摘要
为解决冷轧薄板厂冷轧机组的钢卷拼卷问题,建立了该问题的多目标多背包模型。该模型考虑拼卷方式和最大卷重约束,并将最大化拼卷数和最小化卷重偏差作为模型的评价目标。首先,利用系统聚类法确定钢卷分类和背包中心,简化模型的求解;随后设计一种自适应量子遗传算法,求解每类钢卷的拼卷模型,构造的量子门旋转角度和量子非门变异率根据种群的聚散程度和进化状态自适应调节,在保证算法寻优能力的同时,加快了算法的收敛速度。最后对经典背包问题和实际生产数据进行了仿真,结果表明,所提的模型和算法可行且有效。
To solve the problem of coils merging in the cold rolling mill, a multi-objective and multi-knapsack model was established by considering the merging mode and the maximal available coils weight. The objective of this model was to maximize the number of merging and to minimize the deviation of the coil＇s weight simultaneously. Based on this model, firstly the system clustering method was used to acquire the classification of coils and the knapsack model＇s center so as to simplify the model＇s computation. Then a self-adaptive quantum genetic algorithm was developed to solve the coils merging model, in which the quantum door revolving gate angle and no-door mutation probability were adaptively adjusted according to the state of population＇s concentration-dispersion and evolution. The designed self-adaptive adjustment mechanism ensured the algorithm＇s search ability and speeded up the convergence at the same time. Finally, the simulation results using the classical knapsack problem and practical production data showed that the proposed model and algorithm were feasible and effective.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2009年第7期1421-1429,共9页
Computer Integrated Manufacturing Systems
基金
国家863计划资助项目(2007AA04Z156)~~
关键词
冷轧机组
多背包问题
系统聚类法
自适应量子遗传算法
cold rolling mill
multi-knapsack problem
system clusters method
adaptive quantum genetic algorithm
