在多品質生產系統中考量不同技術等級人員配置下檢驗站設置最佳化

Optimal Inspection Allocation with Placement of Different Skill Levels of Workers for Multi-Characteristics Production Systems

10.6840/cycu201201025

連豈浩

序列式生產系統 ； 多品質特性 ； 技術等級人員 ； 基因演算法 ； Serial production system ； Multi-characteristics ； Workers skill levels ； Genetic algorithm

中原大學工業與系統工程學系學位論文

2012年

碩士

饒忻

繁體中文

近年來，高科技產業的蓬勃發展及消費型態的改變，產品生命週期越來越短，使得生產製程需配合新產品的推出不斷更新，加上勞動力成本不斷上升，如果企業沒有良善的規劃，可能會造成製造成本上升與產品品質下降之情形，進而使得企業在市場上的競爭力下降。因此在生產系統中，企業必須兼顧品質與績效及決定各檢驗站的設置與不同品質特性下是否檢驗，並在有限的資源下做出最佳的資源配置。此外員工有著不同作業能力，人員的配置影響生產系統的流暢度及產品的品質，也是一值得探討的重要課題。 本研究主要探討如何將不同技術等級人員配置在工作站及檢驗站，在多品質特性下，研究序列式生產系統的檢驗站設置策略。本研究透過生產系統模式的建立，將目標函數設定為最大利潤來發展最佳等級人員配置及檢驗站設置策略。在求解方法上，利用窮舉法求得最佳人員配置及檢驗站設置策略，並利用基因演算法求解，分析求解方法之執行績效和生產系統之相關問題。本研究相信當業界在生產系統需要操作人員分配及檢驗站設置規劃時，能給予有效的意見。

In recent years, along with the booming development of high-tech industry and the change of customer purchasing style, product life cycles are getting shorter and shorter, which makes manufacturing processes should be updated constantly for those new products. Moreover, labor costs have been rising. If enterprises do not have a nice planning, then it will cause production cost to increase and product quality to reduce, and in turn drop the competitiveness of enterprises in the market. Therefore, in the production system, enterprises must take good care of both quality and performance with the best allocation of inspection and other limited resources. In addition, workers have different operational capabilities. How to allocate the right worker in the right place will affect production performance and product quality, which is also an essential issue to study. This study explores how to place workers with different skill levels in the workstations and inspection stations for studying the allocation policy of inspections for serial production systems under multi-characteristics. A mathematical model will be developed with maximum profit as the objective function. The study would use the enumerative method to find the optimal inspection policy and workers placement, and use two different solution approaches: genetic algorithm as well. The performance of different solution algorithms is discussed. We believe that the results of this study can provide effective suggestions for industrial people who are planning to do worker placement for workstations and inspection stations.

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