Abstract
Many ports worldwide continue to expand their capacity by developing a multiterminal system to catch up with the global containerized trade demand. However, this expansion strategy increases the demand for container exchange between terminals and their logistics facilities within a port, known as interterminal transport (ITT). ITT forms a complex transportation network in a large port, which must be managed efficiently given the economic and environmental implications. The use of trucks in ITT operations leads to the interterminal truck routing problem (ITTRP), which has been attracting increasing attention from researchers. One of the objectives of truck routing optimization in ITT is the minimization of empty-truck trips. Selection of the transport order (TO) based on the current truck location is critical in minimizing empty-truck trips. However, ITT entails not only transporting containers between terminals operated 24 h: in cases where containers need to be transported to a logistics facility within operating hours, empty-truck trip cost (ETTC) minimization must also consider the operational times of the transport origin and destination. Otherwise, truck waiting time might be incurred because the truck may arrive before the opening time of the facility. Truck waiting time seems trivial, but it is not, since thousands of containers move between locations within a port every day. So, truck waiting time can be a source of ITT-related costs if it is not managed wisely. Minimization of empty-truck trips and truck waiting time is considered a multiobjective optimization problem. This paper proposes a method of cooperative multiagent deep reinforcement learning (RL) to produce TO truck routes that minimize ETTC and truck waiting time. Two standard algorithms, simulated annealing (SA) and tabu search (TS) were chosen to assess the performance of the proposed method. The experimental results show that the proposed method represents a considerable improvement over the other algorithms.
Highlights
Global containerized trade continues to grow annually
We propose a cooperative multiagent deep reinforcement learning (RL) (CMADRL) that contains two agents focusing on a specific objective to cope with this situation
The x-axis in both figures presents the number of episodes, and the y-axis shows the cumulative reward per episode
Summary
The United Nations Conference and Development (UNCTAD) report for 2018, for example, shows a 6.7% increase from the previous year [1]. This situation forces most large ports worldwide to develop a multiterminal system to meet the containerized trade demand. It complicates the container transport process and increases the demand for container transport between terminals and logistics facilities, known as interterminal transport (ITT). In many situations, some containers can be stored temporarily at the stack, transferred to various transport modes, or exchanged between terminals or logistics facilities within a port. Some degree of ITT is Processes 2021, 9, 1728 or logistics facilities within a port.
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