Interterminal Truck Routing Optimization Using Deep Reinforcement Learning

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.

Trailer allocation and truck routing using bipartite graph assignment and deep reinforcement learning

“…In the case of truck routing, Adi et al (2020) suggested using deep reinforcement learning (DRL) to generate truck routes for a particular container transport order while taking into account numerous important aspects such as order origin, destination, time window, and due date. They considered the origins and destinations list provided beforehand and the method they used to solve the homogeneous vehicle PDP is DQN with experience replay memory.…”

Interterminal Truck Routing Optimization Using Cooperative Multiagent Deep Reinforcement Learning

“…Adi et al [16] proposed single-agent DRL to solve the VRP in the context of ITT. The authors utilized DRL to provide a feasible truck route with a minimum total cost related to the use of trucks in transporting containers among container terminals.…”

Solving the Inter-Terminal Truck Routing Problem for Delay Minimization Using Simulated Annealing with Normalized Exploration Rate

“…Upon discharge from the vessel, the container is expected to be delivered directly to the customer. However, in most cases, a container is transported to the stacking area, transferred between terminals and logistics facilities, or transferred to different modes of transportation to meet all logistics requirements [3]. This container movement is known as inter-terminal transport (ITT) [2,4].…”

“…Previous studies related to ITT were typically concerned with the efficient pick-up and delivery of containers between separated areas within the seaports [2,3,[6][7][8][9][10]. While the mathematical model of the specific ITT problem of interest is typically presented, in most cases the problem is solved either heuristically or approximately by using metaheuristics due to the computational complexity of the problem [8][9][10].…”