Appointment scheduling problem under fairness policy in healthcare services: fuzzy ant lion optimizer

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210en_US
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874en_US
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorAla, Ali
dc.contributor.authorSimic, Vladimir
dc.contributor.authorPamucar, Dragan
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2022-07-05T14:08:39Z
dc.date.available2022-07-05T14:08:39Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractThis study addresses the application of the Integer Linear Programming technique for the patient Appointment Scheduling Problem (ASP). In this research, we propose a Mixed-Integer Linear Programming (MILP) model to formulate the problem and treat patients admitted to hospitals and stay in a queue based on their general health status (urgent or regular patients). Moreover, the ASP has two main objectives that often provide early patient admissions. The first objective is based on fairness policy as an essential factor in the healthcare service to help minimize patient waiting time. The second one is to maximize the efficiency of healthcare services in line with patients’ satisfaction. Moreover, we have addressed the Fuzzy Ant Lion Optimization (FALO) strategy and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are utilized to compare and solve the resulting multi-objective ASP. As the application of the model, fairness policy is analyzed in scenario 1 using FALO, and in scenario 2, NSGA-II is applied. The performances of the solution algorithms are then tested using datasets of a big regional hospital in Shanghai. The outcomes indicate potential advantages of implementing the presented approach. In particular, the suggested FALO increases the fairness and patients’ satisfaction by more than 80% while reducing the waiting times by 50% within the basic appointment scheduling system. © 2022 Elsevier Ltden_US
dc.identifier.citationAla, A., Simic, V., Pamucar, D., & Tirkolaee, E. B. (2022). Appointment scheduling problem under fairness policy in healthcare services: Fuzzy ant lion optimizer. Expert Systems with Applications, 207 doi:10.1016/j.eswa.2022.117949en_US
dc.identifier.doi10.1016/j.eswa.2022.117949en_US
dc.identifier.issn0957-4174en_US
dc.identifier.scopus2-s2.0-85132920395en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2022.117949
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2964
dc.identifier.volume207en_US
dc.identifier.wosWOS:000828194400003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAppointment Schedulingen_US
dc.subjectFairness Policyen_US
dc.subjectFuzzy Ant Lion Optimizationen_US
dc.subjectFuzzy Seten_US
dc.subjectHealthcare Optimizationen_US
dc.subjectNSGA-IIen_US
dc.titleAppointment scheduling problem under fairness policy in healthcare services: fuzzy ant lion optimizeren_US
dc.typeArticleen_US

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