On the value of operational flexibility in the trailer shipment and assignment problem: Data-driven approaches and reinforcement learning

Abstract

This paper addresses the trailer shipment problem—the task of managing the optimal weight of products in a trailer, taking into consideration the uncertain weight of tractors provided by Third-Party Logistics (3PL) providers, and abiding by the gross weight regulation. We propose a series of data-analytics methodologies, including Sample Average Approximation (SAA), Empirical Risk Minimization (ERM), and a dynamic trailer assignment methodology using Reinforcement Learning (RL), to optimize the trailer shipment process. The introduction of operational flexibility and the dynamic utilization of tractor weight information upon arrival are pivotal to the effectiveness of the RL-based methodology. To validate our approaches, we apply them to transaction-level shipping data from a real company. The results demonstrate significant cost reductions in the logistics process, driven by the dynamic assignment methodology which allows efficient selection of trailers to suit varying tractor weights. This research proposes an innovative approach to the prevalent trailer shipment problem, applicable to a wide range of industries using 3PL outsourcing. Through this work, we demonstrate the transformative potential of data-analytics methodologies to enhance efficiency and profitability in logistics operations.