An Adapted Multi-Objective Genetic Algorithm for Healthcare Supplier Selection Decision

Marwa F. Mohamed, Mohamed Meselhy Eltoukhy, Khalil Al Ruqeishi () and Ahmad Salah
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Marwa F. Mohamed: Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt
Mohamed Meselhy Eltoukhy: Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt
Khalil Al Ruqeishi: Mathematical and Physical Sciences Department, College of Arts and Sciences, University of Nizwa, P.O. Box 33, Nizwa 616, Oman
Ahmad Salah: Information Technology Department, College of Computing and Information Science, University of Technology and Applied Sciences, P.O. Box 75, Nizwa 612, Oman

Mathematics, 2023, vol. 11, issue 6, 1-14

Abstract: With the advancement of information technology and economic globalization, the problem of supplier selection is gaining in popularity. The impact of supplier selection decisions made were quick and noteworthy on the healthcare profitability and total cost of medical equipment. Thus, there is an urgent need for decision support systems that address the optimal healthcare supplier selection problem, as this problem is addressed by a limited number of studies. Those studies addressed this problem mathematically or by using meta-heuristics methods. The focus of this work is to advance the meta-heuristics methods by considering more objectives rather than the utilized objectives. In this context, the optimal supplier selection problem for healthcare equipment was formulated as a mathematical model to expose the required objectives and constraints for the sake of searching for the optimal suppliers. Subsequently, the problem is realized as a multi-objective problem, with the help of this proposed model. The model has three minimization objectives: (1) transportation cost; (2) delivery time; and (3) the number of damaged items. The proposed system includes realistic constraints such as device quality, usability, and service quality. The model also takes into account capacity limits for each supplier. Next, it is proposed to adapt the well-known non-dominated sorting genetic algorithm (NSGA)-III algorithm to choose the optimal suppliers. The results of the adapted NSGA-III have been compared with several heuristic algorithms and two meta-heuristic algorithms (i.e., particle swarm optimization and NSGA-II). The obtained results show that the adapted NSGA-III outperformed the methods of comparison.

Keywords: decision support system; healthcare; logistics services; meta-heuristics; NSGA-III; supplier selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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