生產線平衡，是製造業者所嚮往的生產狀態，但往往會因為製程或是增購的設備，導致前後站生產的串聯系統出現了缺料或是在製品過多的現象，而生產順序以及生產數量的取捨調度，考驗著企業的調度人員，在有限的資源中，藉由系統即時且準確的排出最適當的生產順序，為當今的主流，工作站裡可以根據機台性質分為專用設備以及彈性設備，其中專用設備僅能於當前工作站作業無跨製程的能力，而彈性設備則是能加工於不同的工作站別，可依據需求做調整，生產規劃中的協同服務為專用與彈性設備其性質能夠在同一個工件上加工，本研究將探討專用設備在考慮可靠度的狀態下，協同服務串聯系統在各狀態時將會如何調度現有彈性設備資源，得出各階段最佳決策以及該模式下的性質。

本研究以庫存成本最小化為最佳化目標，將過往協同服務串聯系統之研究延伸，在原有二階段串聯系統中，考慮了專用設備的可靠度，因為設備失效的頻率以及維護所花費的成本皆會影響生產節奏，增設了可靠度的考量後將使得生產的狀態更加貼近現實面，接著本研究將建立模型中相關參數，建立馬可夫決策模型，並依據目前工作站的工件數量，彈性設備的資源調度將做為系統決策，比較出調度後令成本最小化的決策，利用動態規劃的演算法計算出多期數下之結論，並最終給予使用者回饋；本研究將會根據這個模型，同時考慮專用設備的停機策略以及不停機策略，對原有文獻提及的性質定理做比較，並且我們從中得出了某些情況下，因受到設備可靠度的影響，隨著工作站的工件數量越少，最佳決策反而投入更多的資源於該工作站，這部分結論與過往文獻有所差異，並且這也與現實認知中有所差異，本研究將說明相關參數細節以及建立之技巧。

It is common to have tandem systems used in production configuration in the manufacturing industry. According to the nature of the machine, the station can be separated into dedicated equipment and flexible equipment. The dedicated equipment can only work in their corresponding station, and the flexible equipment can be flexibly dispatched to different stations. Collaborative services are dedicated and flexible equipment that can work together on the same Work In Process (WIP). Therefore, in the case of limited resources, in order to obtain the optimal decision-making and mode properties at each stage, this study will discuss how the dedicated equipment schedules the existing flexible device resources.
This study takes the minimization of inventory cost as the optimization goal. It extends the previous research on collaborative service tandem system and adds the conditions of station dedicated equipment reliability. Besides, it considers the idling policies and non-idling policies of dedicated equipment, and establishes a Markov decision model based on this situation. The resource allocation of flexible equipment is used as a decision variable, and the expected cost equation is constructed. At last, dynamic programming is used to find the optimal policy at each state. From some cases, due to the influence of dedicated equipment reliability, as the number of WIP in the station is less, the optimal decision is invested more resources in the station. This part of the conclusion is different from the past literature. And this is also different from real cognition. This study will illustrate the details of related parameters and the techniques for establishing.

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