由於科技進步及全球化的影響，企業經營的觸角已擴展到世界各地。為了有效的提升企業運作的效率，及搭配不斷變動的商業流程。企業透過企業資源規劃(ERP)平台，希望能以最快速的流程來逹成顧客的需求。由於企業本身的競爭力的增強主要透過不斷的學習來獲得。因此，一個有效的教育訓練方式，能有效的增強企業的核心競爭力。過去，企業安排教育訓練行程時，需考慮各種決策因子，包括人為和非人為因素。為了能有效的協調這些因素，相關人員總是花費相當多的時間和成本，但效果總是令人失望。本研究主要提出一個二階段的多目標教育訓練排程基因演算法。第一階段為改良式多目標最佳學習擴展路徑基因演算法，第二階段為排程基因演算法。針對第一階段提出的TMOGA演算法，我們以C metric 衡量指標來討論其求解表現。實驗結果顯示，TMOGA的求解結果在收斂性與擴散性上都有不錯的表現，為一有效求解組合性問題之方法。最後並透過企業平台的建置，讓企業管理階層及員工能隨時的進行相關排程，在眾多的考量因素中，選擇出最佳的柏拉圖最佳解。本系統協助企業選擇一排程方案讓使用者能藉由參加教育訓練接收流程的相關知識，協助ERP平台的運作，使得企業經營能以最有利的方式，來因應瞬息萬變的商業環境。

Due to advance of technology and globalization, enterprise extends their business to all over the world. In order to enhance their ability and adapt to dynamic business environment, they use Enterprise Resource Planning (ERP) system to meet the customer’s needs. The competitiveness of enterprise gain from training iteratively, they need an effective way to enhance their core capability. In the past, they consider varied decision factor to schedule training schedule and also spent lots of cost. However, the result can't be satisfied always. We propose two-stage multiobjective training scheduling genetic algorithm. The first stage is enhanced multi-objective optimal expansion path genetic algorithm. The second stage is scheduling genetic algorithm. We use C metric as our performance index to measure performance of first stage algorithm. In our findings, TMOGA algorithm has better solution quality than traditional MOGA algorithm. Finally, we build decision support system to help manager and employee schedule relative training scheduling in their company. The system help employee understand how business processes work and lead ERP system working more effectively. Consequently, enterprise can use the system to find better Pareto optimal solutions among conflicting objectives and help the manager to make a suitable scheduling decision. We believe that better training can enhance firm’s competitiveness to adapt to competitive business environment.

一、中文部份
炬見工作室，ERP規劃理論與實作，博碩文化，2006。
陳啟嘉，基因結構探勘於承接式子群體基因演算法求解多目標組合性問題，元智大學工業工程與管理研究所碩士論文，2006。

二、英文部份
Chang, P. C., J. C. Hsieh, and S. G. Lin. The Development of Gradual Priority Weighting Approach for the Multi-Objective Flowshop Scheduling Problem. International Journal of Production Economics, Vol.79, Iss.3, p.171-181. 2002.
Chen, L., J. Mcphee, et al. A diversified multiobjective GA for optimizing reservoir rule curves. Advances in Water Resources, Vol.30, Iss.5, p.1082-1093. 2007.
Chen, T.-Y. Expanding competence sets for the consumer decision problem. European Journal of Operational Research, Vol.138, Iss.3, p.622-648. 2002.
Cieniawski S E, W. J., Ranjithan S Using genetic algorithms to solve multiobjective groundwater monitoring problem Water Resources Research, Vol.31, Iss.2, p.399-409. 1995.
Coulson, T., Shayo, C., Olfman, L., Tapie-Rohm, C.E.,. ERP Training Strategies: Conceptual Training And The Formation Of Accurate Mental Models. Proceedings of the 2003 SIGMIS conference on Computer personnel research, p.87-97.2003.
Doerner, K. F., W. J. Gutjahr, et al. Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection. European Journal of Operational Research, Vol.171, Iss.3, p.830-841. 2006.
Dorigo, M. e L. M. Gambardella. Ant colonies for the travelling salesman problem. Biosystems, Vol.43, Iss.2, p.73-81. 1997.
