Operating room planning for emergency surgery: optimization in multiobjective modeling and management from the latest developments in computational intelligence techniques
| dc.authorid | 0000-0003-0804-2400 | |
| dc.contributor.author | Li, Qiqian | |
| dc.contributor.author | Liu, Yali | |
| dc.contributor.author | Sipahi Döngül, Esra | |
| dc.contributor.author | Yang, Yufen | |
| dc.contributor.author | Ruan, Xiaoyuan | |
| dc.contributor.author | Enbeyle, Wegayehu | |
| dc.date.accessioned | 2022-04-14T05:07:21Z | |
| dc.date.available | 2022-04-14T05:07:21Z | |
| dc.date.issued | 2022 | |
| dc.department | Sağlık Bilimleri Fakültesi | |
| dc.description.abstract | This study presents an optimization approach for scheduling the operation room for emergency surgeries, considering the priority of surgeries. This optimization model aims to minimize the costs associated with elective and emergency surgeries and maximize the number of scheduled surgeries. In this study, surgeon assistants to perform each surgery are considered in order to achieve the goals. Since the time of each surgery varies according to the conditions of the patient, this parameter is considered as an uncertain one, and a robust optimization method is applied to deal with uncertainty. To demonstrate the effectiveness of the proposed method, a case study in one of the East Asian hospitals is presented and analyzed using GAMS software. Moreover, hybrid simulation and gray wolf optimization algorithm (GWO) have been implemented to solve the optimization model in different scenarios. The results show that increasing the risk parameters in the robust optimization model will increase the system costs. Moreover, in case of uncertainty, the solutions obtained from the GWO simulation method are on average 73.75% better than the solutions obtained from the GWO algorithm. | |
| dc.identifier.doi | 10.1155/2022/2290644 | |
| dc.identifier.endpage | - | en_US |
| dc.identifier.issue | - | en_US |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.startpage | - | en_US |
| dc.identifier.uri | https:/dx.doi.org/10.1155/2022/2290644 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/9284 | |
| dc.identifier.volume | - | en_US |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.indekslendigikaynak | PubMed | |
| dc.language.iso | en | |
| dc.publisher | Hindawi Limited | |
| dc.relation.ispartof | Computational Intelligence and Neuroscience | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Operating Room Planning | |
| dc.subject | Emergency Surgery | |
| dc.title | Operating room planning for emergency surgery: optimization in multiobjective modeling and management from the latest developments in computational intelligence techniques | |
| dc.type | Article |
