Yard crane and AGV scheduling in automated container terminal: A multi-robot task allocation framework

Chen, Xuchao; He, Shiwei; Zhang, Yongxiang; Lu, Tong; Shang, Pan; Zhou, Xuesong

doi:10.1016/j.trc.2020.02.012

Cited by 134 publications

(46 citation statements)

References 90 publications

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“…Second, the MILP model and the heuristic algorithm GSAHA proposed in this paper can be further developed to consider different station types, such as the terminal station where trains need to perform the turnaround movement that makes the train platforming problem more complicated [42,43]. ird, the train movements at the stations can be viewed as a set of space-time paths, and thus more efficient dual decomposition methods could be developed accordingly [7,44,45]. Fourth, more efficient solution algorithms can be developed for the simultaneous reoptimization of several interconnected railway stations in a railway line or even a regional railway network with more complicated structure so that the joint optimization of delay situations and train timetabling problems can be achieved [8,19,27,[46][47][48][49].…”

Section: Discussionmentioning

confidence: 99%

A Fast Approach for Reoptimization of Railway Train Platforming in Case of Train Delays

Zhang

Zhong

Yin

et al. 2020

Journal of Advanced Transportation

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Train platforming is critical for ensuring the safety and efficiency of train operations within the stations, especially when unexpected train delays occur. This paper studies the problem of reoptimization of train platforming in case of train delays, where the train station is modeled using the discretization of the platform track time-space resources. To solve the reoptimization problem, we propose a mixed integer linear programming (MILP) model, which minimizes the weighted sum of total train delays and the platform track assignment costs, subject to constraints defined by operational requirements. Moreover, we design an efficient heuristic algorithm to solve the MILP model such that it can speed up the reoptimization process with good solution precision. Furthermore, a real-world case is taken as an example to show the efficiency and effectiveness of the proposed model and algorithm. The computational results show that the MILP model established in this paper can describe the reoptimization of train platforming accurately, and it can be solved quickly by the proposed heuristic algorithm. In addition, the model and algorithm developed in this paper can provide an effective computer-aided decision-making tool for the train dispatchers in case of train delays.

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Section: Discussionmentioning

confidence: 99%

A Fast Approach for Reoptimization of Railway Train Platforming in Case of Train Delays

Zhang

Zhong

Yin

et al. 2020

Journal of Advanced Transportation

Self Cite

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“…Besides, some studies have considered the setting of multiple cranes sharing one stacking [38]. For AGV scheduling problems, most researchers focused on seeking the optimized assignment and routing plan for vehicles [39]. Chen et al [40] proposed a method based on the computer vision technology to extract vehicle trajectory efficiently and accurately from video and enrich more trajectory data sets under traffic conditions for traffic flow research.…”

Section: Related Workmentioning

confidence: 99%

Automated Container Terminal Production Operation and Optimization via an AdaBoost-Based Digital Twin Framework

Chang

Gao

et al. 2021

Journal of Advanced Transportation

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Digital twin (DT), machine learning, and industrial Internet of things (IIoT) provide great potential for the transformation of the container terminal from automation to intelligence. The production control in the loading and unloading process of automated container terminals (ACTs) involves complex situations, which puts forward high requirements for efficiency and safety. To realize the real-time optimization and security of the ACT, a framework integrating DT with the AdaBoost algorithm is proposed in this study. The framework is mainly composed of physical space, a data service platform, and virtual space, in which the twin space and service system constitute virtual space. In the proposed framework, a multidimensional and multiscale DT model in twin space is first built through a 3D MAX and U3D technology. Second, we introduce a random forest and XGBoost to compare with AdaBoost to select the best algorithm to train and optimize the DT mechanism model. Third, the experimental results show that the AdaBoost algorithm is better than others by comparing the performance indexes of model accuracy, root mean square error, interpretable variance, and fitting error. In addition, we implement empirical experiments by different scales to further evaluate the proposed framework. The experimental results show that the mode of the DT-based terminal operation has higher loading and unloading efficiency than that of the conventional terminal operation, increasing by 23.34% and 31.46% in small-scale and large-scale problems, respectively. Moreover, the visualization service provided by the DT system can monitor the status of automation equipment in real time to ensure the safety of operation.

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“…Therefore, the terminal operators tend to optimize QCs' performance as much as possible. YCs also affect container terminal operation and improve terminals' productivity [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Improvement of Container Terminal Productivity with Knowledge about Future Transport Modes: A Theoretical Agent-Based Modelling Approach

Mazloumi

Hassel

2021

Sustainability

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Despite all the achievements in improving container terminal performance in terms of equipment and container stacking systems (CSS), terminal operators are still facing several challenges. One of these challenges is the lack of information about further transportation modes of the container, which leads to extra movements of the container inside the stacking area. Hence, we aimed to examine factors that affect container handling processes and to evaluate a container terminal’s overall equipment effectiveness. This study used data from a container terminal at the Port of Antwerp, Belgium. An agent-based model was developed based on a block-stacking strategy to investigate two scenarios: (1) having information about further transportation modes and (2) a base scenario. The Overall Equipment Effectiveness Index (OEE) was also adopted to evaluate the container terminal’s effectiveness in both scenarios. Results showed that having information on further transportation mode significantly increased the container outflow, and the OEE index improved compared to the base scenario’s results. Therefore, we recommend an integrated data-sharing system where all the stakeholders can share their information with no fear of losing their market share.

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Yard crane and AGV scheduling in automated container terminal: A multi-robot task allocation framework

Cited by 134 publications

References 90 publications

A Fast Approach for Reoptimization of Railway Train Platforming in Case of Train Delays

A Fast Approach for Reoptimization of Railway Train Platforming in Case of Train Delays

Automated Container Terminal Production Operation and Optimization via an AdaBoost-Based Digital Twin Framework

Improvement of Container Terminal Productivity with Knowledge about Future Transport Modes: A Theoretical Agent-Based Modelling Approach

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