A Hybrid Matheuristic Framework for the Chinese Postman Problem with Load-Dependent Costs

Abstract

The Chinese Postman Problem with load-dependent costs (CPP-LC) arises in real-world logistics and transportation systems where travel costs depend on vehicle load and energy consumption. In this work, we propose a hybrid optimization framework that integrates metaheuristic search with mathematical programming to efficiently solve CPP-LC. The proposed method combines local search procedures with reduced mixed-integer linear programming (MILP) models to balance exploration and intensification. In addition, we develop an Ant Colony Optimization (ACO) algorithm to enhance scalability on large instances. Extensive experiments on benchmark datasets demonstrate that the proposed framework consistently achieves high-quality solutions and outperforms existing approaches in solution quality, while maintaining competitive computational efficiency. These results highlight the effectiveness of hybrid optimization strategies for complex, load-dependent routing problems in practical applications. Our implementation is publicly available at https://github.com/HySonLab/MatCPP