人力資源是企業最重要的資產之一，尤其在中高階層管理人員的選擇上，更是與公司的未來發展有著密不可分的關係。因應高階職位的需求，對於備選人員有著很多的評估因素，如何根據這些因素，從眾多候選者當中選出最符合該職位需求的人，為公司發展而言是一個重要的議題。而在考量決策者評估時會涉及自身主觀想法，這些主觀想法往往含有不確定性與模糊性，故有許多學者以模糊理論為基礎，結合群體決策(group decision making)及多屬性決策方法(Multi-Attribute Decision-Making ,MADM)進行群體評估。
然而在群體決策的過程當中，雖然有著集思廣益的效果，但評估者間的意見有時會造成衝突，使得決策者的意見無法被充分表達；而使用多屬性決策方法時，各種方法在不同情境下有各自的不足。為使人員選拔被應用於更廣泛的決策情境，本研究期望建構一套以證據理論為基礎之人員晉升決策系統，在評估者評估人員主觀屬性部分，為了能讓評估者更好的表達其意見，將會使用直覺式模糊數來表達評估者對於人員之正面、負面資訊，並藉由相似性測度轉換評估者評估於不同屬性類別後，利用證據組合規則做評估者資訊之整合。
在最終人員排序的部分，以信任區間(belief interval)進行評估排序，信任區間可藉由信任函數及似真函數直觀地反應原始評估者評估之人員表現高低程度，並可合併不同類別之表現結果，使排序結果更可以反應出評估者之原始意見。

關鍵詞：證據理論、證據組合規則、人員晉升、直覺式模糊數

Human resources are one of the important assets of enterprises, especially in the promotion of senior managers, since it’s inseparable from the future development. For demand of those positions, there are many evaluation factors for candidates. How to select the most suitable person for the position from the many candidates based on the determined factors is an important issue. In the past, the evaluation method was often developed using Multi-Attribute Decision-Making (MADM) approach by a group of decision makers based on fuzzy set theory because of the subjective judgement of the evaluators.
However, in the process of group decision making, the conflicts may happen among the opinions of the evaluators so that the consensus of the decision makers cannot be achieved. In addition, MADM approaches usually have some shortcomings. To overcome those shortcomings of traditional group decision making and MADM methods, we propose a new solution process based on fuzzy evidence theory. In order to make the evaluators to better express their opinions, intuitionistic fuzzy numbers will be used to express the positive and negative information of the evaluators. The evaluation values will be represented as different categories of attribute using the similarity measure. Then, integrate the evaluation values and prioritize candidates by combination rule. We illustrate the proposed approach by an example to deal with a personnel promotion problem. The results show that the proposed approach is more flexible and reasonable for real applications than the existing methods.

Key words: Personnel promotion, Evidence theory, Combination rule, Intuitionistic fuzzy number

中文部分
李冠霖. (2019). 應用證據理論於失效模式與效應分析.成功大學工業與資訊管理學系碩 士學位論文, http://repository.ncku.edu.tw/handle/987654321/186445
荊裕荃. (2019). 應用集群方法於直覺式模糊之群體決策模式.成功大學工業與資訊管理學系碩士學位論文, https://hdl.handle.net/11296/2evx75.

