近代的科技躍進快速且產品變化多樣，在工業發展時期製造過程需要具備
快速與低成本的性質，然而因應未來的市場需求，生產方式將會趨向於少量多
樣的生產模式。彈性製造系統為對應此少量多樣生產模式的製造模式，在彈性
製造系統中常使用群組技術來提升製造效率，而群組技術常使用群組規劃的方
式協助進行，群組規劃將製成相似的產品工件分為產品族，然後依據產品族將
機台分群，形成工件與機台組成的群組。透過群組規劃的分群模式使彈性製造
系統分割成各個小型製造系統，讓每個小系統有更好的製造效率與提升生產
力。本研究透過緊密基因演算法與變動臨域搜尋法的概念進行群組規劃，透過
更好的群組規劃結果使製造系統能夠有更好的效率，以協助彈性製造系統的製
造流程，使其生產力提升、成本降低、品質提高且效率更好。

Technology is developing quickly and the type of consumption pattern is being varied. In order to Satisfying customer demand, Products are customized to customer. Manufacturer need to adopt the job shop production to produce different products for customer. Because manufacturers implement the job shop production, the cell technology is used for completing job shop production. While manufacturer use group technology, they usually need to find some method to solve the cell formation problem. In this thesis book, we hybrid Compact Genetic Algorithm and Variable Neighborhood Search to solve cell formation problems. We propose the different idea of algorithm which local search is focused in our thesis. We test the 35 problems with various size from literatures are used to test the performance of algorithm. We use Taguchi method for determining the parameter setting and then we compare other methods which was proposed from former research. Finally, we analyze the experimental result and discuss reasons which cause difference scheme.

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