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U-shaped assembly line worker assignment and balancing problem: A mathematical model and two meta-heuristics

机译:U型装配线工人分配和平衡问题:一个数学模型和两个元启发式方法

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As the U-shaped assembly lines are concerned with manual assembly operations, workers' performance plays a major role. However, the past studies assume that all workers can carry out all tasks with an equal performance level, and actual task times do not depend on the worker who performs it. On the other hand, this is unrealistic. In this study, maximization of the line efficiency for the U-shaped assembly line worker assignment and balancing problem by considering the worker performance is aimed. First, a nonlinear model of the problem is formulated, and then it is linearized. Later, an Artificial Bee Colony Algorithm and a Genetic Algorithm are proposed to solve it, in short computational times. A hypothetical data set (Jackson, 1956), a real data set (Cevikcan et al., 2009) and data sets of assembly line worker assignment and balancing problem (Araujo et al., 2015) were solved by using the model and the meta-heuristics. The meta-heuristics were able to reach solutions that have equal line efficiency with the optimum solution found by the proposed mathematical model. The contribution of this study is that the mathematical model for the U-shaped line assembly line worker assignment and balancing type-E problem that considers the workers' performance is formulated, and an Artificial Bee Colony Algorithm is proposed for the first time for this problem type.
机译:由于U形装配线涉及手动装配操作,因此工人的绩效起着重要作用。但是,过去的研究假设所有工人都可以以相同的绩效水平执行所有任务,而实际任务时间并不取决于执行该任务的工人。另一方面,这是不现实的。在这项研究中,旨在通过考虑工人的绩效最大化U型装配线工人分配的生产效率和平衡问题。首先,建立问题的非线性模型,然后将其线性化。后来,人们提出了一种人工蜂群算法和一种遗传算法来解决它,而且计算时间很短。通过使用模型和元数据,解决了假设数据集(杰克逊,1956年),真实数据集(Cevikcan等,2009年)以及流水线工人分配和平衡问题的数据集(Araujo等人,2015年)。 -启发式。元启发式算法能够达到具有与拟议数学模型找到的最优解相同的线路效率的解。这项研究的贡献在于,建立了考虑工人绩效的U型生产线装配线工人分配和平衡E型问题的数学模型,并首次提出了人工蜂群算法。类型。

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