Volume 21, Issue 3 (9-2024)
jor 2024, 21(3): 81-97 |
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Sahabi A, Nasseri S H, Paydar M M. Study of a Mathematical Model for the Hospital Waste Management Problem. jor 2024; 21 (3) :81-97
URL: http://jamlu.liau.ac.ir/article-1-2273-en.html
URL: http://jamlu.liau.ac.ir/article-1-2273-en.html
Department of Applied Mathematics, University of Mazandaran, Babolsar, Iran , alisahabi1398@gmail.com
Abstract: (767 Views)
In recent years, the mass production of waste has become one of the significant challenges of today’s world. Among the various types of waste, hospital waste poses a particularly dangerous risk, and its improper management leads to environmental pollution and consequently adverse effects on human life. Therefore, selecting and utilizing appropriate technologies for hospital waste management is of utmost importance. Although numerous studies have been conducted in recent years regarding hospital waste management, there has been little attention paid to the technologies employed in our country. Thus, this research aims to study a mathematical model for the issue of hospital waste management. The hospital waste management problem is modeled as a linear programming problem with economic, social, and environmental objectives. One of the conditions for utilizing this mathematical modeling is having accurate and precise information, which, given the nature of real-world issues, is practically challenging and somewhat imprecise to obtain. Therefore, this study proposes the adoption of one of the approaches to decision-making under uncertainty, such as fuzzy, gray, interval, or stochastic methods. In this research, the fuzzy parameters of the model are considered in two sections: objective coefficients and available resources (capacity of facilities) to enhance the model. To solve this problem, the goal programming method will be used in conjunction with triangular fuzzy numbers. To better describe the model and results, a case study of hospital waste is presented.
Keywords: Hospital Waste Management, Mathematical Programming, Multi-Objective Optimization, Goal Programming, Uncertainty
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