Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows

TL;DR

This paper introduces a novel Roulette Wheel-based Particle Swarm Optimization algorithm to effectively solve the Vehicle Routing Problem with Time Windows, reducing premature convergence and demonstrating competitive performance on benchmark datasets.

Contribution

The paper proposes a new RWPSO algorithm that enhances PSO for VRPTW by incorporating roulette wheel selection, improving convergence and solution quality.

Findings

RWPSO performs competitively on Solomon VRPTW benchmarks.

The method reduces premature convergence in PSO algorithms.

RWPSO outperforms some existing algorithms in solution quality.

Abstract

The well-known Vehicle Routing Problem with Time Windows (VRPTW) aims to reduce the cost of moving goods between several destinations while accommodating constraints like set time windows for certain locations and vehicle capacity. Applications of the VRPTW problem in the real world include Supply Chain Management (SCM) and logistic dispatching, both of which are crucial to the economy and are expanding quickly as work habits change. Therefore, to solve the VRPTW problem, metaheuristic algorithms i.e. Particle Swarm Optimization (PSO) have been found to work effectively, however, they can experience premature convergence. To lower the risk of PSO's premature convergence, the authors have solved VRPTW in this paper utilising a novel form of the PSO methodology that uses the Roulette Wheel Method (RWPSO). Computing experiments using the Solomon VRPTW benchmark datasets on the RWPSO demonstrate that RWPSO is competitive with other state-of-the-art algorithms from the literature. Also, comparisons with two cutting-edge algorithms from the literature show how competitive the suggested algorithm is.