Addressing Waste Management Efficiency in Qatar: A Meta-Heuristic and Approximation Approach to Traveling Salesman Problem

Efficient waste collection and management are imperative for addressing the environmental implications arising from the escalating volume of daily waste generation. To mitigate these challenges, strategic interventions are essential to optimize waste collection systems, particularly in regions like Qatar where financial constraints underscore the need for cost-effective solutions meeting various performance criteria. This study endeavors to optimize waste collection and management in Qatar by utilizing the Traveling Salesman Problem (TSP) framework. Through a meticulous exploration of four distinct TSP implementations - namely, the Nearest Neighbor (NN), Christofides', Ant Colony Optimization (ACO), and Simulated Annealing (SA) - tailored to the distinctive demands of waste collection logistics, our focus has been on delineating the most efficient routes for waste collection vehicles. The selection of these algorithms is based on their demonstrated effectiveness in similar optimization problems and their suitability for waste collection operations. Embracing approximation approaches such as NN and Christofides' algorithm has provided practical solutions, complemented by the strategic insights offered by ACO and SA heuristics. By conducting a rigorous analysis and comparison of these algorithms, we endeavor to offer insights into their efficacy in addressing Qatar's waste management imperatives, thereby informing decision-making processes and enhancing operational efficiency within the waste management domain.