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A genetic algorithms simulation approach for the multi-attribute combinatorial dispatching decision problem

机译:多属性组合调度决策问题的遗传算法仿真方法

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摘要

This paper presents a genetic algorithms (GA) simulation approach in solving a multi-attribute combinatorial dispatching (MACD) decision problem in a flow shop with multiple processors (FSMP) environment. The simulation is capable of modeling a non-linear and stochastic problem. GA are one of the commonly used metaheuristics and are a proven tool for solving complex optimization problems. The proposed GA simulation approach addresses a complex MACD problem. Its solution quality is illustrated by a case study from a multi-layer ceramic capacitor (MLCC) manufacturing plant. Because GA search results are often sensitive to the search parameters, this research optimized the GA parameters by using regression analysis. Empirical results showed that the GA simulation approach outperformed several commonly used dispatching rules. The improvements are ranging from 33% to 61%. On the other hand, the increased shop-floor-control complexity may hinder the implementation of the system. Finally, future research directions are discussed. (c) 2005 Elsevier B.V. All rights reserved.
机译:本文提出了一种遗传算法(GA)仿真方法,用于解决带有多处理器(FSMP)环境的流水车间中的多属性组合调度(MACD)决策问题。该仿真能够对非线性和随机问题进行建模。遗传算法是常用的元启发式算法之一,并且是解决复杂优化问题的可靠工具。拟议的GA模拟方法解决了一个复杂的MACD问题。多层陶瓷电容器(MLCC)制造厂的案例研究说明了其解决方案质量。由于GA搜索结果通常对搜索参数敏感,因此本研究通过使用回归分析对GA参数进行了优化。实证结果表明,遗传算法模拟方法优于几种常用的调度规则。改进范围从33%到61%。另一方面,增加的车间控制复杂性可能会阻碍系统的实施。最后,讨论了未来的研究方向。 (c)2005 Elsevier B.V.保留所有权利。

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