Genetic Algorithms for multiple objective vehicle routing

TL;DR

This paper presents a genetic algorithm tailored for multi-objective vehicle routing problems with time windows, offering a flexible, user-friendly software tool that supports decision-making and has demonstrated competitive performance.

Contribution

It introduces a novel genetic algorithm with a specific dominance relation for multi-objective optimization, implemented in a versatile software system for vehicle routing.

Findings

Successfully solved vehicle routing problems with multiple objectives

Software demonstrated at European Academic Software Award

Algorithm effectively handles convex-dominated alternatives

Abstract

The talk describes a general approach of a genetic algorithm for multiple objective optimization problems. A particular dominance relation between the individuals of the population is used to define a fitness operator, enabling the genetic algorithm to adress even problems with efficient, but convex-dominated alternatives. The algorithm is implemented in a multilingual computer program, solving vehicle routing problems with time windows under multiple objectives. The graphical user interface of the program shows the progress of the genetic algorithm and the main parameters of the approach can be easily modified. In addition to that, the program provides powerful decision support to the decision maker. The software has proved it's excellence at the finals of the European Academic Software Award EASA, held at the Keble college/ University of Oxford/ Great Britain.