Dissertation/Thesis Abstract

A reverse logistics model for medical waste management
by Hejrani, Sasan, M.S., Southern Illinois University at Edwardsville, 2013, 89; 1552663
Abstract (Summary)

Due to their potential hazards to health and environment, collection and treatment of medical wastes have always been an important issue and a risky business. As soon as any object is considered as a medical waste, it must be promptly processed through strict procedures and closely monitored all the way from its inception through its disposal. This study proposes an innovative way to model the reverse logistics network for medical waste management. A mixed integer linear programming (MILP) model is developed to model the waste supply chain as a reverse logistics network. Analytic hierarchy process (AHP) method is used to calculate risk factors and subsequently a genetic algorithm is presented to obtain a solution that minimizes the costs and risks based on the tracking data. A simulation study is conducted to validate the proposed model and algorithm, and also determine the optimal parameters under various setups and scenarios in reasonable time. Numerical experiments are provided to evaluate the effectiveness of the proposed model and algorithm with different scenarios and different approaches. The experimental results indicate the impact of risks on the medical waste distribution is significant such that they can lead to a whole different solution, and therefore they must be carefully considered on future modeling attempts.

Indexing (document details)
Advisor: Ko, Hoo Sang
Commitee: Chen, Xin, Lee, H. Felix
School: Southern Illinois University at Edwardsville
Department: Mechanical and Industrial Engineering
School Location: United States -- Illinois
Source: MAI 52/05M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Industrial engineering, Health care management
Keywords: Genetic algorithm, Lp modeling, Reverse logistics, Rockwell arena, Simulation, Waste management
Publication Number: 1552663
ISBN: 9781303734298
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