多世代產能規劃中存在許多不確定因素，例如：產品售價、需求變化、產品生命週期等，皆影響到企業的營運及獲利，因此本研究將針對目前產能規劃所面臨的不確定因素加以探討，目的為決定各規劃時期之不同世代產能，以使得整體利潤最大化。本研究與過去文獻相異之處在於需求隨產業景氣而變動，並且同時考慮產出與價格不確定。
本研究考慮多世代產能可彈性相互支援之特性，在兩種不同生命週期產品之情況下，建構動態規劃模式，並以數值迭代法(Value Iteration Algorithm)進行模式求解，決定產能規劃期限內每一期之最佳購買策略，以達成最大化利潤之目標。本研究提供一改良之求解演算法大幅減少求解時間，同時探討策略轉移曲線之存在，此曲線將系統狀態分為三區域，每一區域均有對應之最佳策略，可清楚描述產能擴張決策。

Our research studies the multi-generation capacity planning problem. Multi-generation capacity planning has become an interesting research subject, because many uncertainty factors affect the accuracy of planning. These factors include price uncertainties, demand fluctuation and uncertain product life cycle. The objective of this research is find a multi-generation capacity portfolio in each time period that is robust to demand, price, product life cycle uncertainties.
This research models the multi-generation capacity planning problem as a Markov decision processes (MDP) problem. In this MDP model, we consider two generation of manufacturing technology. The new generation capacity serves as a flexible resource that can be used to downward fulfill the deficiency of old generation capacity. The MDP model is solved by value iteration algorithm, and the solution provides a robust capacity expansion strategy for both types of capacity. The objective of this MDP model is to maximize the expected profit under demand and price uncertainties. An efficient algorithm is also developed by action elimination techniques to reduce the computation complexity of the problem. We verify our results under different types of product life cycle setting.

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