Yard crane dispatching to minimize vessel turnaround times in container terminals

Huang, Shell Ying; Li, Ya; Guo, Xiaojun

doi:10.1109/wsc.2014.7020024

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Subsequent investigations by [6] revealed the effectiveness of particle swarm optimization combined with genetic algorithm operators in solving the same problem, particularly on smaller instances. Additionally, [20,21] extended the optimal search strategy introduced in [7] to tardiness related objectives for single YC scheduling, demonstrating superiority in minimizing vessel turnaround times through simulation studies.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Online Yard Crane Scheduling through a Two-Stage Rollout Memetic Genetic Programming

Jin,

Bai,

Zhou

et al. 2024

Preprint

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Over the past decade, the surge in global container port throughput has heightened the demand for terminal efficiency, with the container yard operations being central to the overall port performance. However, the unpredictable arrival of external trucks poses significant challenges for yard cranes, requiring the simultaneous scheduling of internal and external loading tasks.Traditional yard crane scheduling methods often rely on outdated assumptions that fail to account for the dynamic impact of external tasks.In response, container terminals increasingly model the yard crane scheduling as an online problem. A notable advancement in online scheduling is the online rollout method, which evaluates the decisions based on the potential outcomes of their future rollout solutions rather than immediate priorities. Although this method outperforms the latter approach, it faces two main issues: the rollout is time consuming, and decisions based solely on objective of rollout solution may not align with long-term scheduling objectives.To overcome these limitations, we have developed a two-stage adaptive rollout decision model. In the first stage, less desirable tasks are dynamically filtered out to reduce the number of rollouts, while the second stage employs a genetic programming evolved evaluation functions to infuse forward-looking insights into the scheduling process. This approach has proven to significantly enhance yard scheduling efficiency and performance, as confirmed by experimental validation. Given the dynamic nature of yard crane operations, we believe this method could be beneficially applied to other dynamic scheduling contexts.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Online Yard Crane Scheduling through a Two-Stage Rollout Memetic Genetic Programming

Jin,

Bai,

Zhou

et al. 2024

Preprint

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“…Therefore, scheduling YCs alone with the objectives to minimizing vehicle waiting time or YC makespan may not produce a YC operation plan well-synchronized with the QCs. Huang et al (2014) presented an algorithm MMT-RBA* for YC scheduling to minimize the maximum job tardiness of a vessel in order to minimize the vessel's turnaround time. MMT-RBA* uses the Recursive Backtracking algorithm with a bound as in A* search to Minimize Maximum Tardiness of jobs.…”

Section: Scheduling Ycs Alonementioning

confidence: 99%

Yard crane scheduling to minimize total weighted vessel loading time in container terminals

Huang

2017

Flex Serv Manuf J

Self Cite

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One of the most important objectives for container terminals is to minimize the total weighted turnaround time for vessels. Minimizing total weighted vessel loading time directly helps minimize total weighted vessel turnaround time. In this paper we consider the yard crane (YC) scheduling problem in a multi-vessel loading process. We propose a YC scheduling system that minimizes the total weighted maximum tardiness of loading jobs. Our experiments show that this is significantly more effective in the minimization of the total weighted vessel loading time than the existing YC scheduling methods that minimize job waiting time or makespan. Our scheduling system also has the ability to dynamically optimize the YC operations with the consideration of the maximum tardiness already incurred to each vessel earlier in the loading process. To evaluate against our YC scheduling system, an optimal algorithm to minimize makespan (useful if there is YCs shortage) and an optimal algorithm to minimize total weighted job tardiness (common in manufacturing) are also proposed.

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Enhancing online yard crane scheduling through a two-stage rollout memetic genetic programming

Jin,

Bai,

Zhou

et al. 2024

Memetic Comp.

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Yard crane dispatching to minimize vessel turnaround times in container terminals

Cited by 4 publications

References 19 publications

Enhancing Online Yard Crane Scheduling through a Two-Stage Rollout Memetic Genetic Programming

Enhancing Online Yard Crane Scheduling through a Two-Stage Rollout Memetic Genetic Programming

Yard crane scheduling to minimize total weighted vessel loading time in container terminals

Enhancing online yard crane scheduling through a two-stage rollout memetic genetic programming

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