隨著決策問題之複雜性和不確定性增加，一個人有時候無法單獨進行決策，因此群體決策問題漸受重視。一般群體決策中，通常限制專家使用同一種偏好形式，事實上，每個專家有不同的文化和背景，因此熟悉的評估方式也會因人而異，所以本研究提供專家最常用的三種評估方式：區間效用值、區間模糊偏好、區間乘積偏好，讓專家可選擇較熟悉的評估形式，提高決策的精確性。同時，為提高使用上之彈性及決策者對資料不確定性，本研究對三種評估方式之每一評估值皆以區間值表示，以提高方案評估之可行性。
本研究使用目標規劃將專家意見整合，並將決策模式分為三個階段，第一階段為蒐集專家對方案評估之決策矩陣，主要為區間效用值、區間模糊偏好、區間乘積偏好此三種類型，第二階段主要是將區間效用值從絕對關係轉換為相對關係，區間乘積偏好之範圍從[1/9,9]轉換為[0,1]，主要是將各種偏好形式之維度和範圍轉為一致，降低不同衡量單位造成的誤差，第三階段則是考量專家在相對重要程度情況下，使用目標規劃將專家意見進行整合，當專家相對重要性越大時，專家意見和最後群體值差距會較小，以期滿足群體意見離差值最小化目標下，找到最佳的方案。最後，本研究再與Xu(2007a)所提出之模式進行比較與分析，本研究所建構之模式不僅縮小總離差值，也避免最後結果偏頗於某一專家，解決Xu(2007a)模式中忽略維度與範圍不同所產生的缺失，使決策更加公正。

With the increasing complexity of real-life decision making problems, it is less and less possible for a single expert to consider all the relevant aspects before coming to a conclusion. Several methods have been proposed to deal with group decision making problems, but few allow experts to express their preferences in different forms. However, due to culture differences and varied contexts, experts may choose to express their preferences by means of different preference-representation structures. This paper allows experts to choose one of the following three preference structures, interval utility values, interval fuzzy preference relation and interval multiplicative preference relations, in order to increase the accuracy of their decision-making. In addition, for all three preference-representation structures, experts can express their preferences using interval numbers to improve the feasibility of their opinions.
This work proposes a goal programming approach to aggregate expert’ opinions and the decision model consists of three steps: (1) Gather experts’ decision-making matrix including interval utility values, interval fuzzy preference relation and interval multiplicative preference relations. (2) The dimensions of the interval utility values are converted from an absolute relationship to a relative one and the scopes of the interval multiplicative preferences are converted from [1/9,9] to [0,1]. The main purpose of this is to convert the different dimensions and scopes into the same form in order to reduce the errors caused by the different units of measurement. (3) We take into account the importance of each experts when aggregating their opinions. We assume that the more important the expert, the lower the deviation between their opinion and the final group opinion. This approach will find the best alternative with minimum deviation. Finally, we use a practical example to illustrate the use of the proposed approach and demonstrate its superiority compared to the method used in Xu(2007a). The results show that the proposed model can decrease the total deviation and avoid results that bias toward the views of a particular expert. Furthermore, this method deals with the shortcomings caused by the different dimensions and scopes and ensures that the group opinion fairer and more accurate.

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