王素欣1,2,温恒1,2,卢福强1,2,刘浩伯1,2,王雷震1,2.货物三维装箱问题建模及其乌鸦搜索算法优化[J].湖南大学学报:自然科学版,2020,(8):21~30
货物三维装箱问题建模及其乌鸦搜索算法优化
Modeling of 3D Cargo Loading Problem andOptimization of Crow Search Algorithm
  
DOI:
中文关键词:  三维装箱问题  集装箱装载问题  乌鸦搜索算法  转弯重心约束  集装箱包装公司  优化与决策
英文关键词:three-dimensional bin packing problem  container loading problem  crow search algorithm  center of gravity constraint in turning  container packaging corp  optimizaton and decision
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作者单位
王素欣1,2,温恒1,2,卢福强1,2,刘浩伯1,2,王雷震1,2 (1. 东北大学秦皇岛分校 控制工程学院河北 秦皇岛 066004 2. 东北大学 信息科学与工程学院辽宁 沈阳 110819) 
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中文摘要:
      针对货物三维装箱问题建立三维装箱模型. 在模型中,为避免货物在运输过程中转弯时由于偏心导致翻车现象的发生,加入了考虑转弯时重心约束,得到重心区域投影为等腰三角形或者等腰梯形. 货物放置规则中扩大了剩余空间区域,增加了解的多样性. 在算法中,为了提高迭代收敛速度,增强其全局寻优的能力,采用改进的乌鸦搜索算法对模型进行求解与优化. 在改进算法中,提出并引入了多概率随机游走策略和解修复策略. 解修复策略使得算法适用于模型求解,尽可能增加解的多样性. 多概率随机游走策略是种群迭代后继续以多种不同的概率进行随机游走,使得算法全局寻优能力更强. 仿真实例与基准函数测试结果表明,改进后的算法优化效果明显.
英文摘要:
      Aiming at the three-dimensional bin loading problem of cargo, a three-dimensional cargo loading model is established. In the model, in order to avoid the phenomenon of rolling over due to the eccentricity during the turn of the goods in the process of transportation, the gravity center constraint during the turn was added to obtain the projection of the gravity center area as an isosceles triangle or isosceles trapezoid. The cargo placement rules expand the remaining space area and increase the diversity of understanding. In order to improve the speed of iterative convergence and enhance its global optimization ability, an improved crow search algorithm is adopted to solve and optimize the model. In the improved algorithm, a multi-probability random walk strategy and a reconciliation strategy are proposed and introduced. The solution repair strategy makes the algorithm suitable for model solving and increases the diversity of solutions as much as possible. The multi-probability random walk strategy is to continue to walk randomly with different probabilities after population iteration, which makes the global optimization ability of the algorithm stronger. Simulation examples and benchmark function test results show that the improved algorithm has obvious optimization effect.
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