在工廠區位─分派(Facility Location Allocation；FLA)問題中，由於涉及的準則或因子非常多，包含如環境因素、政治因素等的定性因子及設置成本、交通成本等的定量因子，決策者在評估時需要考慮多個準則，此即為多準則決策(Multiple Criteria Decision Making；MCDM)，根據學者應用方式的不同，可將其分為多屬性決策(Multiple Attribute Decision Making；MADM)及多目標決策(Multiple Objective Decision Making；MODM)。由於處理定性準則時存在主觀想法，明確評估值無法完整表達專家意見，因此，本研究欲建構一模式，在模糊環境下，以模糊語意處理多準則決策問題。
本研究模式分為準備階段、第一階段及第二階段，在準備階段，專家選定評估之方案及影響準則，並將準則分為定性準則及定量準則以為後續使用，第一階段為定性準則的處理，專家對各方案之定性準則作語意評估，並應用模糊多屬性決策方法求得各方案的評估值，第二階段為多目標模式的建構，將各方案的評估值與定量準則作為目標式加入模式，接著利用模糊目標規劃優先加法模式及考慮目標間存在模糊關係之優先加法模式計算應設置工廠之數量、地點及配銷給需求點之需求量，並探討兩種求解方法產生的結果並分析，希望透過此模型的建立能幫助決策者在面臨區位分派決策時提供決策上的參考。

Facility Location Allocation (FLA) problems involve many qualitative factors like environment, political and quantitative factors such as setup and transportation costs. In order to deal with this problem, decision makers can consider many different criteria using an approach called Multiple Criteria Decision Making (MCDM). According to different application methods for scholars, MCDM can be divided into Multiple Attribute Decision Making (MADM) and Multiple Objective Decision Making (MODM). In addition, when decision makers deal with qualitative criteria, subjective opinions often exist, and crisp value cannot express expert opinions completely. Thus, this study builds a model in a fuzzy environment that will handle the MCDM problem with fuzzy linguistic variables.
The proposed model includes a preparation state, a first state and a second state. In the preparation state, experts identify criteria and alternatives and then divide the criteria into qualitative criteria and quantitative criteria. The purpose of the first state is to deal with the qualitative criteria. In this state, the experts give performance ratings to alternatives using linguistic variables and then apply Fuzzy MADM to obtain an assessment of each alternative. In the second state, we build a multi-objective model using fuzzy goal programming that takes into consideration the fuzzy relation and priorities among objectives, and then we determine the number and locations of facilities as well as the demand. We subsequently compare and analyze the results between two methodologies, suggesting that the model can be applied to solve the FLA problem and give suggestions to decision makers.

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