A multi-objective optimization model for municipal waste management system in Tehran city, Iran

This paper describes an application of a multi-objective optimization algorithm (i.e., the non-dominated sorting genetic algorithm II [NSGA-II]) and a fuzzy expert system (FES) to allocate municipal waste capacities to recycling, aerobic composting, incinerator, and landfill facilities with these objectives: (1) minimization of the economic cost; and (2) minimization of the environmental cost of the municipal solid waste management system. The non-dominated sorting genetic algorithm II is used to find multi-objective solutions, and a FES is employed for the environmental cost evaluation. Tehran, the capital of Iran, is selected as the case study. In the proposed model, the quantitative and qualitative aspects of the municipal solid waste management issue are taken into account so that the economic and environmental objectives that typically arise in cities in this regard can be considered. To achieve economic and environmental cost objectives, five scenarios with different economic and environmental costs and four solid waste management methods (recycling, aerobic composting, incinerator, and landfill) are employed. The results show that while hybrid methods for municipal waste management can be used, recycling and aerobic composting are the principal methods—that is, the scenarios that utilized higher percentages of these methods resulted in the lowest economic and environmental costs. Additionally, the results of model validation using predicted and real data from the different scenarios demonstrate the high accuracy of the proposed model.