企業在新產品的前期規劃和組合之選定是影響企業生存的重要議題，不僅僅影響企業獲利，也對其技術佈局、市場地位、營運效率都有著至關重要的影響力，為了確保其投入的資源配置 (Resource allocation) 能有效的應用，產品和技術的開發方向須被導引至與公司的經營目標保持一致。
本研究提出了三階段的研究架構，以導引公司領導人在多目標追求下進行產品組合決策及資源配置，基於數據性的結果，提供決策者有更多的選擇，而不是單一解決方案。第一階段導引新產品開發方向和公司的策略目標一致；第二階段協助決策者在衡量標準無法精確計量時進行量化；第三階段進行多目標專案組合的選擇 (Project portfolio selection)。本研究利用層級架構分析法 (Analytic hierarchy process, AHP)、模糊方法 (Fuzzy method) 和非支配排序基因演算法 (Non-dominated sorting generic algorithm II, NSGA-II)，實現決策者在各目標的平衡點中找出適合的選擇，其對應的專案組合不會偏向某一個目標而忽略其他目標，有助於決策者實際操作中做出決策並推動後續的執行。

The early planning and portfolio selection of new products are critical issues impacting a company’s survival. These decisions not only affect profitability but also play a crucial role in shaping technology positioning, market standing, and operational efficiency. To ensure the effective application of allocated resources, product and technology development directions must align closely with the company’s overall strategic goals.
This study proposes a three-stage hybrid approach framework to guide company leaders in making product portfolio decisions and allocating resources effectively within a multi-objective context. Instead of providing a single solution, it offers decision-makers with multiple options based on data-driven results. The first stage aligns the direction of new product development (NPD) with the company’s strategic goals; the second stage aids decision-makers in quantifying metrics when precise measurement is challenging; and the third stage involves multi-objective project portfolio selection. The study employs the Analytic hierarchy process (AHP), fuzzy method, and non-dominated sorting genetic algorithm II (NSGA-II) to identify effective solutions across various trade-offs. This approach enables balanced project portfolios that address multiple objectives without favoring one at the expense of others, facilitating practical decision-making and execution for leaders.

Abbasi, D., Ashrafi, M., & Ghodsypour, S. H. (2020). A multi objective-BSC model for new product development project portfolio selection. Expert Systems with Applications, 162, 113757.
Ahmadi-Javid, A., Fateminia, S. H., & Gemünden, H. G. (2020). A method for risk response planning in project portfolio management. Project Management Journal, 51(1), 77-95.
Albano, T. C., Baptista, E. C., Armellini, F., Jugend, D., & Soler, E. M. (2019). Proposal and solution of a mixed-integer nonlinear optimization model that incorporates future preparedness for project portfolio selection. IEEE Transactions on Engineering Management, 68(4), 1014-1026.
Amiri, M. P. (2010). Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(9), 6218-6224.
Bai, L., Han, X., Zhang, Y., & Xie, X. (2023). Optimal project portfolio selection considering cascading failure among projects. IEEE Transactions on Engineering Management, 71, 4750-4760.
Carraway, R. L., & Schmidt, R. L. (1991). Note—An improved discrete dynamic programming algorithm for allocating resources among interdependent projects. Management Science, 37(9), 1195-1200.
Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9.
Cooper, R. G. (2019). The drivers of success in new-product development. Industrial Marketing Management, 76, 36-47.
Cooper, R. G., & Edgett, S. J. (2001). Portfolio management for new products: picking the winners. Product Development Institute, Ancaster, Ontario, Canada, 3-13.
Cooper, R., Edgett, S., & Kleinschmidt, E. (2001). Portfolio management for new product development: results of an industry practices study. R&D Management, 31(4), 361-380.
Cooper, R. G., Edgett, S. J., & Kleinschmidt, E. J. (1999). New product portfolio management: practices and performance. Journal of Product Innovation Management: An International Publication of The Product Development & Management Association, 16(4), 333-351.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
Dey, P. K. (2006). Integrated project evaluation and selection using multiple-attribute decision-making technique. International Journal of Production Economics, 103(1), 90-103.
Dixit, V., & Tiwari, M. K. (2020). Project portfolio selection and scheduling optimization based on risk measure: a conditional value at risk approach. Annals of Operations Research, 285(1), 9-33.
Dubois, D., & Prade, H. (2015). The legacy of 50 years of fuzzy sets: a discussion. Fuzzy Sets and Systems, 281, 21-31.
Ecer, F., & Torkayesh, A. E. (2022). A stratified fuzzy decision-making approach for sustainable circular supplier selection. IEEE Transactions on Engineering Management, 71, 1130-1144.
