Modelling and solution approaches to the multi-load AGV dispatching problem in container terminals

Klerides, Evelina; Hadjiconstantinou, Eleni

doi:10.1057/mel.2011.22

Cited by 14 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results show that this method can minimize the maximum completion time and improve the efficiency of the terminal AGVs horizontal transportation operation. Klerides E. et al [15] established a model to minimize the cost of AGVs scheduling and proposed a rolling time method. Through the analysis of port cases of different scales, the method of rolling time method was verified.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(1) The number of container tasks considered in each episode |N| ∈ [50, 500], where 50-100 containers are considered for the small-scale problem and 100-500 containers for the large-scale problem; the number of QCs on the sea side |Q| ∈ [2, 8] and the number of YCs on the land side |Y| ∈ [4,10]; and the number of AGVs |V| ∈ [5,15] are considerations of this study. 4) According to the containers and the task sequence arranged before the ship berthing, the earliest possible event time for each container task is set.…”

Section: Experimental Parameters Settingmentioning

confidence: 99%

See 1 more Smart Citation

Scheduling of AGVs in Automated Container Terminal Based on the Deep Deterministic Policy Gradient (DDPG) Using the Convolutional Neural Network (CNN)

Chen

Wang

2021

JMSE

View full text Add to dashboard Cite

In order to improve the horizontal transportation efficiency of the terminal Automated Guided Vehicles (AGVs), it is necessary to focus on coordinating the time and space synchronization operation of the loading and unloading of equipment, the transportation of equipment during the operation, and the reduction in the completion time of the task. Traditional scheduling methods limited dynamic response capabilities and were not suitable for handling dynamic terminal operating environments. Therefore, this paper discusses how to use delivery task information and AGVs spatiotemporal information to dynamically schedule AGVs, minimizes the delay time of tasks and AGVs travel time, and proposes a deep reinforcement learning algorithm framework. The framework combines the benefits of real-time response and flexibility of the Convolutional Neural Network (CNN) and the Deep Deterministic Policy Gradient (DDPG) algorithm, and can dynamically adjust AGVs scheduling strategies according to the input spatiotemporal state information. In the framework, firstly, the AGVs scheduling process is defined as a Markov decision process, which analyzes the system’s spatiotemporal state information in detail, introduces assignment heuristic rules, and rewards the reshaping mechanism in order to realize the decoupling of the model and the AGVs dynamic scheduling problem. Then, a multi-channel matrix is built to characterize space–time state information, the CNN is used to generalize and approximate the action value functions of different state information, and the DDPG algorithm is used to achieve the best AGV and container matching in the decision stage. The proposed model and algorithm frame are applied to experiments with different cases. The scheduling performance of the adaptive genetic algorithm and rolling horizon approach is compared. The results show that, compared with a single scheduling rule, the proposed algorithm improves the average performance of task completion time, task delay time, AGVs travel time and task delay rate by 15.63%, 56.16%, 16.36% and 30.22%, respectively; compared with AGA and RHPA, it reduces the tasks completion time by approximately 3.10% and 2.40%.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Experimental Parameters Settingmentioning

confidence: 99%

Scheduling of AGVs in Automated Container Terminal Based on the Deep Deterministic Policy Gradient (DDPG) Using the Convolutional Neural Network (CNN)

Chen

Wang

2021

JMSE

View full text Add to dashboard Cite

show abstract

“…Most of the authors proposed simulation models to conduct a performance analysis of the proposed dispatching approaches. In some papers, an MIP model is developed but again, the performances were tested under different scenarios using simulation models (Klerides and Hadjiconstantinou 2011).…”

Section: Yard Operations: Yc Scheduling Transport Vehicle Dispatchinmentioning

confidence: 99%

A comprehensive review of quay crane scheduling, yard operations and integrations thereof in container terminals

Kızılay

Eliiyi

2020

Flex Serv Manuf J

View full text Add to dashboard Cite

Over the past decades, container transportation has achieved considerable growth, and maritime trade now constitutes 80% of the global trade. The vessel sizes increased in parallel, up to 21,400 TEU (Twenty-foot-equivalent unit container). Accordingly, global containerized trade reached up to 150 million TEU in 2017 (UNCTAD 2018). This growth brings the need to use scientific methods to manage and operate container terminals more economically throughout the globe. In order to manage container transshipment and to use large vessels efficiently, the docking time at the container port for each vessel should be minimized. The decrease in the docking time enables the vessel to move to its next destination faster, decreasing turnover time and facilitating more containers to be transported. Container terminals can be divided into five main areas as the berth, the quay, the storage yard, the transport area, and the gate. The vessels must be berthed in suitable positions, after which many containers have to be unloaded or loaded via quay cranes, transshipped by vehicles inside the terminal, and stacked by yard cranes to suitable positions, all by using expensive equipment. With the invention of new technologies, the bottleneck at the berth side is almost overcome; however, the yard and the quayside operations have to be further perfected to obtain efficient plans. In this comprehensive literature review study, we aim to combine the literature on both yard and quayside operations, carefully examining independently studied problems as well as integrated ones. General information about port operations and relevant literature is provided. For the quayside, the literature on quay crane assignment and scheduling problems is investigated, whereas, for the yard side, yard crane scheduling, transport vehicle dispatching and scheduling, vehicle routing and traffic control, and storage location and space planning problems are reviewed in depth. In addition to these individual problems, their integrations are also analyzed, relevant publications and their respective contributions are explained in detail. Besides the milestone papers that lead the literature on container terminals, recent publications and advances are also reviewed, and managerial insights and future research directions are identified.

show abstract

“…Grunow et al (2004) proposed a novel heuristic dispatching algorithm for a fleet of multi-load (carrying more than one container) AGVs to minimise the total lateness of AGVs. Klerides and Hadjiconstantinou (2011) also considered the multi-load situation, and a mathematical optimisation model was proposed under a rolling horizon approach. Apart from the deterministic models that were addressed above, Angeloudis and Bell (2010) provided a flexible dispatching algorithm for real-time control of AGVs under various conditions of uncertainty, such as the uncertain waiting time at the quay cranes for AGVs.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling of integrated vehicle scheduling and container storage problems in unloading process at an automated container terminal

Luo

Mendes

2016

Computers & Industrial Engineering

View full text Add to dashboard Cite

Modelling and solution approaches to the multi-load AGV dispatching problem in container terminals

Cited by 14 publications

References 10 publications

Scheduling of AGVs in Automated Container Terminal Based on the Deep Deterministic Policy Gradient (DDPG) Using the Convolutional Neural Network (CNN)

Scheduling of AGVs in Automated Container Terminal Based on the Deep Deterministic Policy Gradient (DDPG) Using the Convolutional Neural Network (CNN)

A comprehensive review of quay crane scheduling, yard operations and integrations thereof in container terminals

Modelling of integrated vehicle scheduling and container storage problems in unloading process at an automated container terminal

Contact Info

Product

Resources

About