Approximate the scheduling of quay cranes with non-crossing constraints

Zhang, An; Zhang, Wenshuai; Chen, Yong; Chen, Guangting; Chen, Xufeng

doi:10.1016/j.ejor.2016.10.021

Cited by 24 publications

(16 citation statements)

References 18 publications

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“…To stabilize the process, many control solutions have been developed over the years, yet they all have downfalls related to the complexity of the dynamics of the ship, the quay crane, the trucks, and all other means of transportation [ 46 , 47 , 48 ]. Optimization of the handling processes is a priority for all logistics companies and it is certainly a hot topic in the current literature [ 49 , 50 , 51 , 52 ], yet optimization of container handling processes only rarely takes into account the damage perspective [ 8 ], which relates to the physical impacts of containers and the surrounding infrastructure. On the other hand, novel data analytics methods are being developed along with the rising popularity of embedded IoT devices [ 53 ], which are capable of delivering adequate solutions for decision-critical operations [ 54 ].…”

Section: Review Of Recent Advancementsmentioning

confidence: 99%

Detecting Shipping Container Impacts with Vertical Cell Guides inside Container Ships during Handling Operations

Jakovlev

Eglynas

Vozňák

et al. 2022

Sensors

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Due to the mechanical nature of container handling operations, as well as natural factors, container and handling infrastructure suffers various types of damage during use, especially within the tight and enclosed environments of a ship’s hull. In this operational environment, it is critical to detect any sort of physical impacts between the vertical cell guides of the ship’s hull and the container. Currently, an inspection of impacts and evaluation of any consequences is performed manually, via visual inspection processes. This process is time-consuming and relies on the technical expertise of the personnel involved. In this paper, we propose a five-step impact-detection methodology (IDM), intended to detect only the most significant impact events based on acceleration data. We conducted real measurements in a container terminal using a sensory device placed on the spreader of the quay crane. The proposed solution identified an average of 12.8 container impacts with the vertical cell guides during common handling operations. In addition, the results indicate that the presented IDM can be used to recognize repeated impacts in the same space of each bay of the ship, and can be used as a decision support tool for predictive maintenance systems.

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Section: Review Of Recent Advancementsmentioning

confidence: 99%

Detecting Shipping Container Impacts with Vertical Cell Guides inside Container Ships during Handling Operations

Jakovlev

Eglynas

Vozňák

et al. 2022

Sensors

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“…Chang et al [18] studied the quay crane allocation under a dynamic strategy. Zhang et al [19] considered the non-crossing constraint of quay cranes. The objective was to minimize the completion time of a vessel.…”

Section: Loading Operation Planningmentioning

confidence: 99%

Collaborative optimization for loading operation planning and vessel traffic scheduling in dry bulk ports

Zhang

Yang

et al. 2022

Advanced Engineering Informatics

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“…Here, 0 refers to a virtual bay representing [27] No No DTIP Simulated annealing Ng and Mak [18] No No IP Branch-and-bound algorithm Gharehgozli et al [19] No No IP Branch-and-bound algorithm Ng [23] Yes No IP Dynamic program-based heuristic Galle et al [21] Yes No IP Approximate algorithm Li et al [22] Yes No MILP Rolling horizon heuristics He et al [26] Yes No MILP GA and particle swarm algorithm Briskorn et al [30] Yes No MILP Bucket brigade algorithm Sha et al [25] Yes No MILP Scheduling algorithm Zhang et al [34] Yes No MILP Approximate approach Sharif et al [20] Yes No Simulation ABM with DP Fotuhi et al [24] Yes No Simulation ABM; reinforcement learning Briskorn and Angeloudis [29] Yes No Simulation Graphical model and polynomial algorithms Ozcan and Eliiyi [31] Yes No Simulation Reward-based stacking algorithm Kress et al [32] Yes No Simulation Bounded DP Jaehn and Kress [33] Yes Yes DTIP Bucket brigade algorithm Carlo and Martínez-Acevedo [8] Yes Yes MILP Priority rules Gharehgozli et al [14] Yes Yes Simulation Priority and operation rules e container carried across the handshake bay is handled by the twin ASCs sequentially: the crane p carries the container from its original location to the handshake bay, and then, the crane c takes it from the handshake bay to the destination.…”

Section: Problem Descriptionmentioning

confidence: 99%

Positioning a Handshake Bay for Twin Stacking Cranes in an Automated Container Terminal Yard Block

Tian

Yin

et al. 2022

Journal of Advanced Transportation

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At automated container terminals (ACTs), twin automated stacking cranes (ASCs) can carry out the tasks—store and retrieve containers simultaneously in a yard block using a handshake bay, where a primary ASC stacks the container at the handshake bay and the other crane carries it to the destination bay. Although the handshake bay increases the degree of crane utilization, the ASCs will interfere with each other at the bay, decreasing the stacking efficiency. This study formulates a mixed-integer linear program (MILP) to position the handshake bay and simultaneously schedule the twin ASCs to minimize the tasks’ makespan. The proposed formulation considers the safe time interval to avoid crane collisions during adjacent crane movements. To solve the model, we developed a random-key genetic algorithm with a priority-based decoding scheme to optimize the task sequences and tasks assigned to the cranes. The priority-based GA can always generate feasible solutions by ranking the container-handling tasks. Numerical experiments prove that the safe temporal interval affects the makespan and the handshake bay’s position. An optimal handshake bay reduces 35% of the makespan compared with a nonoptimal bay, and the proposed algorithm is competitive compared with the on-the-shelf MILP solver and can solve medium- and large-scale instances in short computing time with gaps lower than 5% compared with ideal solutions.

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Approximate the scheduling of quay cranes with non-crossing constraints

Cited by 24 publications

References 18 publications

Detecting Shipping Container Impacts with Vertical Cell Guides inside Container Ships during Handling Operations

Detecting Shipping Container Impacts with Vertical Cell Guides inside Container Ships during Handling Operations

Collaborative optimization for loading operation planning and vessel traffic scheduling in dry bulk ports

Positioning a Handshake Bay for Twin Stacking Cranes in an Automated Container Terminal Yard Block

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