mechanism combined with local search and insertion heuristics specifically designed for DVRPTW.

Findings

The algorithm is competitive and effective on benchmark instances.

It outperforms existing ant-based approaches on highly dynamic instances.

Solutions include reduced vehicle usage and improved routing efficiency.

Abstract

The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is an extension of the well-known Vehicle Routing Problem (VRP), which takes into account the dynamic nature of the problem. This aspect requires the vehicle routes to be updated in an ongoing manner as new customer requests arrive in the system and must be incorporated into an evolving schedule during the working day. Besides the vehicle capacity constraint involved in the classical VRP, DVRPTW considers in addition time windows, which are able to better capture real-world situations. Despite this, so far, few studies have focused on tackling this problem of greater practical importance. To this end, this study devises for the resolution of DVRPTW, an ant colony optimization based algorithm, which resorts to a joint solution construction mechanism, able to construct in parallel the vehicle routes. This method is coupled with a local search procedure, aimed to further improve the solutions built by ants, and with an insertion heuristics, which tries to reduce the number of vehicles used to service the available customers. The experiments indicate that the proposed algorithm is competitive and effective, and on DVRPTW instances with a higher dynamicity level, it is able to yield better results compared to existing ant-based approaches.