A solid waste recycling vehicle routing optimization model under online appointment-based mode using MOACO-ET algorithm

Abstract

Solid waste (SW) recycling is essential to preserving the environment and lowering pollution. Countries around the world promote their residents to participate in SW recycling. This study proposes an online appointment-based SW recycling mode to overcome the limitations of traditional recycling ways, such as limited coverage, low efficiency, and the uncertainty of customer recycling times. Combined with this mode, we build a multi-objective optimization model aimed at finding the optimal vehicle routes within each customer’s acceptable time window, with the goals of reducing operating costs of the recycling industry and customer dissatisfaction with recycling services. A multi-objective ant colony optimization algorithm based on elite thought (MOACO-ET) is designed to solve the suggested model, seek balance among multiple objectives, search for diversified Pareto optimal solutions, and provide flexible and adaptive solutions. To verify the effectiveness of the proposed model and the performance of the MOACO-ET algorithm, a comparative analysis is conducted with the Max-Min Ant System (MMAS) in a case study. We find that MOACO-ET algorithm exhibits better convergence and search performance, customer dissatisfaction can be decreased by 47.58%, and average operating costs and total vehicle mileage are reduced by 2.02% and 2.67%, respectively. This study combines multi-objective optimization with the MOACO-ET algorithm to propose an effective solution for optimizing the collection and transportation routes of recycling vehicles. This approach helps improve SW recycling efficiency, reduce transportation costs, enhance service quality, and promote sustainable development.