LAUSR

Abstract This study addresses a novel two-stage possibilistic-stochastic programming model for designing a reliable municipal solid waste management system (MSWMS) in the presence of random disruptions arising from unexpected natural and man-made hazards and imprecision arising from the lack of knowledge about some input parameters. To design a profit-maximizing and environment-friendly MSWMS, an integrated system is worked out to provide revenues through producing renewable energy, fertilizers, and recycled products from processing municipal solid wastes. To solve the proposed model in large-scale instances, an L-shaped method is designed in which several enhancement strategies such as valid inequalities, local branching, and primal heuristics are developed to expedite the convergence rate. The proposed model is validated through a real case study of the MSWMS in the city of Shiraz, Iran. Compared with traditional models with the consideration of only normal disruption-free conditions, the expected profit of the proposed model drops down slightly but the system will hedge against the risk of different disruptions. The optimal network configuration is strongly affected by disruptive conditions such that the number of constructed facilities is increased and they are moved to more reliable locations. Moreover, when designing reliable MSWMSs from economic, environmental, and risk perspectives is approached, the system will incur more costs. The proposed solution procedure also proves to be effective in finding good quality solutions within a short running time for large-scale instances that are far beyond the reach of CPLEX.