Anticipatory freight selection in intermodal long-haul round-trips

Abstract

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, differences 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 different 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 different terminals in the port of Rotterdam, and back. We show that, under different 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 findings for several network settings and state characteristics, thereby providing key managerial insights about look-ahead policies in intermodal long-haul round-trips.

Dynamic and Stochastic Shipment Matching Problem in Multimodal Transportation

“…With the development of computing power, online decisions can be made incorporating dynamic and stochastic information. In the literature, dynamic and stochastic approaches have been investigated in many areas, such as vehicle routing problems (13)(14)(15)(16), pickup and delivery problems (17)(18)(19)(20)(21), resource allocation problems (22)(23)(24), and multimodal container routing and flow control problems (11,12,25). However, to the best of the authors' knowledge, none of the studies in the literature investigated dynamic and stochastic models for the DSSM problem.…”

“…Van Riessen et al (12) designed a decision tree to derive real-time decision rules for suitable allocation of containers to inland services. Rivera and Mes (25) proposed an algorithm based on approximate dynamic programming to assign newly arrived containers to either barges or trucks to achieve better performance over a planning horizon. While the container routing and flow control problems focus on the container level (i.e., integer decisions indicating the number of containers assigned to services), the DSSM problem focuses on the shipment request level (i.e., binary decisions indicating the matching between requests and services).…”

Optimizing barge utilization in hinterland container transportation

“…If one takes a longer planning horizon it might happen that a customer has not put in an order yet or that the ETA of the sea vessel changes, for instance, because of bad weather conditions. Recent works have also focused on the ETA of a container being stochastic, see for instance Perez Rivera and Mes () and Zuidwijk and Veenstra ().…”

Data-driven process redesign: anticipatory shipping in agro-food supply chains

“…Anticipatory approaches, which refer to using historical data to make a current decision in anticipation of what may happen in the near future, are investigated in different decisions, e.g. freight selection in long-haul round-trips by Pérez Rivera and Mes (2017). This paper addresses anticipatory shipping (AS), a data-driven redesign strategy regarding shipping decisions to expedite delivery service and reduce operational costs.…”