Network Model and Optimization of Medical Waste Reverse Logistics by Improved Genetic Algorithm

Authors:
Lihong Shi;Houming Fan;Pingquan Gao;Hanyu Zhang
Affiliations:
Transportation Management College, Dalian Maritime University, Dalian, China 116026;Transportation Management College, Dalian Maritime University, Dalian, China 116026;Transportation Management College, Dalian Maritime University, Dalian, China 116026;Transportation Management College, Dalian Maritime University, Dalian, China 116026
Venue:
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Year:
2009

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Computers and Industrial Engineering - Special issue on o/perational issues in environmentally conscious manufacturing
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms

Genetic Algorithms

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Abstract

The medical waste management is of great importance because of the potential environmental hazards and public health risks. Manufacturers have to collect the medical waste and control its recovery or disposal. Medical waste recovery, which encompasses reusing, remanufacturing and materials recycling, requires a specially structured reverse logistics network in order to collect the medical waste efficiently. This paper presents a Mixed Integer Linear Programming model with minimizing costs for medical waste reverse logistics networks. The total costs for reverse logistics include transportation cost, fixed cost of opening the collecting centers and processing centers and operation cost at these facilities over finite planning horizons. An improved genetic algorithm method with a hybrid encoding rule is used to solve the proposed model. The efficiency and practicability of the proposed model is validated by an application to an illustrative example dealing with medical waste returned from some hospitals to a given manufacture.