Solving the rotating seru production problem with dynamic multi-objective evolutionary algorithms

Solving the rotating seru production problem with dynamic multi-objective evolutionary algorithms
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用动态多目标进化算法解决旋转血清生产问题

DOI:
10.1016/j.jmse.2021.05.004
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发表时间:
2021-05
影响因子:
6.6
通讯作者:
Yin Yong
Yin Yong
中科院分区:
--
文献类型:
--
作者:
Liu Feng;Fang Kan;Tang Jiafu;Yin Yong

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电子工业市场的动荡导致了一种新的生产系统seru(日语中cell的发音),并已在数百家日本和其他亚洲公司中广泛实施。特别是,已广泛实施的培训系列,其中工人充分交叉培训具有相同的技能水平,但可能在执行任务的熟练程度上有所不同。由于目标冲突和客户需求的动态释放,决定工人轮换顺序和工件装配顺序的排产问题难以求解。为了解决这个问题,我们提出了一个动态的多目标NSGA-II基于模因算法。此外,为了保持理想的种群多样性和提高搜索效率,我们提出了不同的问题特定的进化策略。最后,我们测试我们提出的模因算法与其他国家的最先进的多目标进化算法的性能,并证明我们提出的算法的有效性。
Today's volatile market conditions in electronic industries have lead to a new production system,seru(which is the Japanese pronunciation for cell), and has been widely implemented in hundreds of Japanese and other Asia companies. In particular, the rotatingseruhas been widely implemented, where workers are fully cross-trained with the same skill level but may be different on the proficiency of performing tasks. The rotatingseruproduction problem, which determines the rotating sequence of workers as well as the assembling sequence of jobs, is difficult to solve due to conflicting objectives and dynamic release of customer demands. To solve this problem, we propose a dynamic multi-objective NSGA-II based memetic algorithm. Moreover, to preserve desirable population diversity and improve the searching efficiency, we propose different problem-specific evolutionary strategies. Finally, we test the performance of our proposed memetic algorithm with other state-of-the-art multi-objective evolutionary algorithms and demonstrate the effectiveness of our proposed algorithm.
DOI: 10.1016/0305-0483(83)90088-9
发表时间: 1983-01-01
影响因子: 6.9
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