Route Optimization for Rice Waste Collection in Daule Canton

Abstract

This paper presents a comprehensive hybrid methodology for solving the Capacitated Vehicle Routing Problem (CVRP), focusing on the environmental challenge of optimizing routes for rice waste collection in Daule Canton. The CVRP is formulated as a graph-based problem, where the objective is to minimize the total distance traveled by a fleet of capacity-constrained vehicles while collecting waste from multiple points. Google OR-Tools, a robust optimization toolkit, was employed to model and solve the problem. Simulated annealing was used as the primary metaheuristic to improve solution quality, offering a powerful approach to explore the solution space and find near-optimal solutions. The metaheuristics were carefully defined and parameterized to ensure effective performance under various constraints, such as vehicle capacity and route length. This approach not only addresses the operational aspects of waste collection but also contributes to environmental sustainability by reducing fuel consumption and emissions. The results demonstrate the effectiveness of simulated annealing in solving the CVRP within acceptable computational times, suggesting its suitability for practical applications in waste management. Future work will explore other metaheuristics, such as genetic algorithms and ant colony optimization, to improve solution quality and computational efficiency. By comparing these approaches, we aim to provide a more robust framework for route optimization in similar environmental and logistics scenarios.