摘要
为了更好地解决开放式作业域的混流装配线排序问题,建立了以最小化超载时间与平顺化零部件消耗为优化目标的混流装配线排序问题数学模型,并提出了一种禁忌粒子群算法求解该排序问题。针对标准粒子群算法在算法后期搜索精度不足以及容易陷入局部最优不能跳出的缺陷,引入了禁忌搜索算法建立了对最优微粒的重搜索机制来提高算法跳出局部最优的能力,同时给出了禁忌算法中候选解、禁忌表长度、禁忌对象、藐视准则的设置方法,并采用了随机权重的惯性权重更新方式来平衡算法的全局和局部搜索能力,最后建立了禁忌粒子群的算法流程。通过比较禁忌粒子群算法与遗传算法的实例计算结果,验证了禁忌粒子群算法在求解开放式作业域的混流装配线排序问题中的有效性和优越性。
In order to solve the sequencing problems in open-station mixed-model assembly lines,a mathematical model was established that considered two objectives： to minimize the utility time and keep average consumption rate of parts, and the tabu particle swarm optimization was proposed to solve the problem. Aiming at standard particle swarm optimization with an insufficient accuracy in late search and easy to fall into the local optimum, the tabu search algorithm was brought to establish the optimal particle research mechanism as well as improve the capacity to jump out from a local optimum point. A random weight updates was brought in to balance the global and local search ability. An example was given to test the algorithm. The results indicate that the algorithm can solve sequencing problems in mixed-model assembly lines successfully with an effective outcome.
出处
《机电工程》
CAS
2013年第4期430-434,共5页
Journal of Mechanical & Electrical Engineering
基金
国家自然科学基金资助项目(70971118)
浙江省自然科学基金资助项目(LY12E05021)
浙江省教育厅科研资助项目(Y201121984)
关键词
粒子群算法
混流装配线排序
禁忌搜索算法
排序
particle swarm optimization（PSO）
mixed-model assembly line
tabu search
sequencing
