نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیات علمی دانشگاه شاهد

2 دانشجو

چکیده

امروزه اساس کار اغلب هلدینگ ها و شرکت‌های پیمانکاری، مبتنی بر پروژه‌هایشان است لذا بخش اعظم درآمدهای آنها در گرو انتخاب و اجرای صحیح پروژه ها می باشد. اصولا ارزیابی و انتخاب پروژه‌ها جهت تشکیل سبد بهینه پروژه سازمان، یک مسئله تصمیم‌گیری چندمعیاره است که با توجه به ماهیت آن و قضاوت‌های ذهنی تصمیم‌گیرندگان، دارای عدم قطعیت و ابهام می‌باشد. از این‌رو مدیران نیازمندی سازوکاری نظام‌مند هستند تا درحضور معیارهای متعدد، تصمیم‌گیری صحیحی را اتخاذ نمایند. در این مقاله، چارچوبی یکپارچه مبتنی بر رویکرد بسط عملکرد کیفیت فازی و روش فرآیند تحلیل شبکه‌ای فازی، جهت برگرداندن نیازمندی‌های کارفرمایان به مشخصه‌های فنی موردنیاز و همچنین ارزیابی و انتخاب پروژه‌های کاندید برای ورود به سبد پروژه‌های سازمان پیشنهاد می‌شود. برای نمایش قابلیت‌های چارچوب پیشنهادی، ارزیابی و انتخاب مناسب‌ترین پروژه در حوزه ساختمان و ابنیه در یک شرکت پروژه محور دردستور کار قرار گرفت. نتایج حاصله بیانگر آن است که در میان نیازمندی‌های کارفرمایان این حوزه، "مدیریت نظام‌مند ریسک پروژه" از بیشترین وزن (اهمیت) و در بین مشخصه‌های فنی موردنیاز پروژه (معیارهای ارزیابی پروژه) "توانمندی فناوری" با امتیاز 0.088 از اهمیت بالاتری نسبت به سایر معیارها برخوردارند. بعلاوه پروژه پیشنهادی پنجم در مجموع کلیه معیارها بیشترین امتیاز را به خود اختصاص داده و به‌عنوان پروژه کاندید قرار گرفته است.

کلیدواژه‌ها

عنوان مقاله [English]

Provides an Integrated Framework for Creating a Corporate Project Basket with an Integrated Approach for QFD and ANP under Uncertainty

نویسنده [English]

  • Reza Abbasi 1

1 Faculty Member of Shahed University

2

چکیده [English]

Nowadays, most of the holdings and contracting companies are based on their projects, so most of their revenues depend on the selection and proper implementation of the projects. Basically, evaluating and selecting projects to form an optimal portfolio of an organization's project is a multi-criteria decision-making problem that has uncertainty and ambiguity, depending on its nature and the judgments of decision makers. Hence, managers need systematic mechanisms to make the right decisions in the presence of multiple criteria. In this paper, an integrated framework based on Fuzzy function performance expansion and the Fuzzy Network Analysis process approach is proposed to revert the requirements of employers to the required technical characteristics as well as to evaluate and select candidate projects for entry into the project portfolio of the organization. To demonstrate the capabilities of the proposed framework, the evaluation and selection of the most suitable project in the field of building and construction was carried out in a project-based project company. The results indicate that among the requirements of the employers in this area, "systematic project risk management" of the highest importance (weight) and among the technical characteristics of the project (project evaluation criteria), "technology capability" with a score of 0.088 is more important than other Metrics. In addition, the proposed Fifth Project, in aggregate, has all the highest scores and serves as a candidate project.

