A grouping genetic algorithm for the multi-objective cell formation problem
A grouping genetic algorithm for the multi-objective cell formation problem
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DOI：
10.1080/00207540512331311859
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发表时间：
2005-02
影响因子：
9.2
通讯作者：
K. Yasuda;L. Hu;Yong Yin
中科院分区：
文献类型：
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作者：
K. Yasuda;L. Hu;Yong Yin
在本研究中，我们提出了一种部分采用 Falkenauer 分组遗传算法（GGA）的有效方法来解决多目标细胞形成问题（CFP）。考虑到机器的容量、零件体积和机器上的零件处理时间，目标是最大限度地减少单元负载变化和单元间流动。我们放宽单元尺寸限制并在不预先确定单元数量的情况下求解 CFP，这在现实世界的 CFP 设计中通常很难预测。我们还努力提高算法在群体初始化、适应度评估和防止交叉算子克隆方面的效率。测试了数值示例并与通用遗传算法（GA）进行了比较。结果表明，我们的方法在将机器分组为单元以及确定最佳解决方案的单元数量方面都是有效且灵活的。
In this research, we propose an efficient method to solve the multi-objective cell formation problem (CFP) partially adopting Falkenauer's grouping genetic algorithm (GGA). The objectives are the minimization of both the cell load variation and intercell flows considering the machines’ capacities, part volumes and part processing times on the machines. We relax the cell size constraints and solve the CFP without predetermination of the number of cells, which is usually difficult to predict in a real-world CFP design. We also make some effort to improve the efficiency of our algorithm with respect to initialization of the population, fitness valuation, and keeping crossover operator from cloning. Numerical examples are tested and comparisons are made with general genetic algorithms (GAs). The result shows that our method is effective and flexible in both grouping machines into cells and deciding on the number of cells for the optimal solution.