Generalized OWA operators for uncertain queuing modeling with application in healthcare

• Published in: Soft computing (Berlin, Germany) Germany), 2021-03, Vol.25 (6), p.4951-4962
• Main Authors: Ahmad, Shafiq, Alnowibet, Khalid, Alqasem, Latifah, Merigo, Jose M., Zaindin, Mazen
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Bibliometrics; Computational Intelligence; Control; Customer satisfaction; Customer services; Decision making; Emergency medical care; Emergency medical services; Emergency procedures; Engineering; Health care; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Modelling; Operating costs; Operators; Queuing; Queuing theory; Robotics; Uncertainty
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-020-05507-1

The weighted averaging operators are one of the popular methods for aggregating information. In recent years, ordered weighted averaging operators (OWA) have attained a great attention by researchers. These OWA operators due to their versatility are very useful to model many real world situations. Several extensions of OWA operators are presented in the literature which can handle a situation with uncertainty. Although many queuing models have been proposed in numerous healthcare studies, the inclusion of OWA operators is still rare. In this research study, we propose a novel method using the uncertain generalized ordered weighted average and illustrate its application to the uncertain queue modeling in a hospital emergency room; where incoming flux of patients and the required level of service for each patient is unknown and uncertain. The model with multilateral decision making process has been described which will provide several alternatives to decision makers to select the best alternative for their challenging situations. The proposed method has resulted an improved performance of the queuing system, increased customer satisfaction as well as a significant reduction in the operational cost. This study will enable decision makers to operate a flexible and cost-effective system in the event of uncertainty, uncontrollable and unpredicted situations.