E. Zitzler, K. D., L. Thiele. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, Vol.8, Iss.2, p.173-195. 2000.
Fleming, P. J. e R. C. Purshouse. Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice, Vol.10, Iss.11, p.1223-1241. 2002.
Garcia-Martinez, C., O. Cordon, et al. A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. European Journal of Operational Research, Vol.180, Iss.1, p.116-148. 2007.
Glover, F. Tabu search: part I. OSRA Journal on computing, Vol.1, p.190-206. 1989.
Glover, F., J. P. Kelly, et al. Genetic algorithms and tabu search: Hybrids for optimization. Computers & Operations Research, Vol.22, Iss.1, p.111-134. 1995.
Goldberg, D. E. Genetic Algorithms in search, Optimization, and Machine Learning.MA: Addison-Wesley. 1989.
Gomes, A. A., C.H.; Martins, A.G. A multiple objective evolutionary approach for the design and selection of load control strategies. IEEE Transactions on power systems, Vol.19, Iss.2, p.1173-1180. 2004.
Gravel, M., W. L. Price, et al. Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic. European Journal of Operational Research, Vol.143, Iss.1, p.218-229. 2002.
Grefenstette, J. J. Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on Systems, man & cybernetics, p.122-128. 1986.
H. Klaus, M. R., G.G. Gable,. What is ERP? Information Systems Frontiers, Vol.2, Iss.2, p.141-162. 2000.
Ho, S. L., Y. Shiyou, et al. A tabu method to find the Pareto solutions of multiobjective optimal design problems in electromagnetics. Magnetics, IEEE Transactions on, Vol.38, Iss.2, p.1013-1016. 2002.
I. Nonaka. A dynamic theory of organizational knowledge creation. Organization Science, Vol.5, Iss.1, p.14-37. 1994.
J. Horn, N. N., and D. E. Goldberg. A niched pareto genetic algorithm for multiobjective optimization. 1st IEEE Conference Evolutionary Computation, p.82-87.1994.
K. Fujita, N. H., S. Akagi, S. Kimatura, and H. Yokohata. Multi-objective optimal design of automotive engine using genetic algorithm. Design Engineering Technical Conferences DETC'98. Atlanta, Georgia. 1998.
K.C. Tan , Y. H. C., L.H. Lee. A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems. European Journal of Operational Research, Vol.172, Iss.3, p.855-885. 2006.
Kreng, V. B. e C. M. Tsai. The construct and application of knowledge diffusion model. Expert Systems with Applications, Vol.25, Iss.2, p.177-186. 2003.
Li H. L. And Yu P. L. Optimal competence set expansion using deduction graphs. Journal of Optimization Theory and Applications, Vol.80, Iss.1, p.75-91. 1994.
Light, B. Going beyond `misfit' as a reason for ERP package customisation. Computers in Industry, Vol.56, Iss.6, p.606-619. 2005.
Lin, C.-M. Multicriteria-multistage planning for the optimal path selection using hybrid genetic algorithms. Applied Mathematics and Computation, Vol.180, Iss.2, p.549-558. 2006a.
Lin, C.-M. Multiobjective fuzzy competence set expansion problem by multistage decision-based hybrid genetic algorithms. Applied Mathematics and Computation, Vol.181, Iss.2, p.1402-1416. 2006b.
Lin, C.-M. e M. Gen. Multiobjective resource allocation problem by multistage decision-based hybrid genetic algorithm. Applied Mathematics and Computation, Vol.187, Iss.2, p.574-583. 2007.
Lin, P. H. A. C.-Y. Genetic search strategies in multicriterion optimal design: New York: Springer, Vol.4. p.99-107. 1992.
Mcmullen, P. R. An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives. Artificial Intelligence in Engineering, Vol.15, Iss.3, p.309-317. 2001.
Mouritsen, J. e H. T. Larsen. The 2nd wave of knowledge management: The management control of knowledge resources through intellectual capital information. Management Accounting Research, Vol.16, Iss.3, p.371-394. 2005.
Osman, M. S., M. A. Abo-Sinna, et al. An effective genetic algorithm approach to multiobjective resource allocation problems (MORAPs). Applied Mathematics and Computation, Vol.163, Iss.2, p.755-768. 2005.
