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題名：多屬性群體決策方法選擇之研究
作者：張育維
作者(外文)：Yu-Wei Chang
校院名稱：國立成功大學
系所名稱：交通管理學系碩博士班
指導教授：張有恆
學位類別：博士
出版日期：2009
主題關鍵詞：群體決策；多屬性決策；選擇問題；層級分析法；Multi-attribute decision making；Group decision making；Selection；AHP
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決策為組織或個人每日都在進行的活動，而多屬性決策則為管理者經常使用的決策技巧，常被用來處理具有多項評估屬性的決策問題上。過去已有學者提出許多不同的方法用以解決多屬性決策問題，由於不同方法的使用對於相同的問題而言，常會產生不同的評估結果，因而常造成管理者決策時的困擾。此外，在一般決策制定過程中，往往集結各種不同領域專家的意見而形成群體決策。在群體決策中，由於不同領域專家對於相同問題常會有不同的看法，故需加以整合而成專家共識。當各種整合方法所產生的結論不同時，如何找出一個值以代表整體專家共識，可謂一重要議題。
依據上述所示，吾人認為在多屬性群體決策中，仍舊有兩個議題值得更進一步深入研究。一為不同多屬性決策方法所產生不同結果問題，一為群體決策整合問題。因此，本研究擬訂一套多屬性群體決策方法選擇模式，協助管理者在所有合適的多屬性群體決策方法中選擇最適合的方法，使群體決策的結果與參與決策專家的看法整體而言一致性程度最高，最為接近每位專家的看法，讓參與決策的專家更能願意接受群體決策的結果，並將本研究所發展的模式應用在不同類型的多屬性群體決策範疇上。
在多屬性群體決策方面，主要在層級分析法架構下，依據整合專家共識值方式分為-算術平均數或幾何平均數，加總方式分成-加總個別判斷(AIJ)與加總個別排序(AIP)及三種常見的多屬性方法- SAW、WP及TOPSIS，共整理出九種常見的多屬性群體決策方法，並以「大眾運輸法定優待票價差額補貼財源籌措之研究」為例，說明本研究所提出模式之運算過程。研究結果發現，採取加總個別判斷之幾何平均數整合個別專家意見及多屬性決策法中的WP，其所產生的財源分配結果和個別專家意見一致性程度最高，故建議管理者選用此組方法，因其所產生的結果與專家意見看法最為接近，能更真實反應專家對問題的看法。
在模糊多屬性群體決策方面，依據群體決策整合方式分為-算術平均數與幾何平均數，三種常見的多屬性決策方法- SAW、WP及TOPSIS及三種常見的模糊數排序方法-重心法、等級平均加總法及距離量測法，共整理出十八種常見的模糊多屬性群體決策方法，並以「綠色公車技術式選擇之研究」為例，說明本研究所提方法之計算過程。研究結果發現，採取算術平均數整合個別專家意見，重心法解模糊及SAW進行評估方案選擇，所產生的結果將和個別專家意見一致性程度最高，故建議管理者選擇此組方法，因其所產生的結果與專家的意見最為接近，能更真實、公正反應參與決策專家對問題的看法。
值得注意的是，不同多屬性決策問題可能因為資料型態及所參與決策的專家不同，而有不同的最適合的方法。本研究所研擬的多屬性群體決策方法選擇模式，能應用在一般多屬性群體決策問題上，將能更真實反應參與決策專家的內心感受，有助於協助管理者選擇合適的評估方法，進而提高群體決策品質。
Multi-attribute decision making (MADM) is one of the most well known branches of decision making. Several methods have been proposed for solving related problems, but a major criticism of MADM is that different techniques may yield different results for the same problem. Group decision making is an active area of research within MADM, which attempts to aggregate individual judgments into a group judgment. However, different group preference aggregation methods will often lead to different results.
Under multiattribute group decision making, various individual preference aggregation methods and MADM approaches often lead to different outcomes for selecting or ranking decision alternatives involving multiple attributes. This suggests that the choice of a specific method will significantly influence the ranking outcome. To help managers make better decisions, a mechanism is thus required for selecting an appropriate outcome for a given MADM problem.
An empirical study, entitled “Funding Source Allocation for Public Transportation Subsidy”, was conducted to explain how the proposed approach can be used to help select the best ranking outcome under multiattribute group decision making. Six possible funding sources embodied by four attributes were proposed to subsidize the shortage caused by the welfare policy. AHP was used in data analysis, while two main approaches: aggregating individual judgment (AIJ) and aggregating individual priority (AIP) were used for aggregating information, and SAW, WP and TOPSIS were used for scoring phase. Nine methods were finally used to allocate funding sources. The results show that the method “AIJ and geometric mean for aggregation and WP for the scoring phase” is the best, since its results have the highest degree of consistency degree with experts.
Another empirical study, entitled “Green Bus Technology Selection”, was conducted to explain how the proposed approach can be used to select the best ranking outcome under fuzzy multiattribute group decision making. Six possible green bus technologies embodied by six attributes were proposed to select the most appropriate one for Taiwan. Arithmetic and geometric means were used to integrate the fuzzy judgment values of evaluators. SAW, WP and TOPSIS were used for the scoring phase and three defuzzification methods were used to convert the fuzzy data into crisp scores. Eighteen methods were finally formed for solving the problem.The results show that the method “arithmetic mean for aggregating the fuzzy judgment, SAW for MADM phase and center-of-area method for defuzzification” is the best, since it has the highest degree of consistency with experts. Finally, different MADM problems and data sets may lead to the selection of a different method.
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