MIXED VARIABLE OPTIMIZATION MODEL FOR MEDICAL WASTE REVERSE LOGISTICS NETWORKS USING HYBRID CELLULAR GENETIC ALGORITHM

Abstract

The medical waste reverse logistics networks are an attractive topic as the possible public health dangers and ecological ventures for burning a large quantity of medical waste. Reverse logistics networks structures were created for reducing medical waste produced from hospitals for recovery. In this research, a Mixed Variable Optimization (MVO) model is presented for minimizing costs of medical waste reverse logistics networks. The total costs for reverse logistics contain stable cost of opening the gathering stations and treating stations, shipped cost and processing cost, finally a schedule is proposed of flows of medical 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 CGAMV, 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 presented model is examined on a numerical experiment and it is found promising.

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Assiut University Journal of Multidisciplinary Scientific Research
Volume 47, Issue 2
December 2018
Pages 42-57

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