Optimizing truck scheduling and dock placement at cross-docking systems through a hybrid genetic-ant colony optimization algorithm

This paper proposes an innovative two-stage methodology to optimize truck scheduling and dock placement in cross-docking systems, streamlining logistics by transferring shipments directly from inbound to outbound vehicles without storage. The first stage addresses strategic warehouse location decisions to enhance logistics networks, while the second focuses on operational efficiencies in truck routing and scheduling. A hybrid genetic-ant colony optimization algorithm reduces costs, minimizes environmental impacts, and improves service quality. Supported by mathematical modeling and experimental analysis, the results demonstrate the methodology's ability to enhance efficiency and reduce costs. Findings reveal that Increasing fleet capacity reduces emissions and costs, balancing economic and environmental goals. Experimental results indicate that the hybrid approach achieves up to 25% cost savings and a 19% improvement in Pareto efficiency compared to standalone methods, highlighting its practical applications in logistics and providing a foundation for future research to address uncertainties, incorporate inventory management, and refine optimization approaches.