中文摘要
在全球化及技術提昇的環境下，企業不斷創造出跨功能任務的新工作。現代企業不論是服務業(如銀行業)或高科技產業(如半導體產業)皆面臨快速反應及多樣化的小批量生產等問題。解決問題的方式需要企業具有高度的組織彈性。組織彈性將是提昇企業競爭力的最佳武器。然而，組織彈性能夠成功的最重要因素來自於人員彈性。多功能技能人員將能夠滿足企業人力的調派，提高企業的經營績效。因此，分配適當的人員到適當的位置組成和諧的跨功能小組是決策者最重要的任務之ㄧ。在人力資源管理中，多功能技能人力的指派被廣泛地研究；其中，利用職位要求標準的調整和人員受訓的安排是人力資源部門中兩項重要的作業。
本論文提出二個研究議題詳細探討企業藉由調整各職位的要求標準及經由職能訓練提高個人技能的多功能技能員工指派問題。第一個議題是建構一個具有回饋機制的系統方法，此方法同時考慮職位間具有相依性且團隊成員間能力差異小。然而，大部分文獻較少同時討論此二項觀點。為了組成和諧的跨功能小組，此方法的目的是使得職位與人員能夠獲得最佳契合。在一個模糊的環境下，本研究發展一個0-1整數的雙目標(BOBIP)模式並且將它轉換成模糊雙目標規劃(FBOGP)模式。一個精心設計的啟發式演算法用來求解模式中的四個參數，並且利用LINDO 8.0求解FBOGP模式。結果顯示在可以接受的滿意程度內，本方法比窮舉法更有效率，並且獲得許多重要管理上的意涵。第二個議題是延伸第一個議題並增加二個特性：(1)每一位員工的技能應該超過公司在該職位所設定主要技能的最低要求(亦即，非補償技能因素)及滿足該職位下所需各種技能的加權分數的最低綜合能力指標(亦即，補償技能因素)，則該員工具有分配到該職位的資格；文獻上大部分學者僅考慮補償技能因素；(2)在有限的個人訓練預算及整體總預算下，企業利用員工職能訓練提升技能達成公司的要求績效。實務個案應用結果顯示，本論文所提出的經由訓練的多功能技能員工(MFSPTT)模式在整體受訓預算限制、個人受訓預算限制及各種的績效要求下比個案公司現行的方法得到較好的人員指派結果。
本論文的二種解法與最佳解法相比是更有效率的方法，適合實務應用。因此，企業在人力資源規劃下設計決策支援系統時，可考慮使用這些啟發式解法。

Abstract
In this globalization and technology advanced environment, cross-functional new jobs are constantly created. Today’s businesses, either companies in service industry such as banks or firms in high-tech industry such as semiconductor manufacturers, are facing the problems of quick response and small lot-size diversified production. They can solve the problems with highly organizational flexibility and will be the best tool for increasing a company‘s competitiveness. However, one of the most important factors for successfully implementing organizational flexibility is personnel flexibility. Multi-functional skilled personnel virtually can satisfy the workforce assignment requirements and increase the business performance. Therefore, assigning the right employees to the right positions and then creating a collaboratively cross-functional team is one of the most crucial tasks performed by the decision makers of a company. The study on personnel assignment has received much attention in human resources management. Adjusting the requirements of positional standard and scheduling the personnel training program are two important activities in a human resources department.