Ertenlice, O., & Kalayci, C. B. (2018). A survey of swarm intelligence for portfolio optimization: algorithms and applications. Swarm and Evolutionary Computation, 39, 36-52.
Fernandez, E., Lopez, E., Mazcorro, G., Olmedo, R., & Coello, C. A. C. (2013). Application of the non-outranked sorting genetic algorithm to public project portfolio selection. Information Sciences, 228, 131-149.
Fox, G. E., Baker, N. R., & Bryant, J. L. (1984). Economic models for R and D project selection in the presence of project interactions. Management Science, 30(7), 890-902.
Frej, E. A., Ekel, P., & de Almeida, A. T. (2021). A benefit-to-cost ratio based approach for portfolio selection under multiple criteria with incomplete preference information. Information Sciences, 545, 487-498.
Gemici-Ozkan, B., Wu, S. D., Linderoth, J. T., & Moore, J. E. (2010). OR PRACTICE—R&D project portfolio analysis for the semiconductor industry. Operations Research, 58(6), 1548-1563.
Gutjahr, W. J., Katzensteiner, S., Reiter, P., Stummer, C., & Denk, M. (2010). Multi-objective decision analysis for competence-oriented project portfolio selection. European Journal of Operational Research, 205(3), 670-679.
Hajipour, V., Tavana, M., Santos-Arteaga, F. J., Alinezhad, A., & Di Caprio, D. (2020). An efficient controlled elitism non-dominated sorting genetic algorithm for multi-objective supplier selection under fuzziness. Journal of Computational Design and Engineering, 7(4), 469-488.
Huang, C. C., Chu, P. Y., & Chiang, Y. H. (2008). A fuzzy AHP application in government-sponsored R&D project selection. Omega, 36(6), 1038-1052.
Huang, P. C., Tong, L. I., Chang, W. W., & Yeh, W. C. (2011). A two-phase algorithm for product part change utilizing AHP and PSO. Expert Systems with Applications, 38(7), 8458-8465.
Hu, X. B., Wang, M., Ye, Q., Han, Z., & Leeson, M. S. (2014). Multi-objective new product development by complete Pareto front and ripple-spreading algorithm. Neurocomputing, 142, 4-15.
Iamratanakul, S., Patanakul, P., & Milosevic, D. (2008). Project portfolio selection: From past to present. In 2008 4th IEEE International Conference on Management of Innovation and Technology (pp. 287-292). IEEE.
Jafarzadeh, H., Akbari, P., & Abedin, B. (2018). A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency–combination of fuzzy QFD and DEA. Expert Systems with Applications, 110, 237-249.
Jain, N., & Singh, A. R. (2020). Sustainable supplier selection under must-be criteria through fuzzy inference system. Journal of Cleaner Production, 248, 119275.
Jayant, A., Gupta, P., Garg, S. K., & Khan, M. (2014). TOPSIS-AHP based approach for selection of reverse logistics service provider: a case study of mobile phone industry. Procedia Engineering, 97, 2147-2156.
Kandel, A., & Byatt, W. J. (1978). Fuzzy sets, fuzzy algebra, and fuzzy statistics. Proceedings of the IEEE, 66(12), 1619-1639.
Keskin, G. A. (2015). Using integrated fuzzy DEMATEL and fuzzy C: means algorithm for supplier evaluation and selection. International Journal of Production Research, 53(12), 3586-3602.
Khalili-Damghani, K., Sadi-Nezhad, S., Lotfi, F. H., & Tavana, M. (2013). A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection. Information Sciences, 220, 442-462.
Koyuncu, E., & Erol, R. (2015). PSO based approach for scheduling NPD projects including overlapping process. Computers & Industrial Engineering, 85, 316-327.
Li, X., Huang, Y. H., Fang, S. C., & Zhang, Y. (2020). An alternative efficient representation for the project portfolio selection problem. European Journal of Operational Research, 281(1), 100-113.
Ma, J., Harstvedt, J. D., Jaradat, R., & Smith, B. (2020). Sustainability driven multi-criteria project portfolio selection under uncertain decision-making environment. Computers & Industrial Engineering, 140, 106236.
Mahmoudi, A., Abbasi, M., & Deng, X. (2022). A novel project portfolio selection framework towards organizational resilience: robust ordinal priority approach. Expert Systems with Applications, 188, 116067.
Mohanty, R. P., Agarwal, R., Choudhury, A. K., & Tiwari, M. K. (2005). A fuzzy ANP-based approach to R&D project selection: a case study. International Journal of Production Research, 43(24), 5199-5216.
