Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows

The vehicle routing problem with time windows (VRPTW) is a non-deterministictime hard (NP-hard) with combinatorial optimization problem (COP). The Artificial Bee Colony (ABC) is a popular swarm intelligence algorithm for COP. In this study, existing Modified ABC (MABC) algorithm is revised to solve...

Full description

Saved in:

Bibliographic Details
Main Author:	Sankor, Salah Mortada Shahen
Format:	Thesis
Language:	eng eng
Published:	2022
Subjects:	QA Mathematics
Online Access:	https://etd.uum.edu.my/10246/1/s903364_01.pdf https://etd.uum.edu.my/10246/2/s903364_02.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uum-etd.10246
record_format	uketd_dc
spelling	my-uum-etd.102462023-01-25T00:37:19Z Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows 2022 Sankor, Salah Mortada Shahen Yusof, Yuhanis Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences QA Mathematics The vehicle routing problem with time windows (VRPTW) is a non-deterministictime hard (NP-hard) with combinatorial optimization problem (COP). The Artificial Bee Colony (ABC) is a popular swarm intelligence algorithm for COP. In this study, existing Modified ABC (MABC) algorithm is revised to solve the VRPTW. While MABC has been reported to be successful, it does have some drawbacks, including a lack of neighbourhood structure selection during the intensification process, a lack of knowledge in population initialization, and occasional stops proceeding the global optimum. This study proposes an enhanced Modified ABC (E-MABC) algorithm which includes (i) N-MABC that overcomes the shortage of neighborhood selection by exchanging the neighborhood structure between two different routes in the solution; (ii) MABC-ACS that solves the issues of knowledge absence in MABC population initialization by incorporating ant colony system heuristics, and (iii) PMABC which addresses the occasional stops proceeding to the global optimum by introducing perturbation that accepts an abandoned solution and jumps out of a local optimum. The proposed algorithm was evaluated using benchmark datasets comprising 56 VRPTW instances and 56 Pickup and Delivery Problems with Time Windows (PDPTW). The performance has been measured using the travelled distance (TD) and the number of deployed vehicles (NV). The results showed that the proposed E-MABC has lower TD and NV than the benchmarked MABC and other algorithms. The E-MABC algorithm is better than the MABC by 96.62%, MOLNS by 87.5%, GAPSO by 53.57%, MODLEM by 76.78%, and RRGA by 42.85% in terms of TD. Additionally, the E-MABC algorithm is better than the MABC by 42.85%, MOLNS by 17.85%, GA-PSO and RRGA by 28.57%, and MODLEN by 46.42% in terms of NV. This indicates that the proposed E-MABC algorithm is promising and effective for the VRPTW and PDPTW, and thus can compete in other routing problems and COPs. 2022 Thesis https://etd.uum.edu.my/10246/ https://etd.uum.edu.my/10246/1/s903364_01.pdf text eng 2025-06-07 staffonly https://etd.uum.edu.my/10246/2/s903364_02.pdf text eng public other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Yusof, Yuhanis
topic	QA Mathematics
spellingShingle	QA Mathematics Sankor, Salah Mortada Shahen Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows
description	The vehicle routing problem with time windows (VRPTW) is a non-deterministictime hard (NP-hard) with combinatorial optimization problem (COP). The Artificial Bee Colony (ABC) is a popular swarm intelligence algorithm for COP. In this study, existing Modified ABC (MABC) algorithm is revised to solve the VRPTW. While MABC has been reported to be successful, it does have some drawbacks, including a lack of neighbourhood structure selection during the intensification process, a lack of knowledge in population initialization, and occasional stops proceeding the global optimum. This study proposes an enhanced Modified ABC (E-MABC) algorithm which includes (i) N-MABC that overcomes the shortage of neighborhood selection by exchanging the neighborhood structure between two different routes in the solution; (ii) MABC-ACS that solves the issues of knowledge absence in MABC population initialization by incorporating ant colony system heuristics, and (iii) PMABC which addresses the occasional stops proceeding to the global optimum by introducing perturbation that accepts an abandoned solution and jumps out of a local optimum. The proposed algorithm was evaluated using benchmark datasets comprising 56 VRPTW instances and 56 Pickup and Delivery Problems with Time Windows (PDPTW). The performance has been measured using the travelled distance (TD) and the number of deployed vehicles (NV). The results showed that the proposed E-MABC has lower TD and NV than the benchmarked MABC and other algorithms. The E-MABC algorithm is better than the MABC by 96.62%, MOLNS by 87.5%, GAPSO by 53.57%, MODLEM by 76.78%, and RRGA by 42.85% in terms of TD. Additionally, the E-MABC algorithm is better than the MABC by 42.85%, MOLNS by 17.85%, GA-PSO and RRGA by 28.57%, and MODLEN by 46.42% in terms of NV. This indicates that the proposed E-MABC algorithm is promising and effective for the VRPTW and PDPTW, and thus can compete in other routing problems and COPs.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Sankor, Salah Mortada Shahen
author_facet	Sankor, Salah Mortada Shahen
author_sort	Sankor, Salah Mortada Shahen
title	Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows
title_short	Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows
title_full	Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows
title_fullStr	Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows
title_full_unstemmed	Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows
title_sort	enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2022
url	https://etd.uum.edu.my/10246/1/s903364_01.pdf https://etd.uum.edu.my/10246/2/s903364_02.pdf
_version_	1776103774275764224

Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows

Similar Items