For this reason, this dissertation addresses two research topics to discuss in detail multi-functional skilled personnel assignment problem by adjusting the requirements of positional standard and advancing employees’ skills through training. The first topic is to propose a systematic approach with a feedback mechanism in which the interdependences among positions and the differences among the selected employees are considered simultaneously. Unfortunately, the two combined considerations have rarely been discussed in the literature. The purpose of this approach is to obtain the best matching of candidates and positions in order to organize a collaboratively cross-functional team. In a fuzzy environment, we formulate a bi-objective binary integer programming (BOBIP) model and translate it into a fuzzy bi-objective goal programming (FBOGP) model. An elaborately designed heuristic algorithm is developed to determine the appropriate values of four important parameters in the FBOGP model, which is solved using LINDO 8.0. The results indicate that the proposed approach achieves the acceptable satisfaction level and requires less computation time than the brute force enumerative method. Additionally, several important findings and managerial implications can be observed from the results of solving the FBOGP model. The real-life usefulness of the proposed method is demonstrated by a practical application. The second topic is to extend the first topic and two important characteristics are added: (1) The non-compensation skill factor that every candidate should meet the necessary conditions of each position is first considered, and then the lowest weighted average of the skill score should be satisfied according to the compensation factor among skills. In other words, each multi-skilled employee who has to pass the required standard can be assigned to the position. In the literature, most scholars emphasized the mutual compensation instead of non-compensation factors among the candidate’s skills for personnel allocation. (2) We advance employees’ skills through training to achieve the projected performance set by a company under the conditions of limited budget for a member’s training and the total training cost for all members in a team rather than adjusting the requirements of positional standard. The empirical results reveal that the proposed multi-functional skilled personnel through training (MFSPTT) model can obtain a better solution to match the necessary performance requirement under the limited total training cost for all members in a team than the current method adopted by the case company.
In summary, the two proposed methods are suitable for practical application since these methods are more efficient than the optimal solution method. Therefore, they can be adopted by practitioners in developing decision support systems for managing human resources.

參考文獻
1. 李志隆撰，邱煥能指導，三種供應商評選方法之研究，國立台灣科技大學工業管理研究所碩士論文，民國94年。
2. Abassi, S.M. and Hollman, K.W., 1994. Self-managed teams: the productivity breakthrough of the 1990s. Journal of Managerial Psychology,9(7), pp.25-30
3. Abernathy, W.J., Baloff, N., Hershey, J.C. and Wandel, S., 1973. A three-stage manpower planning and scheduling model-a service-sector example, Operations Research, 21(5), pp.693-711.
4. Aleskerov, F., Ersel, H. and Yolalan, R., 2003. Personnel allocation among bank branches using a two-stage multi-criterial approach. European Journal of Operational Research, 148(1), pp.116-125.
5. Atkinson, J., 1984. Manpower strategies for flexible organizations. Personnel Management,16(8), pp.28-31.
6. Baird, L. and Meshoulam, I., 1988. Managing two fits of strategic human resource management. Academy of management Review, 13(1), pp.116-128.
7. Blyton, P., 1996. Workforce Flexibility. The handbook of human resource management. Oxford: Blackwell.
8. Bonissone, P.P. and Decker, K.S., 1986. Selecting uncertainty calculi and granularity: an experiment in trading-off precision and complexity, in: L.H. Kanal, J.F. Lemmer (Eds.), Uncertainty in Artificial Intelligence, North-Holland; Amsterdam.
9. Butkiewicz, B.S., 2002. Selection of staff for enterprise using fuzzy logic. IEEE International Conference, 4, pp.6-9.
10. Campbell, G.M. and Diaby, M., 2002. Development and evaluation of an assignment heuristic for allocating cross-trained workers. European Journal of Operational Research, 138(1), pp.9-20.
11. Carlsson, C. and Fuller, R., 1995. Multiple criteria decision making: the case for interdependence. Computers & Operations Research, 22(3), pp.251-260.
12. Chen, C.F. and Chien, L.F., 2008. Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(3), pp.280-290.
13. Chiu, N. C. and Chen, H. M., 2005. An optimal algorithm for solving the dynamic lot-sizing model with learning and forgetting in setups and production. International Journal of Production Economics, 95(2), pp.179-193.
14. Corominas, A., Ojeda, J. and Pastor, R., 2005. Multi-objective allocation of multi-function workers with lower bounded capacity. Journal of the Operational Research Society, 56(7), pp.738-743.