کلیدواژه‌ها [English]

  • Project portfolio management
  • Fuzzy function performance expansion
  • Fuzzy network analysis process
  • Language variables
  • Uncertainty
Altuntas, S., & Dereli, T. (2015). A novel approach based on DEMATEL method and patent citation analysis for prioritizing a portfolio of investment projects. Expert Systems with Applications, 42(3), 1003-1012.
Arasteh, A., Aliahmadi, A., & Omran, M. M. (2014). A Multi-stage Multi Criteria Model for Portfolio Management. Arabian Journal for Science and Engineering, 39(5), 4269-4283.
Bhattacharya, A., Geraghty, J., & Young, P. (2010). Supplier selection paradigm: An integrated hierarchical QFD methodology under multiple-criteria environment. Applied Soft Computing, 10(4), 1013-1027.
Bojadziev, G., & Bojadziev, M. (2007). Fuzzy logic for business, finance, and management. World Scientific Publishing Co., Inc.
Barros de Oliveira, M., Costa, H., Figueiredo, F. V., & Rocha, A. R. C. (2014). Scaling up a Project Portfolio Selection Technique by using Multiobjective Genetic Optimization. iSys-Revista Brasileira de Sistemas de Informação, 7(4), 60-74.
Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649-655.
Cheng, C. H., & Lin, Y. (2002). Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European Journal of Operational Research, 142(1), 174-186.
Dursun, M., & Karsak, E. E. (2013). A QFD-based fuzzy MCDM approach for supplier selection. Applied Mathematical Modelling, 37(8), 5864-5875.
Ewing, P. L. Jr., Tarantino, W., & Parnell, G. S. (2006). Use of decision analysis in the army base realignment and closure (BRAC) 2005 military value analysis. Decision Analysis, 3, 33–49.
Fernandez, E., Gomez, C., Rivera, G., & Cruz-Reyes, L. (2015). Hybrid meta-heuristic approach for handling many objectives and decisions on partial support in project portfolio optimization. Information Sciences, 315, 102-122.
Ferreira, L., Borenstein, D., Santi, E. (2016). Hybrid fuzzy MADM ranking procedure for better alternative discrimination. Engineering applications of artificial intelligence, 50,71-82.
Ghafoori, S., Taghizadeh.Y, MR., (2017), Proposing a multi-objective mathematical model for RCPSP and solving It with firefly and simulated annealing algorithms, Modern Researches in Decision Making, 1(4), 117 142.
Guo, S., Yu, L., Li, X., Kar, S. (2016). Fuzzy multi-period portfolio selection with different investment horizons. European Journal of Operational Research, 25, 1026-1035.
Hassanzadeh, F., Nemati, H., & Sun, M. (2014). Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection. European Journal of Operational Research, 238(1), 41-53.
Henriksen, A. D. P., & Palocsay, S. W. (2008). An Excel-based decision support system for scoring and ranking proposed R&D projects. International Journal of Information Technology and Decision Making, 7(3), 529–546.
Ho, W. R. J., Tsai, C. L., Tzeng, G. H., & Fang, S. K. (2011). Combined DEMATEL technique with a novel MCDM model for exploring portfolio selection based on CAPM. Expert Systems with Applications, 38(1), 16-25.
Jafarzadeh, M., Tareghian, H. R., Rahbarnia, F., & Ghanbari, R. (2015). Optimal selection of project portfolios using reinvestment strategy within a flexible time horizon. European Journal of Operational Research, 243(2), 658-664.
Juan, Yi-Kai, Yeng-Horng Perng, Daniel Castro-Lacouture, and Kuo-Sheng Lu. Housing refurbishment contractor selection based on a hybrid fuzzy-QFD approach. Automation in Construction 18, no. 2 (2009): 139-144.
Kadri, R.L., Boctor, F.F, (2017) .An efficient genetic algorithm to solve the resource- constrained
project scheduling problem with transfer times: The single mode case, European Journal of Operational Research, 265(2), 454-462.
