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OPTIMUM ALLOCATION OF INSPECTION EFFORT IN MULTISTAGE MANUFACTURING PROCESSES

机译:多步骤制造过程中的最佳检验分配

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The allocation of inspection effort (AIE) in multi-stage manufacturing system has been studied extensively over the last fifty years. The objective of this paper is to review the existing approaches, propose a classification of the available models in terms of the type of manufacturing system that they refer to and the applied solution methods and examine the effectiveness of the inspection strategies by developing appropriate generalised algorithm and software tool. The review revealed firstly that inspection allocation problem has been studied comprehensively by using variety of analytical and Monte-Carlo simulation methods rather than combination of both simulation techniques in a simulation-optimisation framework. Secondly large proportion of the papers focuses on several work stations representing part of a manufacturing line without attempting to solve the global optimisation problem which lead to solutions based on complete enumeration that are known to be computationally ineffective when the number of workstations increase. The developed simulation program demonstrated that methods determining the position of inspection by using complete enumeration method (EM) are of limited use in the majority of manufacturing situations when the number of workstations exceeds eighteen. This led to the development of a heuristic algorithm the performance of which was compared with the complete enumeration algorithm. It was found that heuristic method can derive an acceptable solution significantly faster. At present authors continue to develop heuristic algorithms for the AIE problem and methaheuristics using biologically inspired techniques.
机译:在过去的五十年中,对多阶段制造系统中检查工作量(AIE)的分配进行了广泛的研究。本文的目的是回顾现有方法,针对可用模型进行分类,以它们所引用的制造系统类型和应用的解决方法为基础,并通过开发适当的通用算法和检验方法来检验检查策略的有效性。软件工具。审查首先指出,已经通过使用各种分析方法和蒙特卡洛模拟方法对检验分配问题进行了全面研究,而不是在模拟优化框架中结合了两种模拟技术。其次,大量论文集中在代表生产线一部分的几个工作站上,而没有尝试解决全局优化问题,这导致了基于完全枚举的解决方案,众所周知,当工作站数量增加时,这种计算是无效的。开发的仿真程序表明,当工作站数量超过18个时,在大多数制造情况下,使用完全枚举方法(EM)确定检查位置的方法使用有限。这导致了启发式算法的发展,其性能与完整的枚举算法进行了比较。发现启发式方法可以显着更快地得出可接受的解决方案。目前,作者继续使用生物学启发技术开发针对AIE问题和启发式算法的启发式算法。

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