製造糸統的彈性是為了應客戶需求的變化，然而彈性的增加會造成糸統變得越來 越複雜，糸統複雜的程度則是會影響作決策時的困難性。因此，糸統的複雜程度可視 為影響決策判斷的重要因素。製造糸統的複雜程度可分成兩個部份，分別是靜態複雜 度與動態複雜度。靜態複雜度是由糸統結構與組成元素關係所造成的複雜程度，動態 複雜度則是糸統動態操作行為所造成的複雜程度。本研究以訊息熵當作衡量複雜度的 工具，利用熵值的計算得到不同狀態下糸統的熵值來探討製造糸統作業的靜態及動態 複雜度，建立衡量糸統複雜度的指標。在靜態複雜度方面，考慮的因子包括機台彈性、 作業彈性以及批量中產品混合比例，並討論這些因子對於靜態複雜度的影響。在動態 複雜度方面，考慮多機台下，在投入率與服務率的變化下，造成等候狀態發生的不確 定性，利用等候理論的學理基礎，分析糸統的動態等候行為模式，並引用訊息熵的概 念，發展出等候行為的複雜度評估模式，探討糸統投入率與服務率、最大容量、平行 機台數量等因子對於複雜度所造成的影響，以作為管理者決策的依據。

In order to respond to the change in customer demand, flexible manufacturing systems are developed and implemented. However, the increasing of flexibility results in that the system is more complex. The level of system complexity will cause the difficulty of decision making. Therefore, systems’ complexity can be treated as an important factor for decision making. There are two types of complexities: static complexity and dynamic complexity. The static complexity includes the complexity coming from system structure and the interaction of internal components. The dynamic complexity includes the complexity coming from system dynamic behaviors. In this research, the information entropy is proposed to evaluate the static and dynamic complexities of the system. For the static complexity, the influences of machine number, job number and part mix on static complexity are discussed. For the dynamic complexity, queuing theory is introduced to analyze the behavior of system dynamic queue, and the influences of arrival rate, service rate, system capacity and the number of parallel machine on dynamic complexity are discussed. The results of evaluated system complexity can help managers for decision making.