Developing an imperialist competitive algorithm based on two improvement strategies in a hierarchical capacitated health network.
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| Title: | Developing an imperialist competitive algorithm based on two improvement strategies in a hierarchical capacitated health network. |
|---|---|
| Authors: | Khanduzi, Raheleh1 (AUTHOR) khanduzi@gonbad.ac.ir, Sadati, Mir Ehsan Hesam2,3 (AUTHOR) msadati@sabanciuniv.edu |
| Source: | Neural Computing & Applications. Apr2025, Vol. 37 Issue 12, p7947-7970. 24p. |
| Subjects: | Imperialist competitive algorithm, Applied mathematics, Health facilities, NP-hard problems, Linear programming |
| Abstract: | The present paper on the location of clinic (C), hospital (H) and medical center (MC) in the Golestan province of Iran is motivated by its present condition coming from limited distribution and ease of access for related Cs, Hs, and MCs. Design of a median hierarchical location-allocation model for the needed healthcare facilities, from Cs to Hs and MCs, is a vital and valuable activity from the emergency viewpoints of both patients and the government. This model has been formulated as a mixed-integer linear mathematical framework for finding the optimal location of these capacitated healthcare facilities, the allocation of patients to these Cs, Hs, or MCs and also for the referrals of the patients' needs to them while minimizing the total demand-weighted travel distance. This problem is in the category of an NP-hard problem. An efficient and robust imperialist competitive algorithm based on two initialization and local mechanisms is also presented to improve the computational time and accuracy of simulation results. Comparative performance of the developed method with some well-known metaheuristics has been surveyed using a real case study for the healthcare network for different problems with a change in the model parameters' values. The novel method is reliable and valid according to accuracy and execution time. The sensitivity analysis results concerning the maximum number of locations (i.e., Cs, Hs and MCs). Furthermore, the percent of the referred demand determines the significance and practical observations related to the combination of the Cs, Hs, and MCs to be established. Our new model is illustrated to be gainful as it offers a robust build plan to designers for making location decisions for developing the Golestan healthcare network. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 184301964 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Developing an imperialist competitive algorithm based on two improvement strategies in a hierarchical capacitated health network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Khanduzi%2C+Raheleh%22">Khanduzi, Raheleh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> khanduzi@gonbad.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Sadati%2C+Mir+Ehsan+Hesam%22">Sadati, Mir Ehsan Hesam</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> msadati@sabanciuniv.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Apr2025, Vol. 37 Issue 12, p7947-7970. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Imperialist+competitive+algorithm%22">Imperialist competitive algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Applied+mathematics%22">Applied mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Health+facilities%22">Health facilities</searchLink><br /><searchLink fieldCode="DE" term="%22NP-hard+problems%22">NP-hard problems</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+programming%22">Linear programming</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The present paper on the location of clinic (C), hospital (H) and medical center (MC) in the Golestan province of Iran is motivated by its present condition coming from limited distribution and ease of access for related Cs, Hs, and MCs. Design of a median hierarchical location-allocation model for the needed healthcare facilities, from Cs to Hs and MCs, is a vital and valuable activity from the emergency viewpoints of both patients and the government. This model has been formulated as a mixed-integer linear mathematical framework for finding the optimal location of these capacitated healthcare facilities, the allocation of patients to these Cs, Hs, or MCs and also for the referrals of the patients' needs to them while minimizing the total demand-weighted travel distance. This problem is in the category of an NP-hard problem. An efficient and robust imperialist competitive algorithm based on two initialization and local mechanisms is also presented to improve the computational time and accuracy of simulation results. Comparative performance of the developed method with some well-known metaheuristics has been surveyed using a real case study for the healthcare network for different problems with a change in the model parameters' values. The novel method is reliable and valid according to accuracy and execution time. The sensitivity analysis results concerning the maximum number of locations (i.e., Cs, Hs and MCs). Furthermore, the percent of the referred demand determines the significance and practical observations related to the combination of the Cs, Hs, and MCs to be established. Our new model is illustrated to be gainful as it offers a robust build plan to designers for making location decisions for developing the Golestan healthcare network. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-024-10513-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 7947 Subjects: – SubjectFull: Imperialist competitive algorithm Type: general – SubjectFull: Applied mathematics Type: general – SubjectFull: Health facilities Type: general – SubjectFull: NP-hard problems Type: general – SubjectFull: Linear programming Type: general Titles: – TitleFull: Developing an imperialist competitive algorithm based on two improvement strategies in a hierarchical capacitated health network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Khanduzi, Raheleh – PersonEntity: Name: NameFull: Sadati, Mir Ehsan Hesam IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 37 – Type: issue Value: 12 Titles: – TitleFull: Neural Computing & Applications Type: main |
| ResultId | 1 |
