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Material handling considerations in the FMS loading problem with full routing flexibility

机译:具有全面布线灵活性的FMS装载问题中的物料搬运注意事项

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This study exploits machining and routing flexibility to effectively deal with the material handling requirements resulting from a frequently changing demand mix in a manufacturing system where material handling is a bottleneck. For this purpose, the objective function of the operation and tool loading problem is selected as the minimisation of the total distance traveled by parts during their production. Versatile machines and the flexible process plans offer full routing flexibility that enable the same workpiece to be processed using alternative sequences of operations on alternative machines. Three mathematical programming (MP) models and a genetic algorithm (GA) are proposed to solve this problem. The proposed MP formulations include a mixed-integer nonlinear programming (MINLP) model and two mixed-integer programming (MIP) models, which offer different representations for the flexible process plans. The GA is integrated with linear programming for fitness evaluation and incorporates several adaptive strategies for diversification. The performances of these solution methods are tested through extensive numerical experiments. The MP models are evaluated on the basis of the exact solutions they yield as well as how they lend themselves for GA fitness evaluation. The GA-LP integration works successfully for this hard-to-solve problem.
机译:这项研究利用加工和工艺路线的灵活性来有效处理因物料搬运成为瓶颈的制造系统中频繁变化的需求组合而导致的物料搬运要求。为此,选择操作和工具装载问题的目标函数,以最大程度地减少零件在生产过程中所经过的总距离。多功能的机器和灵活的工艺计划提供了完整的工艺路线灵活性,使您可以使用其他机器上的其他操作序列来处理同一工件。提出了三种数学编程(MP)模型和一种遗传算法(GA)来解决此问题。拟议的MP公式包括一个混合整数非线性规划(MINLP)模型和两个混合整数规划(MIP)模型,它们为灵活的工艺计划提供了不同的表示形式。 GA与用于适应性评估的线性规划集成在一起,并结合了多种自适应策略以实现多元化。这些解决方案方法的性能通过大量的数值实验进行了测试。 MP模型是根据它们产生的精确解以及它们如何适合GA适应性评估来评估的。 GA-LP集成成功解决了这个难以解决的问题。

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