Beschreibung:
This paper studies the trailer shipment problem and suggests data-analytics methodologies to efficiently solve it. The problem is to determine the proper weight of products loaded on a trailer (a transporting container) by the manufacturer, which is delivered by a tractor (a motor vehicle) owned by third-party logistics (3PL) providers. The manufacturer must meet a regulation that restricts the gross weight of the truck on the road. However, the challenge comes from the fact that the tractor weight is uncertain and unknown to the manufacturer when it determines the load size on the trailer. We apply data-analytics methodologies that compute the optimal loading size (weight) of a trailer. Furthermore, we propose a dynamic trailer assignment methodology using reinforcement learning, which allows operational flexibility in the trailer shipment problem. Using the transaction-level shipping data obtained from a real company, the suggested methodologies are evaluated. The experiment reveals that such operational flexibility can reduce the logistics cost dramatically. This work studies a general context of the trailer shipment problem and suggests efficient data-analytics approaches, which can be widely applied in diverse industries using 3PL outsourcing