A Fuzzy Modeling Framework for Sustainable Municipal Solid Waste Collection by the Mixed Fleet

Abstract

Waste collection is a significant service for which local governments and municipalities are responsible, involving extensive operational tasks directly or indirectly affecting various stakeholders. The operational tasks are expensive based on high investment costs, operating costs, and environmental costs, impacting health and the environment as well. Also, some parameters in the waste collection may not have crisp values because of the uncertainty associated with fuzzy set theory. Moreover, sustainable waste collection may achieve to reduce carbon emissions. This study dwells on sustainably collecting, transporting, and delivering multiple municipal solid wastes from geographically dispersed locations to landfills under uncertainty. This paper introduces the fuzzy mixed fleet waste collection problem (FMF-WCP) that aims to utilize routing a mix of conventional fuel and electric vehicles, considering the sustainability in urban logistics. The problem also considers multiple shifts for multiple municipal solid wastes when the transformation of the model is conducted for the fuzzy inequalities. A powerful hybrid heuristic, which consists of a new greedy construction heuristic, an adaptive large neighborhood search (ALNS) employing extended repair and destroy operators, and a local search procedure, is proposed to solve the complex problem. Then, extensive experimental analyses are conducted to highlight the high-quality performance of the hybrid heuristic using both newly created small and large real-life instances. Furthermore, a sensitivity analysis is also performed to examine the effect of fleet composition and cost factors. The experimental results show the effectiveness of the introduced hybrid heuristic on the problem. The results show that an increase in energy costs directly leads to an increase in operating expenses. Therefore, environmentally friendly vehicles may achieve a cost-effective operation with lower carbon emissions, providing sustainability.