Dynamic Bi-Objective Routing of Multiple Vehicles

TL;DR

This paper introduces a dynamic bi-objective vehicle routing approach that adapts to real-time customer requests, optimizing route efficiency and customer coverage simultaneously using an extended evolutionary algorithm.

Contribution

It extends a dynamic evolutionary multi-objective algorithm to handle multiple vehicles in real-time routing scenarios, addressing the complexity of sequential decision making and irreversible routes.

Findings

DEMOA performs competitively against offline clairvoyant methods.

The approach effectively balances route length and customer coverage.

It outperforms previous single-vehicle dynamic routing methods.

Abstract

In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.