考虑机器利用率百分比的双目标制造单元部署NSBA算法

Tamal Ghosh, K. Martinsen
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引用次数: 0

摘要

在细胞制造(CM)中,机器利用率被认为是制造细胞形成问题(CFP)的一个重要生产因素。这个最近发展起来的概念正确地强调了CM背景下的比率数据。本文建立了一种基于利用率的双目标数学模型,该模型能最大限度地减少由瓶颈机器和空洞数量引起的机器总利用率。在此基础上,提出了一种新的数据生成算法。采用非支配排序蝙蝠算法(non- dominant sorting bat algorithm, NSBA)求解上述双目标CFP,并与已有的多目标蝙蝠算法(multi-objective bat algorithm, MOBA)进行了比较。对所得结果进行了统计检验,并确认了数据的一致性。计算实验表明,该算法的Pareto解提高了35.7%。这项研究的贡献有三个方面。首先,为基于利用率的CFPs开发了精确的双目标数学表达式。其次,提出了一种新的数据生成算法。第三,NSBA技术测试成功。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deploying NSBA algorithm for bi-objective manufacturing cells considering percentage utilisation of machines
Percentage utilisation of machines is considered as an important production factor for manufacturing cell formation problem (CFP) in cellular manufacturing (CM). This recently developed concept correctly emphasises ratio data in context of CM. In this paper, an utilisation-based bi-objective mathematical model is developed, which minimises the total machine utilisation induced by bottleneck machines and number of voids. Thereafter, a new data-generating algorithm is introduced. The abovementioned bi-objective CFP is solved using a non-dominated sorting bat algorithm (NSBA), which is compared with published multi-objective bat algorithm (MOBA) successfully. Statistical tests are conducted and data consistency is confirmed on obtained results. The computational experiments depict that the Pareto solutions of NSBA are 35.7% improved. The contribution of this research is threefold. First, an accurate bi-objective mathematical expression is developed for utilisation-based CFPs. Second, a novel data-generating algorithm is stated. Third, NSBA technique is successfully tested.
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