A sustainable modelling for solid waste management using analytical hierarchy process, Monte Carlo simulation and NSGA-III

Abstract

The issue of solid waste management is one of the greatest challenges of any society. Instead of leveraging solid waste's potential to produce energy using waste-to-energy (WTE) technologies, different countries especially developing countries stick to their ineffective and inferior methods to dispose of solid wastes. To fill this gap between energy production and waste management, this paper develops a novel methodology that integrates three efficacious tools, including Monte Carlo simulation, multi-attribute decision-making, and mathematical optimisation modelling to solve the problem from the perspective of economy, energy recovery, environment (3E), and social sustainability. In this paper, a multi-attribute decision-making method, namely AHP, is investigated to select the most suitable emerging WTE technological options as well as the optimal waste collection routes, to capture solid waste as a potential renewable energy source. Monte Carlo simulation and beta-PERT distribution are also applied to reduce the uncertainty intrinsic to decision-making. After validating the proposed methodology in a real-world case study of Tehran city, we apply a novel meta-heuristic algorithm namely NSGA-III to solve the problem on large scales with a reasonable computational time. The performance of this algorithm in solving waste location-routing problems is explored by conducting several trials. Highlights Addressing an integrated waste-to-energy and transportation network for solid waste management. Developing a novel methodology for finding the best waste-to-energy technological alternatives with respect to the sustainable criteria. Finding the high-quality set of waste collection routes, in addition to the location of facilities with the consideration of waste collection in a separated manner and vehicle's fuel consumption and emission in the MIP model. Monte Carlo simulation and beta-PERT distribution are proposed to stochastically model the absence of accurate data. Application of NSGA-III algorithm to handle the solid waste location routing problem.