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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.

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.

Event logs generated by Process-Aware Information Systems (PAIS) provide many opportunities for analysis that are expected to help organizations optimize their business processes. The ability to monitor business processes proactively can allow an organization to achieve, maintain or enhance competitiveness in the market. Predictive Business Process Monitoring (PBPM) can provide measures such as the prediction of the remaining time of an ongoing process instance (case) by taking past activities in running process instances into account, as based on the event logs of previously completed process instances. With the prediction provided, we expect that organizations can respond quickly to deviations from the desired process. In the context of the growing popularity of deep learning and the need to utilize heterogeneous representation of data; in this study, we derived a new deep-learning approach that utilizes two types of data representation based on a parallel-structure model, which consists of a convolutional neural network (CNN) and a multi-layer perceptron (MLP) with an embedding layer, to predict the remaining time. Conducting experiments with real-world datasets, we compared our proposed method against the existing deep-learning approach to confirm its utility for the provision of more precise prediction (as indicated by error metrics) relative to the baseline method.

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