Critical Literature Review into Planning of Inter-Terminal Transport: In Port Areas and the Hinterland

Hu, Qin; Corman, Francesco; Lodewijks, Gabriël

doi:10.1155/2019/9893615

Cited by 12 publications

(5 citation statements)

References 75 publications

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“…Furthermore, research underscores the strategic imperative of modernizing intermodal terminals to align with overarching logistics goals (Hu et al, 2019). The literature advocates for the creation of agile, responsive, and technologically advanced logistics ecosystems, equipped to navigate the complexities of contemporary supply chain dynamics (Chowdhary, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Strategic Application of Industry 4.0 Technologies in Enhancing Intermodal Transport Terminal Efficiency

Krstić,

Tadić,

Brnjac

2023

JOTE

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In the realm of contemporary logistics, the criticality of intermodal terminals as central nodes for seamless cargo transitions between various transportation modes is well-recognized. This study focuses on the strategic integration of Industry 4.0 (I4.0) technologies to advance the operational efficiency of these terminals. A hybrid Multiple Criteria Decision Making (MCDM) methodology, amalgamating the Best-Worst Method (BWM) and Axial Distance based Aggregated Measurement (ADAM), is employed for a systematic evaluation. This approach facilitates the identification and prioritization of key I4.0 technologies. Findings of this study underscore the paramount importance of the Internet of Things (IoT), Artificial and Ambient Intelligence, and Autonomous and Automated Guided Vehicles in revolutionizing terminal efficiency. The efficacy of the proposed hybrid model is demonstrated in its capacity to generate practical, insightful recommendations for technology selection, thereby guiding stakeholders in making informed investments. These investments are projected to significantly enhance the operational capabilities of intermodal terminals and, by extension, the efficiency of the overall supply chain. The contribution of this study lies in its addressal of the existing research gap concerning the applicability and selection of I4.0 technologies in intermodal transport terminals (ITTs). It offers a novel, pragmatic framework for stakeholders within the logistics sector, aimed at facilitating the modernization and optimization of terminal operations. The insights and strategic directions provided herein are anticipated to be of substantial value to those endeavoring to navigate the complexities of terminal modernization in the era of I4.0.

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Section: Literature Reviewmentioning

confidence: 99%

Strategic Application of Industry 4.0 Technologies in Enhancing Intermodal Transport Terminal Efficiency

Krstić,

Tadić,

Brnjac

2023

JOTE

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“…The growth in freight volumes greatly impacts intermodal logistics areas, such as ports and business parks in the hinterland. In this context, several ports such as Shanghai and Rotterdam are investing in multi-terminal systems, which inherently result in highly complex transport systems within the port (Hu et al 2019). As a result, companies face new challenges in the field of ITT problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation of the Internal Electric Fleet Dispatching Problem at a Seaport: A Reinforcement Learning Approach

Brunetti

Campuzano

Mes

2022

2022 Winter Simulation Conference (WSC)

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Through discrete-event simulation, we evaluate the impact of using a fleet of electric and autonomous vehicles (EAVs) to decouple inbound trucks from the internal freight flows in a seaport located in the Netherlands. To support the operational control of EAVs, we use agent-based modeling and support the decision-making capabilities using a reinforcement learning (RL) approach. More specifically, to model the assignment of EAVs to container transport or battery charge, we introduce the Internal Electric Fleet Dispatching Problem (IEFDP). To solve the IEFDP, we propose an RL approach and benchmark its performance against four different assignment heuristics. Our results are compelling: the RL approach outperforms the benchmark heuristics, and the decoupling process significantly reduces congestion and waiting times for truck drivers as well as potentially improve the traffic's sustainability, against a slight increase in length of stay of containers at the port. INTRODUCTIONFreight transport volumes have been increasing for decades and are expected to more than double by 2050 (ITF 2021). This, combined with containerization, greatly affects the intermodal logistics areas that are at the heart of international freight networks, such as ports and business parks in the hinterland (Behdani et al. 2020). At these locations, a promising solution to increase throughput, and reduce congestion and operational costs is the use of automated vehicles (AVs). AV systems are a type of vehicle-based internal transport system traditionally used in manufacturing plants, distribution centers, container terminals, and other confined environments (Le-Anh and De Koster 2006). Recent advances in technology have increased the popularity of AVs along the logistics chain, where companies can now automate their logistics operations outside of private yards, for example, with AVs shunting containers between terminals in a port area.Currently, research is focused on electric automated vehicles (EAVs) due to increasing sustainability concerns and decarbonization goals (Vdovic et al. 2019). By implementing EAV systems, organizations aim to improve sustainability, flexibility, and efficiency. Furthermore, following the improvements in EAV technology and the increase in freight volumes, the authorities of logistics areas, e.g., ports and business parks, are now looking at scaling up the use of EAV systems for the whole area, thus servicing multiple logistics companies (LCs), e.g., terminals, warehouses, and cross-docking centers. The goal is to share an EAV fleet and coordinate transportation to increase operational efficiency and reduce congestion in logistics areas, thus improving both safety and throughput of goods. By sharing EAVs, the LCs can benefit from economies of scale and risk sharing, e.g., with regards to the cost of EAV ownership. The shift of paradigm

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“…Therefore, the design of efficient ITT operations in the future will pose a considerable challenge for many large seaports. Hu et al [ 2 ] conducted a comprehensive literature review related to the planning of interterminal transport in port areas and the hinterland. The research was motivated by two factors: the limited research related to the integrated planning of ITT between seaport and inland terminals and the limited studies that summarized the research findings and identified the directions for future research regarding ITT.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To provide acceptable service levels for importers and exporters, these infrastructural facilities and services need to be addressed and considered by port authority. Hu et al [ 2 ] stated that a multiterminal system increases the complexity of the container transport process. Figure 1 illustrates the process of unloading and loading containers from/to a ship at a typical modern container terminal.…”

Section: Introductionmentioning

confidence: 99%

Interterminal Truck Routing Optimization Using Deep Reinforcement Learning

Adi

Iskandar

Bae

2020

Sensors

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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.

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Critical Literature Review into Planning of Inter-Terminal Transport: In Port Areas and the Hinterland

Cited by 12 publications

References 75 publications

Strategic Application of Industry 4.0 Technologies in Enhancing Intermodal Transport Terminal Efficiency

Strategic Application of Industry 4.0 Technologies in Enhancing Intermodal Transport Terminal Efficiency

Simulation of the Internal Electric Fleet Dispatching Problem at a Seaport: A Reinforcement Learning Approach

Interterminal Truck Routing Optimization Using Deep Reinforcement Learning

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