在競爭激烈的大環境下，生產製造業該如何有效率地管理生產排程，保有其競爭力與優勢，已經成為刻不容緩的議題。然而，因為實際生產排程中存在許多的不確定性，所以，近幾年來已經有許多學者從事有關模糊排程之研究。
在本論文中，我們提出模糊-基因演算法，以研究有關單機排程問題，目標為使總加權完工時間最小，且考慮模糊處理時間。模糊-基因演算法為一個基於基因演算法與模糊理論之方法。工作處理時間以梯形模糊數的型式表示，並利用基因演算法協助我們尋找最佳的生產排序。此外，我們使用VBA程式語言與Evolver實作排程問題的模型。
我們從OR-library排程問題的加權延後問題中，選取40個工作數之問題共125個問題作測試。根據實驗的結果顯示，模糊-基因演算法可以搜尋出一組使可能與必要最佳之程度最大化，並同時使模糊總加權完工時間趨近最小化之排序，且平均改善率高達38.09%，所需的執行時間僅為數分鐘。

It is an imperious issue to efficiently manage the production scheduling for the production manufacturing industries. The results help them to hold the competitiveness and advantage in the extremely competitive environment. Nowadays, many scholars devote themselves to the research of the fuzzy scheduling in recent few years because this issue exits many uncertainties in the real production scheduling.
This thesis presents a Fuzzy-Genetic Algorithm (FGA) to solve the single machine scheduling problems for minimizing the total weighted completion time while considering the uncertain processing time. The FGA method is based on the genetic algorithm and fuzzy theory. The processing time can be represented by a trapeziod fuzzy number. Here the genetic algorithm is applied to find a best solution for processing sequence. In addition, VBA programming language and Evolver are integrated to realize the model for the scheduling problems.
In the experiments, 125 test examples of 40 jobs scheduling problems with the weighted tardiness are acquired from the OR-library. According to the experimental results, the FGA can perform the search to find a schedule of maximizing the degrees of possible and necessary optimality while minimizing the fuzzy total weighted completion time within a few minutes. Also, the average improvement rate is 38.09%.

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OR-library (http://people.brunel.ac.uk/~mastjjb/jeb/info.html)