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针对钢管入库优化决策问题,建立了问题的约束满足优化模型,并通过对垛高和钢管堆放规则的分析,提出了基于聚类和约束满足技术的两阶段求解算法.算法在第一阶段采用聚类的方式对待入库的钢管按照多重属性进行分组;在第二阶段利用约束满足技术对于每组钢管分别指派垛位及其在垛位上的具体位置,并通过约束传播动态缩减问题的搜索空间.最后将算法与经典的BFD(best fit deceasing)算法进行实验结果对比.实验结果表明,算法能够在保证倒垛次数最小的前提下,有效减少垛位数并具有良好的垛位利用率,模型及算法可行、有效.
Aiming at the optimization decision-making problem of steel storage, a constrained satisfaction optimization model was established, and a two-stage solving algorithm based on clustering and constraint satisfaction technology was proposed by analyzing stackability and steel pipe stacking rules.The algorithm was adopted in the first stage Clustering the steel pipe to be warehoused according to multiple attributes; using the constraint satisfaction technique in the second stage to assign the pile positions and the specific positions on the pile positions respectively for each group of steel pipes, and searching through the constraint propagation dynamic reduction problem Space.Finally, the algorithm is compared with the classical best fit deceasing (BFD) algorithm.Experimental results show that the algorithm can effectively reduce the number of palletizing and have a good piling utilization under the premise of minimizing the number of palletizing, Models and algorithms are feasible and effective.