A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes
A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes
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DOI：
10.1109/3477.552184
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发表时间：
1997-02
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影响因子：
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通讯作者：
I. Lee;R. Sikora;M. Shaw
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文献类型：
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作者：
I. Lee;R. Sikora;M. Shaw
遗传算法在旅行商问题（TSP）、二次分配问题（QAP）和作业车间调度等组合优化问题中得到了广泛的应用。在所有这些问题中，通常都有一个定义良好的表示，遗传算法使用它来解决问题。我们提出了一种新的方法来解决两个相关的问题-批量和排序-同时使用气体。我们的方法的本质在于对批次大小和序列的信息使用统一表示的概念，并使GAs能够通过用良好的构建块替换原始基因来进化染色体。此外，为了进一步提高性能，还采用了模拟退火工艺。我们评估了将上述方法应用于实际制造工厂的可变批量柔性流水线调度的性能，并将其与诸如成对交换改进、禁忌搜索和模拟退火程序等替代方法进行了比较。结果表明，该方法对柔性流线调度是有效的。
Genetic algorithms (GAs) have been used widely for such combinatorial optimization problems as the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. We present a novel approach for solving two related problems-lot sizing and sequencing-concurrently using GAs. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GAs to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow line scheduling.