ارزیابی عملکرد زنجیره ارزش پروژه های تحقیق و توسعه برای سیستم ها و محصولات پیچیده: رویکرد تحلیل پوششی داده های سه مرحله ای فازی
الموضوعات :پژمان پیکانی 1 , جعفر قیدر خلجانی 2
1 - دانشکده مهندسی صنایع، دانشگاه علم و صنعت، تهران، ایران
2 - مجتمع دانشگاهی مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران
الکلمات المفتاحية: Network Data Envelopment Analysis (NDEA), Research and Development (R&D) Project, Value-Chain, Complex Products and Systems (CoPS), Uncertainty,
ملخص المقالة :
هدف از پژوهش پیش رو، ارائه یک سیستم ارزیابی عملکرد با قابلیت در نظر گرفتن ساختار شبکه ای زنجیره ارزش پروژه های تحقیق و توسعه برای سیستم ها و محصولات پیچیده تحت عدم قطعیت داده ها می باشد. از این رو به منظور دستیابی به این هدف، از رویکرد تحلیل پوششی داده های شبکه ای و برنامه ریزی امکانی برای ارائه یک رویکرد تحلیل پوششی داده های شبکه ای فازی بهره گرفته شده است. لازم به ذکر است که ساختار حاکم بر زنجیره ارزش در قالب سه مرحله شامل تحقیق و توسعه، ساخت و تست و در نهایت عملیات در نظر گرفته شده است. در نهایت رویکرد پیشنهادی پژوهش با استفاده از داده های مربوط به ده پروژه تحقیق و توسعه برای سیستم ها و محصولات پیچیده در ایران پیاده سازی و اجرا گردید که نتایج حاکی از توانمندی و کاربردی بودن رویکرد پیشنهادی تحلیل پوششی داده های سه مرحله ای فازی می باشند.کلمات کلیدی: پروژه تحقیق و توسعه، سیستم ها و محصولات پیچیده، تحلیل پوششی داده های شبکه ای، زنجیره ارزش، عدم قطعیت.
[1] Farrell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.
[2] Charnes, A., Cooper, W.W. and Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
[3] Banker, R.D., Charnes, A. and Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
[4] Peykani. P, Mohammadi. E, & Seyed Esmaeili. F.S. (2018). Measuring performance, estimating most productive scale size, and benchmarking of hospitals using DEA approach: A case study in Iran. International Journal of Hospital Research, 7(2), 21-41.
[5] Kao, C. (2014). Network data envelopment analysis: A review. European Journal of Operational Research, 239(1), 1-16.
[6] Peykani, P., & Mohammadi, E. (2020). Window network data envelopment analysis: an application to investment companies. International Journal of Industrial Mathematics, 12(1), 89-99.
[7] Peykani, P., & Mohammadi, E. (2018). Interval network data envelopment analysis model for classification of investment companies in the presence of uncertain data, Journal of Industrial and Systems Engineering, 11(Special issue: 14th International Industrial Engineering Conference), 63-72.
[8] Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European journal of operational research, 192(3), 949-962.
[9] Peykani, P., Mohammadi, E., & Seyed Esmaeili, F.S. (2018). The classification of investment companies using the interval network data envelopment analysis model. 14thInternational Conference on Industrial Engineering, Iran.
[10] Lotfi, F. H., Navabakhs, M., Tehranian, A., Rostamy-Malkhalifeh, M., & Shahverdi, R. (2007). Ranking bank branches with interval data the application of DEA. International Mathematical Forum, 2(9), 429-440.
[11] Peykani, P., & Mohammadi, E. (2018). Portfolio selection problem under uncertainty: a robust optimization approach. 3th International Conference on Intelligent Decision Science, Iran.
[12] Peykani, P., Mohammadi, E., Rostamy-Malkhalifeh, M., & Hosseinzadeh Lotfi, F. (2019). Fuzzy data envelopment analysis approach for ranking of stocks with an application to Tehran stock exchange. Advances in Mathematical Finance and Applications, 4(1), 31-43.
[13] Peykani, P., & Mohammadi, E. (2018). Robust data envelopment analysis with hybrid uncertainty approaches and its applications in stock performance measurement. 14th International Conference on Industrial Engineering, Iran.
[14] Liu, J. S., & Lu, W. M. (2010). DEA and ranking with the network-based approach: a case of R&D performance. Omega, 38(6), 453-464.
[15] Li, Y., Chen, Y., Liang, L., & Xie, J. (2012). DEA models for extended two-stage network structures. Omega, 40(5), 611-618.
[16] Chiu, Y. H., Huang, C. W., & Chen, Y. C. (2012). The R&D value-chain efficiency measurement for high-tech industries in China. Asia Pacific Journal of Management, 29(4), 989-1006.
[17] Wang, C. H., Lu, Y. H., Huang, C. W., & Lee, J. Y. (2013). R&D, productivity, and market value: An empirical study from high-technology firms. Omega, 41(1), 143-155.
[18] Chun, D., Chung, Y., & Bang, S. (2015). Impact of firm size and industry type on R&D efficiency throughout innovation and commercialisation stages: evidence from Korean manufacturing firms. Technology Analysis & Strategic Management, 27(8), 895-909.
[19] Lu, W. M., Kweh, Q. L., Nourani, M., & Huang, F. W. (2016). Evaluating the efficiency of dual-use technology development programs from the R&D and socio-economic perspectives. Omega, 62, 82-92.
[20] Zuo, K., & Guan, J. (2017). Measuring the R&D efficiency of regions by a parallel DEA game model. Scientometrics, 112(1), 175-194.