Karsak, E. E. (2004). Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment. International Journal of Production Research, 42(18), 3957-3974.
Komar, Manish, M. L. MittalGunjan SoniDheeraj Joshi. (2019). A Tabu Search Algorithm for Simultaneous Selection and Scheduling of Projects, Harmony Search and Nature Inspired Optimization Algorithms. pp 1111-1121.
Korotkov, V. & Wu, D. (2019).  Evaluating the quality of solutions in project portfolio selection, Omega, in press.
Kumar, N., Vidyarthi, D. P. (2015). A model for resource-constrained project scheduling using
adaptive PSO, Soft Computing, 19, 1-16.
Lee, A. H., & Lin, C. Y. (2011). An integrated fuzzy QFD framework for new product development. Flexible services and manufacturing journal, 23(1), 26-47.
Liesio, J. (2006). Robust portfolio optimization in multi-criteria project selection. Licentiate’s Thesis, Helsinki University of Technology.
Liu, H. T., & Wang, C. H. (2010). An advanced quality function deployment model using fuzzy analytic network process. Applied Mathematical Modelling, 34(11), 3333-3351.
Liu, S. S., & Wang, C. J. (2011). Optimizing project selection and scheduling problems with time-dependent resource constraints. Automation in Construction, 20(8), 1110-1119.
Liu, S. T. (2011). A fuzzy modeling for fuzzy portfolio optimization. Expert Systems with Applications, 38(11), 13803-13809.
Mavrotas, G., Diakoulaki, D., & Caloghirou, Y. (2006). Project prioritization under policy restrictions: a com- bination of MCDA with 0–1 programming. European Journal of Operational Research, 171, 296–308.
Medaglia, A. L., Graves, S. B., & Ringuest, L. J. (2007). A multiobjective evolutionary approach for linearly constrained project selection under uncertainty. European Journal of Operational Research, 179, 869– 894.
Nassif, L. N., Santiago Filho, J. C., & Nogueira, J. M. (2013). Project Portfolio Selection in Public Administration Using Fuzzy Logic. Procedia-Social and Behavioral Sciences, 74, 41-50.
Nowak, M. (2013). Project Portfolio Selection Using Interactive Approach. Procedia Engineering, 57, 814-822.
Pendharkar, P. C. (2014). A decision-making framework for justifying a portfolio of IT projects. International Journal of Project Management, 32(4), 625-639.
PMBoK, A. (2013). A Guide to the project Management body of knowledge (Fifth Edition). Project Management Institute, Pennsylvania USA.
Rajesh, G., and P. Malliga. Supplier selection based on AHP QFD Methodology. Procedia Engineering 64 (2013): 1283-1292.
Rafiee, M., & Kianfar, F. (2011). A scenario tree approach to multi-period project selection problem using real-option valuation method. The International Journal of Advanced Manufacturing Technology, 56(1-4), 411-420.
Ribas, J. R., & Silva Rocha, M. (2015). A decision support system for prioritizing investments in an energy efficiency program in favelas in the city of Rio de Janeiro. Journal of Multi‐Criteria Decision Analysis, 22(1-2), 89-99.
Shariatmadari Mohammad,  Nasim Nahavandi, Seyed Hessameddin Zegordi, Mohammad   Hossein Sobhiyah  (2017) .Integrated resource management for simultaneous project selection and scheduling. Computers & Industrial Engineering, Volume 109, July 2017, Pages 39-47.
Taylan, O., Bafail, A. O., Abdulaal, R. M., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-116.
Yu, L., Wang, S., Wen, F., & Lai, K. K. (2012). Genetic algorithm-based multi-criteria project portfolio selection. Annals of Operations Research, 197(1), 71-86.
Zaim, S., Sevkli, M., Camgöz-Akdağ, H., Demirel, O. F., Yayla, A. Y., & Delen, D. (2014). Use of ANP weighted crisp and fuzzy QFD for product development. Expert Systems with Applications, 41(9), 4464-4474.
 Zimmermann, H. J. (2001). Fuzzy set theory—and its applications. Springer Science & Business Media.