Integrated scheduling optimization of U-shaped automated container terminal under loading and unloading mode

Xu, Baoyun; Jie, Depei; Li, Junjun; Yang, Yongsheng; Wen, Furong; Song, Haitao

doi:10.1016/j.cie.2021.107695

Cited by 50 publications

(18 citation statements)

References 22 publications

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“…For new technologies, Yue et al (2021) proposed a scheduling problem of the DTQCs and vehicles, with the consideration of the capacity of the transfer platform. Xu et al (2021a) looked into the integrated scheduling between DTQC, vehicles and YCs. To our best knowledge, there is only one study looking into the management problem of the MTSC, which is a standard scheduling research solved with solver, heuristic algorithm and branch-and-price algorithm ( Abou Kasm and Diabat, 2020 ).…”

Section: Trends In Emerging Technology and Management Researchmentioning

confidence: 99%

Emerging technology and management research in the container terminals: Trends and the COVID-19 pandemic impacts

Zhou¹,

Zhu²,

Bell³

et al. 2022

Ocean & Coastal Management

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Section: Trends In Emerging Technology and Management Researchmentioning

confidence: 99%

Emerging technology and management research in the container terminals: Trends and the COVID-19 pandemic impacts

Zhou¹,

Zhu²,

Bell³

et al. 2022

Ocean & Coastal Management

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“…Conversely, one may just lay out a sequence of such actions and optimize said sequence [13]. Or one could resort to ideas from diverse fields, such as those from reinforcement learning [14,15], or others [16][17][18][19][20][21]. Of course, there are other kinds of HHs that do not select among existing solvers, such as those that generate a new solver altogether [10,11,22].…”

Section: Motivation and Significancementioning

confidence: 99%

“…The authors developed a parameter-free HH for software testing, and they achieved good results with respect to existing strategies. Furthermore, Xu et al proposed a HH for a relatively complex real-life problem [15]. They considered the integrated scheduling of several cranes and automated vehicles for loading and unloading tasks and a U-shaped container.…”

Section: Motivation and Significancementioning

confidence: 99%

MatHH: A Matlab-based Hyper-Heuristic framework

2022

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“…It can be seen that X contains 5 elements, and elements in particle θ are angles between X and [1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1]. The index number of cos θ sorted from small to large becomes [5,2,1,4,3]. According to the reference decision value M, X becomes [13, 9, 14, 16, 1] after mapping.…”

Section: Particle Mapping Spacementioning

confidence: 99%

“…It was applied in the Qinzhou Port of Beibu Gulf Port Co., Ltd. [2]. Figure 2 shows the layout of the new U-shape trafficked automated terminal [3]. It is mainly optimized for the yard.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Scheduling for Multi-Equipment at U-Shape Trafficked Automated Terminal Based on Chaos Particle Swarm Optimization

Yang

et al. 2021

JMSE

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Aimed to improve the efficiency of port operations, Shanghai Zhenhua Heavy Industries Co., Ltd. (ZPMC) proposed a new U-shape trafficked automated terminal. The new U-shape trafficked automated terminal brings a new hybrid scheduling problem. A hybrid scheduling model for yard crane (YC), AGV and ET in the U-shape trafficked automated terminal yard is established to solve the problem. The AGV and ET yard lanes are assumed to be one-way lane. Take the YC, AGV and ET scheduling results (the container transportation sequences) as variables and the minimization of the maximum completion time as the objective function. A scheduling model architecture with hierarchical abstraction of scheduling objects is proposed to refine the problem. The total completion time is solved based on a static and dynamic mixed scheduling strategy. A chaotic particle swarm optimization algorithm with speed control (CCPSO) is proposed, which include a chaotic particle strategy, a particle iterative speed control strategy, and a particle mapping space for hybrid scheduling. The presented model and algorithm were applied to experiments with different numbers of containers and AGVs. The parameters of simulation part refer to Qinzhou Port. The simulation results show that CCPSO can obtain a near-optimal solution in a shorter time and find a better solution when the solution time is sufficient, comparing with the traditional particle swarm optimization algorithm, the adaptive particle swarm optimization algorithm and the random position particle swarm optimization algorithm.

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Integrated scheduling optimization of U-shaped automated container terminal under loading and unloading mode

Cited by 50 publications

References 22 publications

Emerging technology and management research in the container terminals: Trends and the COVID-19 pandemic impacts

Emerging technology and management research in the container terminals: Trends and the COVID-19 pandemic impacts

MatHH: A Matlab-based Hyper-Heuristic framework

Hybrid Scheduling for Multi-Equipment at U-Shape Trafficked Automated Terminal Based on Chaos Particle Swarm Optimization

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