The impact of incomplete vessel arrival information on container stacking

He, Yuyan; Ai-hu, Wang; Su, Hailiang

doi:10.1080/00207543.2019.1686188

Cited by 14 publications

(4 citation statements)

References 30 publications

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“…To accomplish these unpredictable events, the authors proposed a reactive heuristic strategy that takes the baseline schedule as the reference schedule and minimizes the recovery cost. In [14] the effect of the arrival uncertainty on the container stacking is studied, and different heuristics are proposed accordingly to the stacking principles and with levels of vessel arrival information. Iris and Lam [16] proposed a mathematical model for the BACQAP under vessel arrival and handling times uncertain with both proactive and reactive properties.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

An exact robust approach for the integrated berth allocation and quay crane scheduling problem under uncertain arrival times

Rodrigues

Agra

2021

European Journal of Operational Research

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Section: Literature Review and Related Workmentioning

confidence: 99%

An exact robust approach for the integrated berth allocation and quay crane scheduling problem under uncertain arrival times

Rodrigues

Agra

2021

European Journal of Operational Research

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“…MATLAB, 2015version, launched by MathWorks Inc., is used, and the test is conducted on a Windows 32-bit operating system. In this section, we evaluate the proposed AFSA approach and compare it with the classical genetic algorithm (GA) and particle swarm optimization (PSO) from previous work [4,27]. According to the evaluation function based on the ideal point method, the paper makes the following arrangement.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Optimizing the Cargo Location Assignment of Retail E-Commerce Based on an Artificial Fish Swarm Algorithm

Zhang

Chen

et al. 2020

Mathematical Problems in Engineering

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An efficient storage strategy for retail e-commerce warehousing is important for minimizing the order retrieval time to improve the warehouse-output efficiency. In this paper, we consider a model and algorithm to solve the cargo location problem in a retail e-commerce warehouse. The problem is abstracted into storing cargo on three-dimensional shelves, and the mathematical model is built considering three objectives: efficiency, stability, and classification. An artificial swarm algorithm is designed to solve the proposed models. Computational experiments performed on a warehouse show that the proposed approach is effective at solving the cargo location assignment problem and is significant for the operation and organization of a retail e-commerce warehouse.

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“…A new unified integer programming model was designed to solve the problem of reducing the number of containers unloaded. In addition to the weight and shipping order, the uncertainty of delivery time [4], exit box entry time [5], [6], and pick-up time [7], [8] are also critical factors that affect unloading operations. Besides the number of box dumps, the task allocation of the bridge [9] and moving path [10] also affect the box allocation strategy.…”

Section: Introductionmentioning

confidence: 99%

“…Many algorithms to solve bin allocation exist, such as the heuristic algorithm [6], [12] particle swarm algorithm [13], tabu search algorithm [14], mixed harmony simulated annealing algorithm [10], [15], and genetic algorithm [9], [16], [17]. Among these, the genetic algorithm is used more widely used.…”

Section: Introductionmentioning

confidence: 99%

Dangerous Goods Container Location Allocation Strategy based on Improved NSGA-II Algorithm

Zhang¹,

Liang²,

Chen³

2022

IJACSA

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The characteristics of port dangerous goods are complicated and diverse in danger, which is very likely to cause chain effects once a fire and explosion accident occurs. Based on the distribution characteristics of dangerous goods container yards and the special national storage requirements for dangerous goods containers, the paper establishes a multiobjective optimization model with a double priority of safety and economy, starting from reducing the number of reversals. The improved non-dominated sorting genetic algorithm based on the elite strategy was used to solve the model and the algorithm was tested and improved. Based on the Pareto optimal solution set, the entropy weight-TOPSIS method was used to optimize the sorting of multiple solution sets, which improved the performance of the algorithm. The analysis further clarifies the important relationship between attributes, and the running time is shortened by 85.7% compared with the traditional NSGA algorithm. The optimization model and algorithm can provide decision support for the actual operation and management of container storage, and provide a good reference for accident risk prevention and control.

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The impact of incomplete vessel arrival information on container stacking

Cited by 14 publications

References 30 publications

An exact robust approach for the integrated berth allocation and quay crane scheduling problem under uncertain arrival times

An exact robust approach for the integrated berth allocation and quay crane scheduling problem under uncertain arrival times

Optimizing the Cargo Location Assignment of Retail E-Commerce Based on an Artificial Fish Swarm Algorithm

Dangerous Goods Container Location Allocation Strategy based on Improved NSGA-II Algorithm

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