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A New Mathematical Model for Integration of Cell Formation with Machine Layout and Cell Layout by Considering Alternative Process Routing Reliability; A Novel Hybrid Metaheuristic

机译:一种新的数学模型,通过考虑替代过程路由可靠性,通过机器布局和电池布局集成细胞形成的数学模型;一种新的混合杂交成分

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There is still a great deal of attention in cellular manufacturing systems and proposing capable metaheuristics to better solve these complicated optimization models. In this study, machines are considered unreliable that life span of them follows a Weibull distribution. The intra and inter-cell movements for both parts and machines are determined using batch sizes for transferring parts are related to the distance traveled through a rectilinear distance. The objectives minimize the total cost of parts relocations and maximize the processing routes reliability due to alternative process routing. To solve the proposed problem, Genetic Algorithm (GA) and two recent nature-inspired algorithms including Keshtel Algorithm (KA) and Red Deer Algorithm (RDA) are employed. In addition, the main innovation of this paper is to propose a novel hybrid metaheuristic algorithm based on the benefits of aforementioned algorithms. Some numerical instances are defined and solved by the proposed algorithms and also validated by the outputs of exact solver. A real case study is also utilized to validate the proposed solution and modeling algorithms. The results indicate that the proposed hybrid algorithm is more appropriate than the exact solver and outperforms the performance of individual ones.
机译:蜂窝制造系统中仍有很大的注意力,并提出了能力的型培育体,以更好地解决这些复杂的优化模型。在这项研究中,机器被认为是不可靠的,因为它们的生命跨度遵循威布尔分布。两个部件和机器的帧内间和电池间运动使用用于传送部件的批尺寸确定与穿过直线距离的距离有关。目标最小化零件重定位的总成本,并最大限度地提高了由于替代过程路由而可靠性的可靠性。为了解决所提出的问题,采用遗传算法(GA)和最近包括Keshtel算法(KA)和RDA)的两个自然启发算法(RDA)。此外,本文的主要创新是提出一种基于上述算法的益处的新型混合成血管算法。由所提出的算法定义和解决了一些数值实例,并由精确求解器的输出验证。还利用真实的案例研究来验证所提出的解决方案和建模算法。结果表明,所提出的混合算法比确切的求解器更合适,优于个体的性能。

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