Interterminal Truck Routing Optimization Using Deep Reinforcement Learning.

Taufik Nur Adi,Hyerim Bae,Yelita Anggiane Iskandar

doi:10.3390/s20205794

Abstract

The continued growth of the volume of global containerized transport necessitates that most of the major ports in the world improve port productivity by investing in more interconnected terminals. The development of the multiterminal system escalates the complexity of the container transport process and increases the demand for container exchange between different terminals within a port, known as interterminal transport (ITT). Trucks are still the primary modes of freight transportation to transport containers among most terminals. A trucking company needs to consider proper truck routing planning because, based on several studies, it played an essential role in coordinating ITT flows. Furthermore, optimal truck routing in the context of ITT significantly affects port productivity and efficiency. The study of deep reinforcement learning in truck routing optimization is still limited. In this study, we propose deep reinforcement learning to provide truck routes of a given container transport order by considering several significant factors such as order origin, destination, time window, and due date. To assess its performance, we compared between the proposed method and two approaches that are used to solve truck routing problems. The experiment results showed that the proposed method obtains considerably better results compared to the other algorithms.

Highlights

Shipping containers have become ubiquitous and widely adopted by the business world as a standard method of efficient freight transportation
The number of files used in the training process represents the different variation characteristics of the transport order data, which might occur in the real container terminal
Many major ports around the globe are affected by the increasing volume of global containerized transport

Summary

Introduction

Shipping containers have become ubiquitous and widely adopted by the business world as a standard method of efficient freight transportation. The need for developing new container terminals requires investments in more port equipment and facilities such as berths, cranes, straddle carriers, terminal operators, and internal trucks. Hu et al [2] stated that a multiterminal system increases the complexity of the container transport process. The unloading and loading process can be divided into different subprocesses. When a vessel arrives at a terminal in a port, the export container is loaded for deep-sea transport, and import containers are unloaded from the ship and directly transported to the customer. Some containers can be stored temporarily at the stack, transferred to different transportation modes, or exchanged between terminals. The movement of containers among terminals is known as interterminal transport (ITT). Tierney et al [3] defined ITT as any land and sea Sensors 2020, 20, 5794; doi:10.3390/s20205794 www.mdpi.com/journal/sensors

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Results

Conclusion