| Abstract |
The lack of proper management of medical waste leads to environmental pollution and threatens the lives of communities. Good medical waste management means appropriate planning and monitoring at all stages, including collection, separation, treatment, recycling, and disposal which promotes a circular approach. This paper presents a multi-item, multi-period, and bi-objective mixed-integer linear programming circular economy transition model for medical waste management considering the uncertainty in the amount of waste generated to design a green reverse network. In this paper, for the first time, the concept of queuing theory is applied to manage the waiting time of trucks carrying infectious waste in treatment centers. In addition, the proposed model simultaneously addresses the centers' location and heterogeneous vehicles routing, and uses a stochastic scenario-based approach to deal with uncertainty of the waste generated. The purpose of the proposed model is to minimize total cost and population risk, and for this end, to employ an improved augmented epsilon-constraint method (AUGMECON2). Finally, the efficiency of the proposed model and solution approach is examined using the data of a case study in Alborz province that includes six hospitals, three potential collection centers, three potential treatment centers, four potential recycling centers, and three potential disposal centers. |