15. Dubois, D. and Prade, H., 1980. Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York.
16. Fitzpatrick, E.L. and Askin, R.G., 2005. Forming effective worker teams with multi-functional skill requirements. Computers & Industrial Engineering, 48(3), 593-608.
17. Hafeez, K., Zhang, Y.B. and Malak, N., 2002. Determining key capabilities of a firm using analytic hierarchical process. International Journal of Production and Economics, 76(1), pp.39-51.
18. Herrera F., Herrera, V. E. and Verdegay, J.L., 1996. A linguistic decision process in group decision making. Group Decision and Negotiation, 5(2), pp.165-176.
19. Herrera, F., Lopez, E., Mendana, C. and Rodriguez, M.A., 1999. Solving an assignment-selection problem with verbal information and using genetic algorithms. European Journal of Operational Research, 119(2), pp.326-337.
20. Herrera, F. and Herrera, V. E., 2000. Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Sets and Systems, 115(1), pp.67-82.
21. Herrera, F., Lopez, E., Mendana, C. and Rodriguez, M.A., 2001. A linguistic decision model for personnel management solved with a linguistic bi-objective genetic algorithm. Fuzzy Sets and Systems, 118(1), pp.47-64.
22. Herrera, V. E., Martinez, L., Mata, F. and Chiclana, F., 2005. A Consensus Support System Model for Group Decision-making Problems with Multi-granular Linguistic Preference Relations. IEEE Trans. On Fuzzy Systems, 13(5), pp.644-658.
23. Hottenstein, M.P. and Bowman, S. A., 1998. Cross-Training and Worker Flexibility: A Review of DRC System Research. Journal of High Technology Management Research, 9(2), pp.157-174.
24. Hoyt, J. and Matuszek, T., 2001. Testing the contribution of multi-skilled employees to the financial performance of high-tech organizations. Journal of High Technology Management Research, 12(2), pp.167-181.
25. Kacprzyk, J., 1986. A down-to-earth managerial decision making via a fuzzy-logic-based representation of common sense knowledge. In: Pau LF, editor. Artificial intelligence in economics and management. Amsterdam: Elsevier Science Publishers,
26. Karsak, E.E., Sozer, S. and Alptekin, S.E., 2003. Product planning in quality function deployment using a combined analytic network process and goal programming approach. Computers & Industrial Engineering, 44(1), pp.171-190.
27. Karsak, E.E., 2000. A fuzzy multiple objective programming approaches for personnel selection. Systems, Man, and Cybernetics. IEEE, International Conference, 3, pp.2007-2012.
28. Kateter, M., 1993. Cross-training: the tactical view. Training, 30(1), pp.35-40.
29. Kher, H.V., Malhotra, M.K., Philipoom, P.R. and Fry, T.D., 1999. Modeling simultaneous worker learning and forgetting in dual resource constrained systems. European Journal of Operational Research, 115(1), pp.158-172.
30. Kirkman, B.L. and Rosen, B., 1999. Beyond self-management: antecedents and consequences of team empowerment. Academy of Management Journal, 42(1), pp.58-74.
31. Korvin, A.D., Shipley, M.F. and Kleyle, R., 2002. Utilizing fuzzy compatibility of skill for selection in multi-phase projects. Journal of Engineering and Technology Management, 19(3-4), pp.307-319.
32. Lee, J.W. and Kim, S.H., 2000. Using analytic network process and goal programming for interdependent information system project selection. Computers & Operations Research, 27(4), pp.367-382.
33. Lee, J.W. and Kim, S.H., 2001. An integrated approach for interdependent information system project selection. International Journal of Project Management, 19(2), pp.111-118.
34. Lengnick-Hall, C.A. and Lengnick-Hall, M.L., 1988. Strategic human resources management: A review of the literature and a proposed typology, Academy of Management Review, 13(3), pp.454-470.
