Optimization of reverse logistics network for medical waste recycling

The occurrence of a major medical event usually leads to a surge in the generation of medical waste. If the waste generated is not disposed of in a timely manner, it can pose a great threat to humans and the surrounding environment. Based on the large volume and hazardous nature of medical waste, this research explores the social value and importance of establishing a professional network for medical waste reverse logistics. A three-tier recycling network model consisting of recycling centres, testing and processing centres, reprocessing centres, power plants and landfill plants has been developed based on the special characteristics of medical waste. A mixed integer linear programming (MILP) model was developed with the goal of minimizing costs. Genetic algorithm, MILP solver and Lingo solver are used to solve the problem of node selection and waste recycling volume allocation in the network. Several datasets were utilized to verify the effectiveness, demonstrating better solutions for problems of varying sizes. The results of the case study show that genetic algorithm can show good results in solving problems of different sizes. The solver's ability to solve the problem decreases as it grows in size. By comparing the methods used, choosing an efficient and accurate approach to the reverse logistics recovery problem has become more achievable. The results of the case study show the stability of the reverse logistics optimization model at different scales. The construction of the reverse logistics network optimization model resulted in lower total recycling costs and higher recycling efficiency. Finally, some insights are given based on the obtained results.