[21] Xiong, X., Yang, G. L., & Guan, Z. C. (2018). Assessing R&D efficiency using a two-stage dynamic DEA model: a case study of research institutes in the Chinese academy of sciences. Journal of Informetrics, 12(3), 784-805.
[22] Chen, K., Kou, M., & Fu, X. (2018). Evaluation of multi-period regional R&D efficiency: an application of dynamic DEA to China's regional R&D systems. Omega, 74, 103-114.
[23] Liu, H. H., Yang, G. L., Liu, X. X., & Song, Y. Y. (2019). R&D performance assessment of industrial enterprises in China: a two-stage DEA approach. Socio-Economic Planning Sciences, 100753.
[24] Peykani, P., & Mohammadi, E. (2018). Fuzzy network data envelopment analysis: a possibility approach. 3th International Conference on Intelligent Decision Science, Iran.
[25] Chen, Y., & Zhu, J. (2004). Measuring information technology's indirect impact on firm performance. Information Technology and Management, 5(1-2), 9-22.
[26] Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two‐stage processes: game approach and efficiency decomposition. Naval Research Logistics (NRL), 55(7), 643-653.
[27] Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418-429.
[28] Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research, 196(3), 1170-1176.
[29] Peykani, P., Mohammadi, E., & Seyed Esmaeili, F.S. (2019). Stock evaluation under mixed uncertainties using robust DEA model. Journal of Quality Engineering and Production Optimization, 4(1), 73-84.
[30] Peykani, P., Mohammadi, E., Jabbarzadeh, A., & Jandaghian, A. (2016). Utilizing robust data envelopment analysis model for measuring efficiency of stock, a case study: Tehran stock exchange. Journal of New Research in Mathematics, 1(4), 15-24.
[31] Peykani, P., Mohammadi, E., Sadjadi, S. J., & Rostamy-Malkhalifeh, M. (2018). A robust variant of radial measure for performance assessment of stock. 3th International Conference on Intelligent Decision Science, Iran.
[32] Peykani. P, Seyed Esmaeili. F.S, Hosseinzadeh Lotfi. F, & Rostamy-Malkhalifeh. M. (2019). Estimating most productive scale size in DEA under uncertainty. 11th National Conference on Data Envelopment Analysis, Iran.
[33] Emrouznejad, A., & Tavana, M. (2014). Performance measurement with fuzzy data envelopment analysis. Springer.
[34] Peykani. P, Seyed Esmaeili. F.S, Rostamy-Malkhalifeh. M, & Hosseinzadeh Lotfi. F. (2018). Measuring productivity changes of hospitals in Tehran: the fuzzy Malmquist productivity index. International Journal of Hospital Research, 7(3), 1-17.
[35] Peykani. P, Seyed Esmaeili. F.S, Rostamy-Malkhalifeh. M, Hosseinzadeh Lotfi. F, & Tehrani. R. (2019). Fuzzy range directional measure: the pessimistic approach. 11th National Conference on Data Envelopment Analysis, Iran.
[36] Huang, Z., & Li, S. X. (2001). Stochastic DEA models with different types of input-output disturbances. Journal of Productivity Analysis, 15(2), 95-113.
[37] Cooper, W. W., Deng, H., Huang, Z., & Li, S. X. (2004). Chance constrained programming approaches to congestion in stochastic data envelopment analysis. European Journal of Operational Research, 155(2), 487-501.
[38] Cooper, W. W., Deng, H., Huang, Z., & Li, S. X. (2002). Chance constrained programming approaches to technical efficiencies and inefficiencies in stochastic data envelopment analysis. Journal of the Operational Research Society, 53(12), 1347-1356.
[39] Zadeh, L. A. (1978). Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Sets and Systems, 1(1), 3-28.
[40] Peykani, P., Mohammadi, E., Pishvaee, M.S., Rostamy-Malkhalifeh, M., & Jabbarzadeh, A. (2018). A novel fuzzy data envelopment analysis based on robust possibilistic programming: possibility, necessity and credibility-based approaches. RAIRO-Operations Research, 52(4), 1445-1463.
[41] Peykani, P., Mohammadi, E., Emrouznejad, A., Pishvaee, M.S., & Rostamy-Malkhalifeh, M. (2019). Fuzzy data envelopment analysis: an adjustable approach. Expert Systems with Applications, 136, 439-452.
[42] Olesen, O. B., & Petersen, N. C. (2016). Stochastic data envelopment analysis - a review. European Journal of Operational Research, 251(1), 2-21.
[43] Ghassemi, A., Hu, M., & Zhou, Z. (2017). Robust planning decision model for an integrated water system. Journal of Water Resources Planning and Management, 143(5), 05017002.
[44] Ghassemi, A. (2019). System of Systems Approach to Develop an Energy-Water Nexus Model Under Uncertainty, Doctoral Dissertation, University of Illinois at Chicago.
[45] Peykani, P., Mohammadi, E., Farzipoor Saen, R., Sadjadi, S. J., & Rostamy-Malkhalifeh, M. (2020). Data envelopment analysis and robust optimization: a review. Expert Systems, e12534.
[46] Shahhosseini, M., Hu, G., & Pham, H. (2019). Optimizing ensemble weights and hyperparameters of machine learning models for regression problems. ARXIV:1908.05287.
[47] Shahhosseini, M., Martinez-Feria, R. A., Hu, G., & Archontoulis, S. V. (2019). Maize yield and nitrate loss prediction with machine learning algorithms. Environmental Research Letters, 14(12), 124026.
[48] Shahhosseini, M., Hu, G., & Pham, H. (2019). Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction. INFORMS International Conference on Service Science, 87-97. Springer, Cham.
[49] Shahhosseini, M., Hu, G., & Archontoulis, S. V. (2020). Forecasting corn yield with machine learning ensembles. ARXIV:2001.09055.