35. Liang, G.S. and Wang, M.J., 1992. Personnel placement in a fuzzy environment. Computers & Operations Research, 19(2), pp.107-121.
36. Liang, G.S. and Wang, M.J., 1994. Personnel selection using fuzzy MCDM algorithm. European Journal of Operational Research, 78(1), pp.22-33.
37. McCune, J.C., 1994. One the train gang. Management Review, 83(10), pp.57-60.
38. Milliman, J., Von Glinow, M. A. and Nathan, M., 1991. Organizational life cycles and strategic international human resource management in multinational companies: implications for congruence theory, Academy of Management Review, 16(2), pp.318-339.
39. Montemayor, E., 1996. Congruence between pay policy and competitive strategy in high- performing firms, Journal of Management, 22(6), pp.889-908.
40. Narsimhan, R., 1980. Goal programming in a fuzzy environment. Decision Sciences, 11(3), pp.325-336.
41. Ngai, E. W. T. and Wat, F. K. T., 2003. Design and development of a fuzzy expert system for hotel selection. Omega, 31(4), pp.275-286.
42. Opricovic, S. and Tzeng, G.H., 2004. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), PP.445-455.
43. Poole, M. and Warner, M., 1998. Handbook of human resource management. London: International Thomson Business Press.
44. Robbins, S. P., 1994. Essentials of organizational behavior. Englewood Cliffs, NJ: Prentice Hall.
45. Rogerson, l., 1993. Cover your bases. Small Business Reports, 18(7), pp.60-64.
46. Saaty, T. L., 1980. The analytic hierarchy process. New York: Irwin McGraw-Hill.
47. Saaty, T. L. and Takizawa, M., 1986. Dependence and independence: From linear hierarchies to nonlinear networks. European Journal of Operational Research, 26(2), pp.229-237.
48. Saaty, T. L., 1996. Decision making with dependence and feedback: The analytic network process. Pittsburgh, PA: RWS Publications.
49. Sayin, S. and Karabati, S., 2007. Assign cross-trained workers to departments: A two-stage optimization model to maximize utility and skill improvement. European Journal of Operational Research, 176(3), pp.1643-1658.
50. Siferd, S.P. and Benton, W.C., 1992. Workforce staffing and scheduling: Hospital nursing specific models. European Journal of Operational Research, 60(3), pp.233-246.
51. Slomp J., Bokhorst, J. A.C. and Molleman, E., 2005. Cross-training in a cellular manufacturing environment. Computers & Industrial Engineering, 48(3), pp.609-624.
52. Toroslu, I.H., 2003. Personnel assignment problem with hierarchical ordering constraints. Computers & Industrial Engineering, 45(3), pp.493-510.
53. Toroslu, I.H. and Arslanoglu, Y., 2007. Genetic algorithm for the personnel assignment problem with multiple objectives. Information Sciences, 177(4), pp.787-803.
54. Yaakob, S.B. and Kawata, S., 1999. Workers’ placement in an industrial environment. Fuzzy Sets and Systems 106(3), pp.289-297.
55. Yu, P.L., 1973. A class of solutions for group decision problems. Management Science, 19(8), pp.936-946.
56. Yu, P.L., 1985. Multiple-Criteria Decision Making. Concepts, Techniques and Extensions. Plenum Press, New York.
57. Yurdakul, M., 2003. Measure long-term performance of a manufacturing firm using the analytic network process approach. International Journal of Production Research, 41(11), pp.2501-2529.
58. Zadeh, L.A., 1965. Fuzzy sets. Information and Control, 8(2), pp.338-353.
59. Zeleny, M., 1973. Compromise programming. In: Cochrane, J.L., Zeleny, M. (Eds), Multiple Criteria Decision Making. University of South Carolina, Columbia, SC, pp.262-301.
60. Zimmermann, H.J., 1978. Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), pp.45-55.