Article a novel algorithm for capacitated vehicle routing problem for smart cities

Sajid, M; Singh, J; Haidri, RA; Prasad, M; Varadarajan, V; Kotecha, K; Garg, D

Article a novel algorithm for capacitated vehicle routing problem for smart cities

Sajid, M Singh, J Haidri, RA Prasad, M

Varadarajan, V Kotecha, K Garg, D

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Symmetry, 2021, 13, (10)
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Sajid, M
dc.contributor.author	Singh, J
dc.contributor.author	Haidri, RA
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.contributor.author	Varadarajan, V
dc.contributor.author	Kotecha, K
dc.contributor.author	Garg, D
dc.date.accessioned	2022-03-17T21:42:53Z
dc.date.available	2022-03-17T21:42:53Z
dc.date.issued	2021-10-01
dc.identifier.citation	Symmetry, 2021, 13, (10)
dc.identifier.issn	2073-8994
dc.identifier.issn	2073-8994
dc.identifier.uri	http://hdl.handle.net/10453/155302
dc.description.abstract	Smart logistics is an indispensable building block in smart cities development that requires solving the challenge of efficiently serving the demands of geographically distributed customers by a fleet of vehicles. It consists of a very well-known NP-hard complex optimization problem, which is known as the capacitated vehicle routing problem (CVRP). The CVRP has widespread real-life applications such as delivery in smart logistics, the pharmaceutical distribution of vacancies, disaster relief efforts, and others. In this work, a novel giant tour best cost crossover (GTBCX) operator is proposed which works stochastically to search for the optimal solutions of the CVRP. An NSGA-II-based routing algorithm employing GTBCX is also proposed to solve the CVRP to minimize the total distance traveled as well as to minimize the longest route length. The simulated study is performed on 88 benchmark CVRP instances to validate the success of our proposed GTBCX operator against the nearest neighbor crossover (NNX) and edge assembly crossover (EAX) operators. The rigorous simulation study shows that the GTBCX is a powerful operator and helps to find results that are superior in terms of the overall distance traveled, length of the longest route, quality, and number of Pareto solutions. This work employs a multi-objective optimization algorithm to solve the capacitated vehicle routing problem (CVRP), where the CVRP is represented in the form of a two-dimensional graph. To compute the values’ objective functions, the distance between two nodes in the graph is considered symmetric. This indicates that the genetic algorithm complex optimization algorithm is employed to solve CVRP, which is a symmetry distance-based graph.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Symmetry
dc.relation.isbasedon	10.3390/sym13101923
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Article a novel algorithm for capacitated vehicle routing problem for smart cities
dc.type	Journal Article
utslib.citation.volume	13
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2022-03-17T21:42:49Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	10

Abstract:

Smart logistics is an indispensable building block in smart cities development that requires solving the challenge of efficiently serving the demands of geographically distributed customers by a fleet of vehicles. It consists of a very well-known NP-hard complex optimization problem, which is known as the capacitated vehicle routing problem (CVRP). The CVRP has widespread real-life applications such as delivery in smart logistics, the pharmaceutical distribution of vacancies, disaster relief efforts, and others. In this work, a novel giant tour best cost crossover (GTBCX) operator is proposed which works stochastically to search for the optimal solutions of the CVRP. An NSGA-II-based routing algorithm employing GTBCX is also proposed to solve the CVRP to minimize the total distance traveled as well as to minimize the longest route length. The simulated study is performed on 88 benchmark CVRP instances to validate the success of our proposed GTBCX operator against the nearest neighbor crossover (NNX) and edge assembly crossover (EAX) operators. The rigorous simulation study shows that the GTBCX is a powerful operator and helps to find results that are superior in terms of the overall distance traveled, length of the longest route, quality, and number of Pareto solutions. This work employs a multi-objective optimization algorithm to solve the capacitated vehicle routing problem (CVRP), where the CVRP is represented in the form of a two-dimensional graph. To compute the values’ objective functions, the distance between two nodes in the graph is considered symmetric. This indicates that the genetic algorithm complex optimization algorithm is employed to solve CVRP, which is a symmetry distance-based graph.

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http://hdl.handle.net/10453/155302