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

Solving urban transit route design problem using selection hyper-heuristics

Ahmed, Leena, Mumford, Christine and Kheiri, Ahmed 2019. Solving urban transit route design problem using selection hyper-heuristics. European Journal of Operational Research 247 (2) , pp. 545-559. 10.1016/j.ejor.2018.10.022

[img]
Preview
PDF - Accepted Post-Print Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (401kB) | Preview

Abstract

The urban transit routing problem (UTRP) focuses on finding efficient travelling routes for vehicles in a public transportation system. It is one of the most significant problems faced by transit planners and city authorities throughout the world. This problem belongs to the class of difficult combinatorial problems, whose optimal solution is hard to find with the complexity that arises from the large search space, and the number of constraints imposed in constructing the solution. Hyper-heuristics have emerged as general-purpose search techniques that explore the space of low level heuristics to improve a given solution under an iterative framework. In this work, we evaluate the performance of a set of selection hyper-heuristics on the route design problem of bus networks, with the goal of minimising the passengers’ travel time, and the operator’s costs. Each selection hyper-heuristic is empirically tested on a set of benchmark instances and statistically compared to the other selection hyper-heuristics to determine the best approach. A sequence-based selection method combined with the great deluge acceptance method achieved the best performance, succeeding in finding improved results in much faster run times over the current best known solutions.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 0377-2217
Date of First Compliant Deposit: 24 October 2018
Date of Acceptance: 10 October 2018
Last Modified: 17 Oct 2020 02:05
URI: http://orca.cf.ac.uk/id/eprint/116137

Citation Data

Cited 6 times in Google Scholar. View in Google Scholar

Cited 1 time 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