Murakami, H. (2024). Product life cycles, product innovation and firm growth. Annals of Operations Research, 337(3), 873-890.
Patanakul, P. (2020). How to achieve effectiveness in project portfolio management. IEEE Transactions on Engineering Management, 69(4), 987-999.
Paul, S. K. (2015). Supplier selection for managing supply risks in supply chain: a fuzzy approach. The International Journal of Advanced Manufacturing Technology, 79, 657-664.
Perez-Escobedo, J. L., Azzaro-Pantel, C., & Pibouleau, L. (2012). Multiobjective strategies for new product development in the pharmaceutical industry. Computers & Chemical Engineering, 37, 278-296.
Rabiei, P., Arias-Aranda, D., & Stantchev, V. (2023). Introducing a novel multi-objective optimization model for volunteer assignment in the post-disaster phase: combining fuzzy inference systems with NSGA-II and NRGA. Expert Systems with Applications, 226, 120142.
Rad, F. H., & Rowzan, S. M. (2018). Designing a hybrid system dynamic model for analyzing the impact of strategic alignment on project portfolio selection. Simulation Modelling Practice and Theory, 89, 175-194.
Ramzan, M., Jaffar, A., Iqbal, A., Anwar, S., Rauf, A., & Shahid, A. A. (2012). Project scheduling conflict identification and resolution using genetic algorithms (GA). Telecommunication Systems, 51, 167-175.
Sampath, S., Gel, E. S., Fowler, J. W., & Kempf, K. G. (2015). A decision-making framework for project portfolio planning at Intel Corporation. Interfaces, 45(5), 391-408.
Santiago, L. P., & Soares, V. M. O. (2018). Strategic alignment of an R&D portfolio by crafting the set of buckets. IEEE Transactions on Engineering Management, 67(2), 309-321.
Sequeira, M., Adlemo, A., & Hilletofth, P. (2023). A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions. Operations Management Research, 16(1), 164-191.
Shtub, A., Bard, J. F., & Globerson, S. (1994). Project Management: Engineering, Technology, and Implementation. Prentice-Hall, Inc..
Si, H., Kavadias, S., & Loch, C. (2022). Managing innovation portfolios: from project selection to portfolio design. Production and Operations Management, 31(12), 4572-4588.
Tahri, H. (2015). Mathematical optimization methods: application in project portfolio management. Procedia-Social and Behavioral Sciences, 210, 339-347.
Tofighian, A. A., & Naderi, B. (2015). Modeling and solving the project selection and scheduling. Computers & Industrial Engineering, 83, 30-38.
Toloo, M., & Mirbolouki, M. (2019). A new project selection method using data envelopment analysis. Computers & Industrial Engineering, 138, 106119.
Ulrich, K. T., & Pearson, S. A. (1993). Does product design really determine 80% of manufacturing cost? Massachusetts Institute of Technology, Sloan School of Management.
Urhahn, C., & Spieth, P. (2014). Governing the portfolio management process for product innovation—A quantitative analysis on the relationship between portfolio management governance, portfolio innovativeness, and firm performance. IEEE Transactions on Engineering Management, 61(3), 522-533.
Wang, X., Zeng, D., Dai, H., & Zhu, Y. (2020). Making the right business decision: forecasting the binary NPD strategy in Chinese automotive industry with machine learning methods. Technological Forecasting and Social Change, 155, 120032.
Weustink, I. F., Ten Brinke, E., Streppel, A. H., & Kals, H. J. J. (2000). A generic framework for cost estimation and cost control in product design. Journal of Materials Processing Technology, 103(1), 141-148.
Wheelwright, S. C. (1992). Creating project plans to focus product development. Harvard Business School Pub.
Wu, Y., Xu, C., Ke, Y., Chen, K., & Sun, X. (2018). An intuitionistic fuzzy multi-criteria framework for large-scale rooftop PV project portfolio selection: case study in Zhejiang, China. Energy, 143, 295-309.
Wu, Y., Xu, C., Ke, Y., Li, X., & Li, L. (2019). Portfolio selection of distributed energy generation projects considering uncertainty and project interaction under different enterprise strategic scenarios. Applied Energy, 236, 444-464.
Zolfaghari, S., & Mousavi, S. M. (2021). A novel mathematical programming model for multi-mode project portfolio selection and scheduling with flexible resources and due dates under interval-valued fuzzy random uncertainty. Expert Systems with Applications, 182, 115207.
簡禎富. (2015). 決策分析與管理: 紫式決策分析以全面提升決策品質. 雙葉書廊.
陳耀茂. (2019). 決策分析─方法與應用. 五南圖書.