{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T22:48:24Z","timestamp":1774046904412,"version":"3.50.1"},"reference-count":50,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BPMJ"],"published-print":{"date-parts":[[2023,5,9]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Outpatient care delivery is one of the key revenue sources of a hospital which plays a salient role in timely care delivery. The key purpose of the study is to propose a multi-objective simulation-based decision support model that considers the cost of care delivery and patient dissatisfaction as its two key conflicting objectives. Patient dissatisfaction considers service fairness. Patient idiosyncrasies such as no-show, unpunctuality and balking have been considered in the model involving multiple classes of patients.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>A model has been designed using data collected from field investigations. In the first stage, queuing theory based discrete event simulation model has been developed. Genetic algorithm has been used to solve the scalarized problem and obtain actionable insights. In the second stage, non-dominated sorting genetic algorithm II (NSGA-II) has been involved to achieve the Pareto optimal fronts considering equal priority of the two objectives.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The computational results considering various parameter settings can help in efficient resource planning while ensuring better care delivery. The model proposed in the study provides structural insights on the business strategy of healthcare service providers on optimizing the dual goals of care delivery cost and service fairness.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The study is one of the early works that helps to improve the care delivery process by taking into consideration the environmental factors as well as service fairness. The study demonstrates the usage of simulation-based multi-objective optimization to provide a more sustainable patient centric care delivery.<\/jats:p><\/jats:sec>","DOI":"10.1108\/bpmj-07-2022-0335","type":"journal-article","created":{"date-parts":[[2023,2,16]],"date-time":"2023-02-16T01:02:44Z","timestamp":1676509364000},"page":"630-652","source":"Crossref","is-referenced-by-count":12,"title":["A multi-objective optimization model for outpatient care delivery\u00a0with service fairness"],"prefix":"10.1108","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3910-745X","authenticated-orcid":false,"given":"Soumyajyoti","family":"Datta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohit","family":"Kapoor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2163-7567","authenticated-orcid":false,"given":"Peeyush","family":"Mehta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2023,2,17]]},"reference":[{"key":"key2023050606272571700_ref001","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.ejor.2016.06.064","article-title":"Outpatient appointment systems in healthcare: a\u00a0review of optimization studies","volume":"258","year":"2017","journal-title":"European Journal of Operational Research"},{"key":"key2023050606272571700_ref002","article-title":"A modified genetic algorithm for non-emergency outpatient appointment scheduling with highly demanded medical services considering patient priorities","volume":"139","year":"2020","journal-title":"Computers and Industrial Engineering"},{"key":"key2023050606272571700_ref003","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1108\/BPMJ-11-2019-0457","article-title":"Enhancing patient flow in emergency department (ED) using lean strategies\u2013an integrated voice of customer and voice of process perspective","volume":"27","year":"2021","journal-title":"Business Process Management Journal"},{"key":"key2023050606272571700_ref004","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1051\/ro\/2014041","article-title":"Modelling and optimization of outpatient appointment scheduling","volume":"49","year":"2015","journal-title":"RAIRO - Operations Research"},{"key":"key2023050606272571700_ref005","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1080\/21548331.2021.1942878","article-title":"Perceived characteristics of outpatient appointment scheduling association with patient satisfaction and treatment adherence: an innovation theory application","volume":"49","year":"2021","journal-title":"Hospital Practice"},{"key":"key2023050606272571700_ref006","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1111\/j.2517-6161.1952.tb00112.x","article-title":"A study of queues and appointment systems in hospital out-patient departments, with special reference to waiting-times","volume":"14","year":"1952","journal-title":"Journal of the Royal Statistical Society. 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