臺灣博碩士論文加值系統

產能規劃是生產決策者決定要有多少數量的機器設備來提供產能，生產所需數量的產品。由於產品需求的不確定性，使得產能規劃的工作更為艱難。本文針對多規劃時期下，多產品種類、多機器種類、不確定需求下之產能規劃問題以兩種方法求解並比較其結果。此兩種方法的目標式皆為欠貨成本加上機台購買成本之最小化，限制式則包含產能與預算的限制。
本論文把未來的需求模式化為以好幾套的需求劇本 (demand scenarios)所組成的集合，並對每一套劇本指定其發生機率。第一種方法將此問題模式化為隨機規劃模型並求解之。第二種方法將問題模式化為需求固定的混合整數規劃模型，接著，再將此模型配合決策樹的架構來模式化需求的不確定性並求解之。
本論文的實驗把兩種方法求得的成本，分別計算在不同需求劇本下與完美資訊期望值的差距百分比。實驗結果顯示，隨機規劃與完美資訊期望值在不同需求劇本下的差距較小，求解的時間也比決策樹分析方法快。

Capacity planning is the calculation of the required numbers of machines for production. Due to the demand uncertainty, capacity planning is becoming more and more difficult. This study focuses on multiple periods, multiple product types, multiple machine types, and demand uncertainty capacity planning problem, and uses two approaches to solve the problem. The object function of the approaches is to minimize the backorder cost and machines purchasing cost.
The first approach formulates the problem as stochastic programs. The model assumes there are several demand scenarios, and the probability of each scenario is known. The second approach formulates the same problem with known demand by mixed integer programs, and then uses decision tree to deal with the demand uncertainty.
Our experiment compared object values from stochastic programming and expected costs from decision tree analysis with the expected value with perfect information（EVwPI）under different numbers of scenario. According to the experiment results, the stochastic programming approach is superior to decision tree analysis.

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