A genetic-algorithm-aided fuzzy chance-constrained programming model for municipal solid waste management

A genetic-algorithm-aided fuzzy chance-constrained programming (GAFCCP) model is developed for supporting municipal solid waste (MSW) management under uncertainty. The proposed model is an innovative combination of the genetic algorithm (GA) and fuzzy chance-constrained programming (FCCP) method and thus makes a unique contribution to enhancing the feasibility and applicability of the optimization model. The GA was capable of tackling the complicated fuzzy membership function and was used to seek optimal solutions by progressively evaluating the performance of the individual solutions; meanwhile, FCCP ensured that the fuzzy constraints were satisfied at specified confidence levels, leading to cost-effective solutions under acceptable risk magnitudes. A long-term regional waste management model of Zhongshan City, China, was formulated to demonstrate the applicability of the proposed GAFCCP model. The comparison results with ongoing treatment schemes demonstrated the superiority of the generated model solutions in the aspects of cost reduction and greenhouse gas emission mitigation.