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Optimal Inspection Allocation for Workstations of Attribute Data with Multi-characteristics in Multi-station Systems

机译：多工作站系统中具有多特征的属性数据工作站的最优检查分配

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The study models multi-characteristics inspection for inspection allocation problems with workstations of attribute data in serial production systems. Either 100% or 0% inspection is performed and Type I and Type II errors are considered. In addition, this study considers three possibilities of treatment of detected nonconforming units, namely, repair, rework and scrap. With the above considerations, a profit model is developed for optimally allocating inspections. Moreover, a genetic algorithm is used to solve the problem and it is proved to have much less computation time, compared with an optimization method based on complete enumeration, especially when number of workstations and characteristics becomes more.The study models multi-characteristics inspection for inspection allocation problems with workstations of attribute data in serial production systems. Either 100% or 0% inspection is performed and Type I and Type II errors are considered. In addition, this study considers three possibilities of treatment of detected nonconforming units, namely, repair, rework and scrap. With the above considerations, a profit model is developed for optimally allocating inspections. Moreover, a genetic algorithm is used to solve the problem and it is proved to have much less computation time, compared with an optimization method based on complete enumeration, especially when number of workstations and characteristics becomes more.The study models multi-characteristics inspection for inspection allocation problems with workstations of attribute data in serial production systems. Either 100% or 0% inspection is performed and Type I and Type II errors are considered. In addition, this study considers three possibilities of treatment of detected nonconforming units, namely, repair, rework and scrap. With the above considerations, a profit model is developed for optimally allocating inspections. Moreover, a genetic algorithm is used to solve the problem and it is proved to have much less computation time, compared with an optimization method based on complete enumeration, especially when number of workstations and characteristics becomes more.
机译：该研究对串行生产系统中属性数据工作站的检查分配问题进行了多特征检查建模。执行100％或0％检查，并考虑I型和II型错误。此外，本研究还考虑了对检测到的不合格部件进行处理的三种可能性，即维修，返工和报废。基于以上考虑，开发了一种利润模型，用于最佳地分配检查。此外，与基于完全枚举的优化方法相比，使用遗传算法解决了该问题，并且证明了其计算时间要少得多，尤其是在工作站数量和特性越来越多的情况下。批量生产系统中属性数据工作站的检查分配问题。执行100％或0％检查，并考虑I型和II型错误。此外，本研究还考虑了对检测到的不合格部件进行处理的三种可能性，即维修，返工和报废。基于以上考虑，开发了一种利润模型，用于最佳地分配检查。此外，与基于完全枚举的优化方法相比，使用遗传算法解决了该问题，并且证明了其计算时间要少得多，尤其是在工作站数量和特性越来越多的情况下。批量生产系统中属性数据工作站的检查分配问题。执行100％或0％检查，并考虑I型和II型错误。此外，本研究还考虑了对检测到的不合格部件进行处理的三种可能性，即维修，返工和报废。基于以上考虑，开发了一种利润模型，用于最佳地分配检查。此外，与基于完全枚举的优化方法相比，使用遗传算法解决了该问题，并且证明了它具有更少的计算时间，尤其是当工作站数量和特性变得更多时。

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