Shipper-Centric Short-Term Vehicle Capacity Requirement Planning for First-Mile Bulk-Commodity Delivery

초록

This study considers the short-term vehicle capacity requirement planning problem from the shipper's perspective, driven by the rapidly increasing demand for transporting bulk commodities in first mile logistics. The problem is to determine the types and quantities of vehicles and assign them to regions. The objective is to minimize the total costs associated with transport and additional stop costs. This problem is formulated as a mixed-integer programming model to reflect practical logistics constraints. This proposes two matheuristic algorithms to solve this problem. The relax-and-fix heuristic constructs an initial solution by progressively relaxing and fixing subsets of decision variables, while the fix-and-optimize heuristic improves this solution through iterative local optimization, refixing certain variables. Based on synthetic and real data from a South Korean context, computational experiments show that these proposed algorithms consistently produce acceptable quality solutions within a reasonable computation time. Scenario analyses provide further practical insights for shippers by incorporating real-world operational constraints.

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