英文部分
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. In Intuitionistic fuzzy sets 1-137.
Bali, O., Dagdeviren, M., & Gumus, S. (2015). An integrated dynamic intuitionistic fuzzy MADM approach for personnel promotion problem. Kybernetes, 44(10) , 1422-1436.
Boran, F. E., & Akay, D. (2014). A biparametric similarity measure on intuitionistic fuzzy sets with applications to pattern recognition. Information Sciences, 255, 45-57.
Borman, W. C., Hanson, M. A., & Hedge, J. W. (1997). Personnel selection. Annual Review of Psychology, 48(1), 299-337.
Burillo, P., Bustince, H., & Mohedano, V. (1994). Some definitions of intuitionistic fuzzy number. First properties. Proceedings of the 1st Workshop on Fuzzy Based Expert Systems. Sofia Bulgaria, 53-55.
Baykasoğlu, A., & Gölcük, İ. (2019). An interactive data-driven (dynamic) multiple attribute decision making model via interval type-2 fuzzy functions. Mathematics, 7(7), 584.
Castillo, C. N., Degamo, F. K., Gitgano, F. T., Loo, L. A., Pacaanas, S. M., Toroy, N., Ocampo, C. O. (2017). Appropriate criteria set for personnel promotion across organizational levels using analytic hierarchy process (AHP). International Journal of Production Management and Engineering, 5(1), 11-22.
Chen, L.-S., & Cheng, C.-H. (2005). Selecting IS personnel use fuzzy GDSS based on metric distance method. European Journal of Operational Research, 160(3), 803-820.
Campos, F., & Cavalcante, S. (2003, October). An extended approach for Dempster-Shafer theory. In Proceedings Fifth IEEE Workshop on Mobile Computing Systems and Applications 338-344.
Chen, S.-J., & Chen, S.-M. (2003). Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. IEEE Transactions on Fuzzy Systems, 11(1), 45-56.
Chen, S.-M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311-319.
Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.
Delgado, M., Vila, M. A., & Voxman, W. (1998). A fuzziness measure for fuzzy numbers: Applications. Fuzzy Sets and Systems, 94(2), 205-216.
Dubois, D., & Prade, H. (1978). Operations on fuzzy numbers. International Journal of Systems Science, 9(6), 613-626.
Dursun, M., & Karsak, E. E. (2010). A fuzzy MCDM approach for personnel selection. Expert Systems with Applications, 37(6), 4324-4330.
Hsu, H.-M., & Chen, C.-T. (1996). Aggregation of fuzzy opinions under group decision making. Fuzzy Sets and Systems, 79(3), 279-285.
Hung, W.-L., & Yang, M.-S. (2004). Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance. Pattern RecognitionLetters, 25(14), 1603-1611.
Hwang, C.-L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple Attribute Decision Making, 58-191.
Hu, J., Zhang, Y., Chen, X., & Liu, Y. (2013). Multi-criteria decision making method based on possibility degree of interval type-2 fuzzy number. Knowledge-Based Systems, 43, 21-29.
Karsak, E. E. (2001). Personnel selection using a fuzzy MCDM approach based on ideal and anti-ideal solutions. In Multiple Criteria Decision Making in the New Millennium, 393-402.
Kokoc, M., & Ersoz, S. (2020). Personnel Evaluation Under Intuitionistic Fuzzy Environment. International Journal of Intelligent Systems and Applications in Engineering, 8(3), 137-146.
Li, D., & Cheng, C. (2002). Fuzzy multiobjective programming methods for fuzzy constrained matrix games with fuzzy numbers. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(04), 385-400.
Liang, Z., & Shi, P. (2003). Similarity measures on intuitionistic fuzzy sets. Pattern Recognition Letters, 24(15), 2687-2693.
Mahapatra, G. S., & Roy, T. K. (2009). Reliability evaluation using triangular intuitionistic fuzzy numbers arithmetic operations. World Academy of Science, Engineering and Technology, 3(2), 350-357.
Mitchell, H. B. (2003). On the Dengfeng–Chuntian similarity measure and its application to pattern recognition. Pattern Recognition Letters, 24(16), 3101-3104.
Petrovic‐Lazarevic, S. (2001). Personnel selection fuzzy model. International Transactions in Operational Research, 8(1), 89-105.
Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26.
Shafer, G. (1976). A mathematical theory of evidence(Vol. 42). Princeton university press.
Song, Y., & Wang, X. (2017). A new similarity measure between intuitionistic fuzzy sets and the positive definiteness of the similarity matrix. Pattern Analysis and Applications, 20(1), 215-226.
Szmidt, E., & Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems, 114(3), 505-518.
Szmidt, E., & Kacprzyk, J. (2001). Entropy for intuitionistic fuzzy sets. Fuzzy Sets and Systems, 118(3), 467-477.
Ye, J. (2011). Cosine similarity measures for intuitionistic fuzzy sets and their applications. Mathematical and Computer Modelling, 53(1-2), 91-97.
Yeh, C. H. (2003). The selection of multiattribute decision making methods for scholarship student selection. International Journal of Selection and Assessment, 11(4), 289-296.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199-249.
Zadeh, L. A. (1986). A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. AI Magazine, 7(2), 85-85.
Zeleny, M., & Cochrane, J. (1982). Multiple criteria decision making McGraw-Hill New York, 34.
Zhang, H., & Yu, L. (2013). New distance measures between intuitionistic fuzzy sets and interval-valued fuzzy sets. Information Sciences, 245, 181-196.