Sustainable design modifications municipal solid waste management network and better optimization for risk reduction analyses

摘要

The staggering rate of waste generation due to industrialization, urbanization and population growth has raised a pressing problem regarding environmental damages and human health hazards, especially in developing nations. Severe environmental and social risks such as surface and groundwater pollution, soil and air contamination, serious human health hazards and damage to aesthetic aspects are some inappropriate waste management results. To overcome these burdens and move toward sustainable municipal solid waste management, effective and efficient practices are required. This study deals with a sustainable municipal solid waste network design problem under uncertainty. For this purpose, a multi-objective fuzzy robust programming model with the aim of minimizing the total costs of the network and environmental damages and optimizing social impacts is proposed. Different practical waste treatment practices are considered in the proposed network, including incineration, recycling, composting and sanitary landfilling. Also, a risk that is related to the population of facilities' potential location is introduced and the Fuzzy Failure Mode and Effects Analysis is applied to determine the value of the mentioned risk. In order to tackle uncertain parameters of the proposed model, the Fuzzy Robust Optimization approach is applied. The proposed multi-objective model solves as a single-objective model using the Fuzzy Goal Programming approach with Different Importance and Priorities. The applicability of the model is demonstrated through a real case study of Minudasht's municipal solid waste management system. The optimal location with the least risk and environmental impacts for each treatment facility with related technology and method is introduced. Furthermore, sensitivity analysis has done on some parameters to determine the effect of parameters' changes on objective functions. The results showed the effectiveness and efficiency of the proposed model. (C) 2020 Elsevier Ltd. All rights reserved.