Optimizing Empty Container Repositioning at Inland Terminals Using Data Analytics to Make an Informed Decision for a Seaport in Sweden

Abstract

Sweden, as a country, maintains a fair balance between imports and exports. However, in this post-pandemic era, there has been a rise in both imports and exports. Consequently, the transportation of goods involves extensive movement of containers. This thesis focuses on optimizing the existing container transport network from the perspective of the seaport, shipping lines, inland terminals, and rail operators. The network and flows are modelled as a mixed integer linear programming problem using Python and Gurobi. The goal is to meet the demand for empty containers while reducing turnaround time, container kilometres, the number of containers, and emissions. The results of the optimization model aid decision-making in selecting inland terminals for triangulation. The selection criteria for inland terminals include the number of containers handled, total emissions, triangle distance factor, and container kilometres. Currently, the flow of containers does not employ any strategies for utilizing empty containers. However, through the analysis of the optimization model, the thesis achieves the reuse of empty import containers by implementing triangulation and storage at inland terminals. Implementation of these strategies holds significant potential for cost savings for large shipping lines. In the current container transport system, there are four legs of transport: two for import flows (from port to importer and back) and two for export flows (from port to exporter and back). By implementing the proposed strategies, the number of legs in the transportation system can be reduced from four to three, resulting in increased efficiency. The results of the optimization model have demonstrated promising potential for cost savings across various inland terminals. The implications for different actors involved in the container transport network are thoroughly investigated in this study. Moreover, this research addresses the gap between qualitative decision-making and the need for substantial quantitative analysis. By utilizing the optimization model, decision-makers now have a valuable tool that provides quantitative insights, enabling more informed and data-driven decision-making processes.