Multiobjective charged system search for optimum location of bank branch

Talatahari, S; Ranjbar, A; Tolouei, M; Rahimi, I

Multiobjective charged system search for optimum location of bank branch

Talatahari, S Ranjbar, A Tolouei, M Rahimi, I

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Publisher:: Elsevier
Publication Type:: Chapter
Citation:: Multi-Objective Combinatorial Optimization Problems and Solution Methods, 2022, pp. 119-133
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Talatahari, S
dc.contributor.author	Ranjbar, A
dc.contributor.author	Tolouei, M
dc.contributor.author	Rahimi, I
dc.date.accessioned	2023-04-06T20:24:23Z
dc.date.available	2023-04-06T20:24:23Z
dc.date.issued	2022
dc.identifier.citation	Multi-Objective Combinatorial Optimization Problems and Solution Methods, 2022, pp. 119-133
dc.identifier.isbn	978-0-12-823799-1
dc.identifier.uri	http://hdl.handle.net/10453/169371
dc.description.abstract	This chapter focuses on determining the location of bank branches under competitive conditions considering different levels of customer attraction. Finding an optimum location for bank branches depends on many criteria and such problems are known as NP-hard problems. From marketing point of view, the optimum location of a new branch should be far enough from other branches. Also, the sum of distances between all customers and the new branch should be minimized. In this chapter, Multiobjective Charged System Search is applied for finding the optimum location of bank branches with conflicting purposes. The algorithm has capability of finding the optimum location for new branches under competitive conditions with conflicting purposes. To fulfill this purpose, a part of Tabriz city is selected for implementation. The results of the proposed optimization algorithm are compared with other well-known algorithms. The evaluation reveals that the presented algorithm is able to achieve many of the solutions on the Pareto Front; furthermore, the distribution of the solutions on the Pareto Front and their convergence has been better than Multiobjective Particle Swarm Optimization and Nondominated Sorting Genetic Algorithm II.
dc.format.extent	13
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Multi-Objective Combinatorial Optimization Problems and Solution Methods
dc.relation.isbasedon	10.1016/b978-0-12-823799-1.00013-9
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Multiobjective charged system search for optimum location of bank branch
dc.type	Chapter
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-04-06T20:24:22Z
pubs.place-of-publication	The Netherlands
pubs.publication-status	Published
dc.location	The Netherlands

Abstract:

This chapter focuses on determining the location of bank branches under competitive conditions considering different levels of customer attraction. Finding an optimum location for bank branches depends on many criteria and such problems are known as NP-hard problems. From marketing point of view, the optimum location of a new branch should be far enough from other branches. Also, the sum of distances between all customers and the new branch should be minimized. In this chapter, Multiobjective Charged System Search is applied for finding the optimum location of bank branches with conflicting purposes. The algorithm has capability of finding the optimum location for new branches under competitive conditions with conflicting purposes. To fulfill this purpose, a part of Tabriz city is selected for implementation. The results of the proposed optimization algorithm are compared with other well-known algorithms. The evaluation reveals that the presented algorithm is able to achieve many of the solutions on the Pareto Front; furthermore, the distribution of the solutions on the Pareto Front and their convergence has been better than Multiobjective Particle Swarm Optimization and Nondominated Sorting Genetic Algorithm II.

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http://hdl.handle.net/10453/169371