چكيده به لاتين
Medical waste is one of the most diverse and hazardous waste that, if not managed, can create many hazards such as disease outbreak, infection, and damage to the environment. The improper management of these wastes can lead to severe dangers to humans’ health and the environment. The generated wastes must be collected properly from health-care centers on time and transported to related facilities for treatment. In this research, a sustainable vehicle routing problem for medical wastes collection is presented. Today, attaining sustainable development has become one of the most significant concerns of policymakers and societies and reserchers.In sustainable issues, an effort is made to balance economic, social, and environmental quality. In the waste collection vehicle routing problem literature, there is a research gap in considering all sustainable development goals. Collecting and minimizing the risk of these wastes at the source of generation has not been investigated by improving the collection process. In this research, aside from minimizing fixed and variable costs and minimizing vehicles fuel consumption as economic and environmental goals, a new objective function has been presented to decrease the risk of multiple kinds of hazardous waste as a social goal. This objective function determines the sequence of health-care centers visit in a way that service-giving to high-danger centers takes place sooner than less low-danger centers and the risk of public health is minimized. This is done by considering parameters such as the probability of disease transmission by any kind of waste, the impact severity resulting from disease transmission from any kind of waste, the amount of each kind of waste and the population exposed to wastes. All of these are the determining factors of danger in medical centers.For the mathematical model to get closer to the real conditions of the derivatives types of the vehicle routing problem, the following items have been considered in the model, which have increased its computational complexity. These items include multiple periodic, intermediate facilities, multiple trips, open routing, vehicles and intermediate facilities capacity constraint and permissible time constraint for collecting waste. The objectives in multi-objective problems might be in conflict and obtaining the Pareto solution set helps the decision-maker to find the desired solution. Hence, in this research, to solve the proposed model, a multi-objective genetic algorithm that has been combined with a number of heuristic algorithms is designed and implemented. The solution representation has been provided by a new method. The efficiency and effectiveness of the solution method are investigated in small and large dimensions. Also, a sensitivity analysis is done on the parameters of the model to determine their effect in the problem optimization. The application of the suggested algorithm is shown by a case study for a medical wastes collection company in Isfahan. The results indicate to strike a balance between the goals, a compromised solution is chosen as the solution from the Pareto solution set using the method of displaced ideal. Given the results, with 33.66 percent of cost increase and 12 percent of fuel consumption increase relative to the current collection system, the risk of public health risk can be decreased to 45.09.
