AN OPTIMIZATION MODEL FOR WASTE COLLECTION PATHS THAT AIMS TO CONNECT COST REDUCTION AND EMISSION MITIGATION IN ORDER TO ATTAIN SUSTAINABLE DEVELOPMENT OBJECTIVES IN NORTH …

Abstract

In the contemporary era, population growth and urban expansion are driving the necessity for creating a capable waste management system (WMS) that is based on recent advances and emerging models. Within these systems, waste collection appears as a key function alongside various procedures. A new approach presented in this research suggests the implementation of a two-level WMS to reduce operational costs and environmental implications through the incorporation of Industry 4.0 ideology. Both frameworks use the latest IoT-based traceability devices to compare real-time data on waste levels in containers and sorting facilities against a Threshold Waste Level (TWL) parameter. The primary model focuses on optimizing the operating costs and carbon dioxide emissions associated with transporting waste from containers to sorting facilities, integrating considerations for time constraints. Then, a capacity-constrained vehicle routing problem is formulated as a follow-up model to reduce the costs associated with transporting waste to recycling facilities. To determine the most effective solution, modern meta-heuristic algorithms are deployed, along with the development of various innovative heuristics that are tailored to the specific requirements of the problem. Furthermore, these newly generated heuristic approaches are used to generate preliminary feasible solutions within the meta-heuristic domain, which are then compared to randomly generated solutions. An evaluation of the efficiency of the proposed algorithms is performed, applying the best-worst method (BWM) to rank the algorithms based on criteria such as relative percentage deviation, relative deviation index and hit time.