Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Investigating the use of metaheuristics for solving single vehicle routing problems with time-varying traversal costs

Harwood, Kieran G., Mumford, Christine Lesley and Eglese, R. 2012. Investigating the use of metaheuristics for solving single vehicle routing problems with time-varying traversal costs. Journal of the Operational Research Society , n/a. 10.1057/jors.2012.17

[img]
Preview
Text - Accepted Post-Print Version
Download (378kB) | Preview

Abstract

Metaheuristic algorithms, such as simulated annealing and tabu search, are popular solution techniques for vehicle routing problems (VRPs). These approaches rely on iterative improvements to a starting solution, involving slight alterations to the routes (i.e. neighbourhood moves), moving a node to a diff�erent part of a solution, swapping nodes or inverting sections of a tour, for example. When working with standard VRPs, where the costs of the arcs do not vary with advancing time, evaluating changes to the total cost following a neighbourhood move is a simple process: simply subtract the costs of the links removed from the solution and add the costs for the new links. When a time varying aspect (e.g. congestion) is included in the costs, these calculations become estimations rather than exact values. This paper focuses on a single vehicle VRP, similar to the Travelling Salesman Problem (TSP) and investigates the potential for using estimation methods on simple models with time dependent costs, mimicking the e�ects of road congestion.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Additional Information: Advanced online publication
Publisher: Palgrave
ISSN: 0160-5682
Date of First Compliant Deposit: 30 March 2016
Last Modified: 04 Jun 2017 04:03
URI: http://orca.cf.ac.uk/id/eprint/31844

Citation Data

Cited 8 times in Google Scholar. View in Google Scholar

Cited 9 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics