Anticipatory freight selection in intermodal long-haul round-trips

We consider a Logistic Service Provider (LSP) that transports freight periodically in a long-haul round-trip. At the start of a round-trip, the LSP consolidates freights in a long-haul vehicle and delivers them to multiple destinations in a region. Within this region, the LSP picks up freights using the same vehicle and transports them back to the starting location. The same region is visited every period, independent of which freights were consolidated. Consequently, di erences in costs between two periods are due to the destinations visited (for delivery and pickup of freights) and the use of an alternative transport mode. Freights have di erent time-windows and become known gradually over time. The LSP has probabilistic knowledge about the arrival of freights and their characteristics. Using this knowledge, the goal of the LSP is to consolidate freights in a way that minimizes the total costs over time. To achieve this goal, we propose the use of a look-ahead policy, which is computed using an Approximate Dynamic Programming (ADP) algorithm. We test our solution method using information from a Dutch LSP that transports containers daily, by barge, from the East of the country to di erent terminals in the port of Rotterdam, and back. We show that, under di erent instances of this real-life information, the use of an ADP policy yields cost reductions up to 25.5% compared to a benchmark policy. Furthermore, we discuss our ndings for several network settings and state characteristics, thereby providing key managerial insights about look-ahead policies in intermodal long-haul round-trips.