A Hybrid Cellular Genetic Algorithm Based Mixed Variable Optimization Model for Hospital Waste Closed Loop Supply Chain Loop Reverse logistic Networks | ||
| النشرة المعلوماتية في الحاسبات والمعلومات | ||
| Article 1, Volume 4, Issue 2 - Serial Number 20220402, July 2022, Pages 7-27 PDF (1.23 M) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/fcihib.2022.123469.1068 | ||
| Author | ||
| الاء فهيم* | ||
| قسم الرياضيات کلية العلوم جامعة اسيوط | ||
| Abstract | ||
| Design of hospitals waste supply chain network considering both forward and reverse flows has been increasing day by day due to resource constraints, increased costs and the importance of utilizing returned waste. This paper attempts to integrate both forward and reverse logistics to design a general Closed Loop Supply Chain Reverse Logistics (CLSCRL) network. In this paper, a Mixed Variable Optimization (MVO) model is created for minimizing costs of hospital waste CLSCRL networks. The total costs for CLSCRL optimize cost of opening the gathering stations, treating stations, distribution stations and received hospitals, shipped cost and processing cost. Finally, a schedule is proposed of flows of hospital waste in the network. A hybrid Cellular Genetic Algorithm (CGA) is used for solving the MVO model to be (CGAMV). Pattern Search Method (PSM) is added to the proposed method, to be (CGAMV P) to make more intensification on the best solutions in proposed grid. The grid structure and small neighborhood make fast convergence and exploration during genetic algorithm operators. The performance of the CGAMV P algorithm by the proposed model is examined on a numerical experiment and it shows that the designed model has successfully optimized the location of facilities and network flows.The efficiecy of the proposed model validate to deal with medical waste in hospitals and it could be applied on other manufacture. The result indicates that, the proposed model is applicable. | ||
| Keywords | ||
| cellular genetic algorithm; hospital waste; mixed integer programming | ||
| References | ||
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