多目标拆卸线平衡问题的群集智能优化算法研究

发布时间：2018-03-18 11:27

本文选题：拆卸线平衡问题　切入点：多目标　出处：《西南交通大学》2012年硕士论文　论文类型：学位论文

【摘要】：随着生产者责任延伸制的推行、各国新的更多的环境立法的建立以及公众环境意识的提高,制造商开始回收和再制造废旧的产品。此外,重新使用废旧产品所带来的经济吸引力也从另一个层面推动了更多制造商的投入。拆卸是重新使用、制造、回收、存储以及合理处理产品关键的第一步,拆卸线是实现大规模拆卸的最佳选择,因而有效设计和平衡拆卸线对提高拆卸效率至关重要,因此研究拆卸线平衡问题具有重要的理论和实际意义。结合拆卸线的特点,提出了多目标拆卸线平衡问题的数学模型,其优化目标为最小化工作站数、均衡各工作站空闲时间,并考虑拆卸产品部件的危害、需求以及方向。在此基础上,采用两种群集智能优化算法—粒子群算法和蚁群算法研究了多目标拆卸线平衡问题。提出了一种目标基于优先排序的粒子群算法,该算法采用随机数生成粒子的位置和速度,而位置和速度的更新则为对应随机数相加减,进而把位置的随机数作为选择零件的权重,从而根据权重的大小来选择拆卸的零件。并通过具体的实例以及基准例子验证了算法的有效性。提出了一种基于Pareto的粒子群算法来求解多目标拆卸线平衡问题,该算法采用小生境技术选取多目标的全局最优解,采用Pareto占优以及分散度作为个体评价以及局部最优解选取,最后通过具体的实例以及基准例子验证了算法的有效性。提出了一种目标基于优先排序的蚁群算法来求解多目标拆卸线平衡问题,该算法考虑了以零件拆卸时间、危害以及需求三种规则的启发式信息,并综合考虑利用先验知识、探索新路径、随机选择三种方式的混合搜索机制,有效的提高了算法的搜索效率,并通过具体的实例以及基准例子验证了算法的有效性。
[Abstract]:With the extension of producer responsibility, the establishment of new and more environmental legislation in various countries and the increasing public awareness of the environment, manufacturers began to recycle and re-manufacture used products. The economic appeal of reusing used products also drives more manufacturers on another level. Disassembly is a critical first step in reusing, manufacturing, recycling, storing, and reasonably disposing of products. The disassembly line is the best choice to realize the large-scale disassembly, so it is very important to design and balance the disassembly line effectively to improve the disassembly efficiency. Therefore, it is of great theoretical and practical significance to study the disassembly line balance problem. Combined with the characteristics of disassembly line, the mathematical model of multi-objective disassembly line balance problem is put forward. Its optimization goal is to minimize the number of workstations, to balance the idle time of each workstation, and to consider the harm of disassembly parts. On the basis of this, two cluster intelligent optimization algorithms, particle swarm optimization (PSO) and ant colony algorithm (ACA), are used to study the multi-objective disassembly line balance problem. A priority-based particle swarm optimization algorithm is proposed, in which the random number is used to generate the position and velocity of the particle, and the update of the position and velocity is the addition and subtraction of the corresponding random number. Then the random number of positions is taken as the weight of the selected parts, and then the disassembled parts are selected according to the weight, and the validity of the algorithm is verified by concrete examples and benchmark examples. A particle swarm optimization (PSO) algorithm based on Pareto is proposed to solve the multi-objective disassembly line balance problem. The algorithm uses niche technology to select the global optimal solution of multi-objective, Pareto dominance and dispersion as individual evaluation and local optimal solution selection. Finally, the effectiveness of the algorithm is verified by concrete examples and benchmark examples. An ant colony algorithm based on priority is proposed to solve the multi-objective disassembly line balance problem. The algorithm takes into account the heuristic information of disassembly time, harm and requirement rules, and synthetically considers the use of prior knowledge. The search efficiency of the algorithm is improved by exploring the new path and selecting three kinds of hybrid search mechanism randomly, and the effectiveness of the algorithm is verified by concrete examples and benchmark examples.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP18;TH186

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