Park, J.-H., H.-J. Suh, et al. Perceived absorptive capacity of individual users in performance of Enterprise Resource Planning (ERP) usage: The case for Korean firms. Information & Management. In Press, Corrected Proof.
Poorzahedy, H. e O. M. Rouhani. Hybrid meta-heuristic algorithms for solving network design problem. European Journal of Operational Research, Vol.182, Iss.2, p.578-596. 2007.
Riggle, M. Solving the ERP paradox. Intelligent Enterprise Magazine. 3: 52-56 p. 2000.
Robert Jacobs, F. e J. F. C. Ted' Weston. Enterprise resource planning (ERP)--A brief history. Journal of Operations Management, Vol.25, Iss.2, p.357-363. 2007.
S. Obayashi, D. S., Y. Takeguchi, and N. Hirose. Multiobjective Evolutionary Computation for Supersonic Wing-Shape Optimization. IEEE Transactions on Evolutionary Computation, Vol.4, Iss.2, p.182-187. 2000.
S. Obayashi, T. T., and T. Nakamura. Multiobjective Genetic Algorithm Applied to Aerodynamic Design of Cascade Airfoils. IEEE Transactions on Industrial Electronics, Vol.47, Iss.1, p.570-577. 2000.
Schaffer, J. D. Multi-objective optimization with vector evaluated genetic algorithms. 1st Int. Conf. Genetic Algorithms, p.93-100.1985.
Schaffer, J. D. A. R., L. Caruana; J. Eshelman, and D. Rajarshi. A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. The 3rd International Conference on Genetic Algorithms and Their Applications, p.51-60.1989.
Sein, M. K. A. B., R.P.,. Individual Differences and Conceptual Models in Training Novice End-Users. Human Computer Interaction, Vol.4, Iss.4, p.197-229. 1989.
Solimanpur, M., P. Vrat, et al. A neuro-tabu search heuristic for the flow shop scheduling problem. Computers & Operations Research, Vol.31, Iss.13, p.2151-2164. 2004.
Tan, K. C., C. Y. Cheong, et al. Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation. European Journal of Operational Research, Vol.177, Iss.2, p.813-839. 2007.
Thiele, E. Z. A. L. Multiobjective evolutionary algorithms: a comparative case study and strengthen Pareto approach. IEEE Transactions on Evolutionary Computation, Vol.3, Iss.4, p.257-271. 1999.
Wang, E. T. G. e J. H. F. Chen. Effects of internal support and consultant quality on the consulting process and ERP system quality. Decision Support Systems, Vol.42, Iss.2, p.1029-1041. 2006.
Xu, J., M. Sohoni, et al. A dynamic neighborhood based tabu search algorithm for real-world flight instructor scheduling problems. European Journal of Operational Research, Vol.169, Iss.3, p.978-993. 2006.
Xue, Y., H. Liang, et al. ERP implementation failures in China: Case studies with implications for ERP vendors. International Journal of Production Economics, Vol.97, Iss.3, p.279-295. 2005.
Yalcinoz, T. e O. Koksoy. A multiobjective optimization method to environmental economic dispatch. International Journal of Electrical Power & Energy Systems, Vol.29, Iss.1, p.42-50. 2007.
Yu P. L. Forming Winning Strategies: An Integrated Theory of Habitual Domain. New York: Springer. 1990.
Yu P. L. And Zhang D. Optimal expansion of competence set and decision support. Information System and Operational Research, Vol.30, Iss.2, p.68-85. 1992.
Yu P. L. And Zhang D. Marginal analysis for competence set expansion. Journal of Optimization Theory and Applications, Vol.76, Iss.1, p.87-109. 1993.
Zhao, J.-H., Z. Liu, et al. Reliability optimization using multiobjective ant colony system approaches. Reliability Engineering & System Safety, Vol.92, Iss.1, p.109-120. 2007.
Zheng, S., Yen, D., Tarn, J.,. The new spectrum of the cross-enterprise solution: the integration of supply chain management and enterprise resources planning systems. The Journal of Computer Information Systems, Vol.41, Iss.1, p.84-93. 2000.