Application of mixed integer programming and simulated annealing algorithm in sustainable logistics network design

Abstract

This article integrates mixed integer programming and the simulated annealing algorithm to enhance both the economic efficiency and environmental sustainability of logistics networks. A multi-objective optimization model is first developed, incorporating economic costs, carbon emissions, and social service levels to address the diverse needs of stakeholders. A solution approach combining the precision of mixed integer programming with the global search capabilities of the simulated annealing algorithm is then proposed. This hybrid approach effectively overcomes the limitations of traditional optimization methods, particularly in managing complex constraints and large-scale problems. Through practical case studies, the proposed method demonstrated superior optimization performance. The average total cost after optimization was $52,444.61, outperforming solutions derived from using either mixed integer programming or simulated annealing alone. Furthermore, the service satisfaction rate exceeded 90%, indicating the model’s effectiveness in balancing multiple objectives. The findings of this study not only offer a novel theoretical framework for logistics network design but also provide valuable support for enterprises striving to achieve sustainable development in their operations. The research highlights the potential to strike a harmonious balance between economic, environmental, and social responsibilities, paving the way for more sustainable and efficient logistics practices.