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张秀芬, 蔚刚, 王磊, 萨日娜. 支持复杂产品并行拆卸序列规划的遗传算法[J]. 计算机辅助设计与图形学学报, 2015, 27(7): 1327-1333.
引用本文: 张秀芬, 蔚刚, 王磊, 萨日娜. 支持复杂产品并行拆卸序列规划的遗传算法[J]. 计算机辅助设计与图形学学报, 2015, 27(7): 1327-1333.
Zhang Xiufen, Yu Gang, Wang Lei, Sa Rina. Parallel Disassembly Sequence Planning for Complex Products Based on Genetic Algorithm[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(7): 1327-1333.
Citation: Zhang Xiufen, Yu Gang, Wang Lei, Sa Rina. Parallel Disassembly Sequence Planning for Complex Products Based on Genetic Algorithm[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(7): 1327-1333.

支持复杂产品并行拆卸序列规划的遗传算法

Parallel Disassembly Sequence Planning for Complex Products Based on Genetic Algorithm

  • 摘要: 为高效求解复杂产品的并行拆卸序列规划问题,提出基于遗传算法的复杂产品并行拆卸序列规划方法.针对并行拆卸序列规划问题中拆卸序列长度和每步拆卸零部件个数不确定的特点,提出并行序列染色体编码方法,分别将拆卸单元序列和拆卸步长作为染色体的前段和后段,以此表示一个拆卸序列.基于该染色体编码,采用拆卸混合图描述产品零部件间装配约束关系和拆卸优先级,并导出拆卸约束矩阵和邻接矩阵,由矩阵随机获取可行的初始染色体种群;将基本拆卸时间和不可行拆卸惩罚因子作为优化目标来构建适应度函数,确保最优解的可行性;在初始染色体种群的基础上,适应度函数最小为优化目标,通过遗传、交叉和变异遗传算子实现并行拆卸序列的优化.最后通过实例验证了该方法的可行性和实用性.

     

    Abstract: In order to solve the parallel disassembly sequence planning(PDSP) problem efficiently, a method based on genetic algorithm was developed. According to the uncertain characteristics of disassembly sequence length and the number of parts removed at each step for the PDSP, a chromosome coding method for parallel sequence was presented to express the disassembly sequence and disassembly steps simultaneously. The disassembly hybrid graph(DHG) was constructed to describe the mating contact and disassembly priority relationships among constituting components of the product. From the DHG, the disassembly constraint matrix and adjacent matrix can be deduced so that the chromosome population with a feasibility constraint was generated randomly in order to reduce the search space. The chromosome fitness function combines the total disassembly time and the penalty factor for unfeasible disassembly sequences. Based on the fitness function, the crossover and mutation operation were performed to get the optimum sequence. Finally, an example illustrates the proposed method.

     

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