Dynamic Optimization Strategy of Large Airport Cargo Location based on Virus Evolutionary Genetic Algorithm
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Published:2022-03-15
Issue:1
Volume:70
Page:73-83
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ISSN:1582-5175
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Container-title:Electrotehnica, Electronica, Automatica
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language:
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Short-container-title:EEA
Author:
Jiandong Qiu , ,Kaiyue Zhang ,Minan Tang , ,
Abstract
The automated stereoscopic warehouse of large airport plays an important role in logistics, in which cargo access efficiency is the most important part. And cargo location optimization is an effective method to improve its efficiency. After comparing and analysing the structure and working characteristics of bulk cargo processing system in large airport cargo station, the dynamic optimization problem of the cargo location was modelled. The virus evolutionary genetic algorithm (VEGA) was selected for optimization simulation, and the time-consuming rule was designed according to the actual optimization conditions. A cargo location numbering rule based on time-consuming rule was designed according to the actual optimization conditions. Simulation results show that both the convergence and calculating speeds of the VEGA have been obviously improved compared with those of the traditional genetic algorithm, which can meet the actual needs of the field better.
Publisher
Editura Electra
Subject
Electrical and Electronic Engineering,Control and Systems Engineering
Reference21 articles.
1. "[1] YANG W., LIU J., YUE T. et al., Integrated optimization of cargo location distribution and job scheduling in multi-carrier automated stereoscopic warehouse, in: Computer Integrated Manufacturing Systems, 2019, vol. 25, no. 7, pp. 247-255, ISSN 1006-5911. 2. [2] RAMS H., SCHÖBERL M., SCHLACHER K., Optimal motion planning and energy-based control of a single mast stacker crane, in: IEEE Transactions on Control Systems Technology, 2018, vol. 26, no. 4, pp. 1449-1457, ISSN 1063-6536. 3. [3] YAN B., YAN C., LONG F. et al., Multi-objective optimization of electronic product goods location assignment in stereoscopic warehouse based on adaptive genetic algorithm, in: Journal of Intelligent Manufacturing, 2018, vol. 29, no. 6, pp. 1273-1285, ISSN 0956-5515. 4. [4] SHANG X., YANG K., WANG W. et al., Stochastic hierarchical multimodal hub location problem for cargo delivery systems: formulation and algorithm, in: IEEE Access, 2020, vol. 8, pp. 55076-55090, ISSN 2169-3536. 5. [5] DING F., SONG X. J., Application of hybrid particle swarm algorithm in ETV scheduling optimization, in: Computer Applications and Software, 2019, vol. 36, no. 8, pp. 262-267+316, ISSN 1000-386X.
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