A Simulation Based Metaheuristics for Capacitated Vehicle Routing Problem

Abstract

Waste collection and transportation are essential elements of effective waste management. However, despite their importance, previous studies have highlighted several challenges, such as routing inefficiencies and environmental concerns. This study seeks to develop an optimized approach for waste collection and transportation under conditions of demand uncertainty, capacity limitations, and traffic constraints, through the application of a simheuristics-based method. The methodology utilizes a simheuristics approach, integrating a Genetic Algorithm (GA) to determine optimal routing solutions, while employing Discrete Event Simulation (DES) to incorporate key economic, environmental, and social variables. Data were obtained from field experiments and Google Maps, and assumptions regarding capacity requirements, distances and collection points, transportation cost components, and road conditions were established to ensure the reliability of the simulation results. The application of the simheuristics approach effectively reduces total transportation costs by approximately 51%, while also significantly minimizing environmental impacts. This research contributes to the academic literature by presenting an innovative method that strengthens existing waste collection strategies with an emphasis on sustainability. Additionally, it offers valuable insights for waste management policy, enabling the optimization of waste collection without exceeding capacity limits.