Approximate Dynamic Programming for Inland Empty Container Inventory Management

Empty container repositioning (ECR) is crucial in handling global trade imbalances by managing the flow and storage of empty containers to effectively accommodate customer demands and returns. We formulate a Markov decision process that accounts for practical characteristics of ECR, including multiple transportation modes and lead times, and uncertainty and serial correlation in net container inflows. To determine a cost-minimising repositioning policy for real-life scenarios, we employ a stochastic approximate dynamic programming approach, integrated with statistical techniques, such as convex regression and Latin hyper cube sampling. We highlight the advantages of incorporating exogenous variables into the approximation model. A case study with historical daily data on empty container in- and out-flows demonstrates the effective control of on-hand inventory levels while optimising holding and leasing costs. Moreover, we quantify the benefits of leveraging all transportation modes, with cost reduction potentials of up to 26.05%. Finally, we evaluate the robustness of our algorithm under variations in key parameters.