Crisp and fuzzy optimization models for sustainable municipal solid waste management

Increasing municipal solid waste (MSW) generation in the city has caused a serious problem to the authority, especially for emerging countries. Improper management of MSW will increase greenhouse gas (GHG) emissions. Apart from landfill, there are other options to convert MSW into valuable products or energy, such as recycling and incineration. This paper presents crisp and fuzzy optimization models for designing optimal supply chain networks for sustainable MSW management, considering economic (cost minimization) and environmental ob-jectives (emissions reduction). The crisp model is based on a superstructure and consists of material balances, capacity limits and planning constraints for MSW transfer stations, disposal sites and treatment technologies. Fuzzy optimization with max-min aggregation is then incorporated to handle conflicting economic and envi-ronmental objectives, as well as uncertainties in GHG emission factors and cost coefficients of MSW treatment. A case study for the design of an optimal MSW supply chain network in the city of Qingdao, China is presented to illustrate the proposed approach. The conflicting objectives of minimizing the total net cost and GHG emissions for MSW transport and treatment are analyzed using the crisp model, yielding a set of Pareto optimal solutions. A compromise solution achieving a 21.6% cost reduction and a 28.4% emissions reduction is identified using the fuzzy model. An inferior solution is also found with smaller cost (19.1%) and emissions reductions (25.1%) due to the use of conservative estimates of uncertain cost coefficients and emission factors. These results demonstrate the effectiveness of the proposed models in dealing with the MSW management problem.

Automated summary

This paper presents crisp and fuzzy optimization models for designing optimal supply chain networks for municipal solid waste (MSW) management, considering economic and environmental objectives. A case study in Qingdao, China demonstrates the effectiveness of the proposed models in finding Pareto optimal solutions and identifying compromise solutions. The results show the potential of the models in addressing the MSW management problem.