Open Access
Issue |
MATEC Web Conf.
Volume 312, 2020
9th International Conference on Engineering, Project, and Production Management (EPPM2018)
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Article Number | 01006 | |
Number of page(s) | 10 | |
Section | Theories and Applications of Engineering Management | |
DOI | https://doi.org/10.1051/matecconf/202031201006 | |
Published online | 03 April 2020 |
- A. Kononiuk, A. Gudanowska, (eds), Kierunki rozwoju nanotechnologii w województwie podlaskim. Mapy. Marszruty. Trendy, Oficyna Wydawnicza Politechniki Białostockiej, Białystok (2013) [Google Scholar]
- J. Nazarko, Regionalny foresight gospodarczy. Scenariusze rozwoju innowacyjności mazowieckich przedsiębiorstw, ZPWiM, Warszawa (2013) [Google Scholar]
- L. Nazarko, Future-Oriented Technology Assessment, Procedia Engineering, 182, 504-509 (2017) [CrossRef] [Google Scholar]
- A.L. Porter, Technology Assessment, Impact Assessment, 13(2), 135-151 (1995) [CrossRef] [Google Scholar]
- M. Aaltonen, Evaluation and organization of Futures Research Methodology –V 3.0 [online] https://www.millennium-project.org/millennium/FRM-eval.pdf [11.07.2018] (2009) [Google Scholar]
- A. Magruk, Innovative classification of technology foresight methods, Technological and Economic Development of Economy, 17(4), 700-715 (2011) [CrossRef] [Google Scholar]
- K. Halicka, Innovative classification of methods of the Future-oriented Technology Analysis, Technological and Economic Development of Economy, 22(4), 574-597, (2016) [CrossRef] [Google Scholar]
- R. Popper, How are foresight methods selected?, Foresight, 10(6), 62-89 (2008) [CrossRef] [Google Scholar]
- C. Cagnin, A. Havas, O. Saritas, Future-oriented technology analysis: Its potential to address disruptive transformations, Technological Forecasting & Social Change, 80(3), 379-385 (2013) [CrossRef] [Google Scholar]
- A. Kononiuk, A. Sacio-Szymańska, J. Gáspár, How do companies envisage the future? Functional foresight approaches, Engineering Management in Production and Services, 9(4), 21-33 (2017) [CrossRef] [Google Scholar]
- N. Li, K. Chen, M. Kou, Technology foresight in China: Academic studies, governmental practices and policy applications, Technological Forecasting and Social Change, 119, 246-255 (2017) [CrossRef] [Google Scholar]
- L. Proskuryakova, Energy technology foresight in emerging economies, Technological Forecasting and Social Change, 119, 205-210 (2017) [CrossRef] [Google Scholar]
- B. Förster, Technology foresight for sustainable production in the German automotive supplier industry, Technological Forecasting and Social Change, 92, 237-248 (2015) [CrossRef] [Google Scholar]
- M. Choi, H.-L. Choi, H. Yang, Procedural characteristics of the 4th Korean technology foresight, Foresight, 16(3), 198-209 (2014) [CrossRef] [Google Scholar]
- D.S. Kwon, J.H. Cho, S.Y. Sohn, Comparison of technology efficiency for CO2 emissions reduction among European countries based on DEA with decomposed factors, Journal of Cleaner Production, 151, 109-120 (2017) [CrossRef] [Google Scholar]
- E. Chodakowska, J. Nazarko, Environmental DEA Method for Assessing Productivity of European Countries, Technological and Economic Development of Economy, 23(4), 589-607 (2017) [CrossRef] [Google Scholar]
- T. Sueyoshi, M. Goto, Environmental assessment for corporate sustainability by resource utilization and technology innovation: DEA radial measurement on Japanese industrial sectors, Energy Economics, 46, 295-307 (2014) [CrossRef] [Google Scholar]
- J.-L. Fan, X. Zhang, J. Zhang, S. Peng, Efficiency evaluation of CO2 utilization technologies in China: A super-efficiency DEA analysis based on expert survey, Journal of CO2 Utilization, 11, 54-62 (2015) [Google Scholar]
- A. Shabani, R.F. Saen, A new data envelopment analysis (DEA) model to select eco-efficient technologies in the presence of undesirable outputs, Clean Technologies and Environmental Policy, 16(3), 513-525 (2014) [CrossRef] [Google Scholar]
- Y. Liu., C. Sun, S. Xu, Eco-Efficiency Assessment of Water Systems in China, Water Resource Management, 27(14), 4927-4939 (2013) [CrossRef] [Google Scholar]
- S.K. Lee, G. Mogi, K.S. Hui, A fuzzy analytic hierarchy process (AHP)/data envelopment analysis (DEA) hybrid model for efficiently allocating energy R&D resources: In the case of energy technologies against high oil prices, Renewable and Sustainable Energy Reviews, 21, 347-355 (2013) [CrossRef] [Google Scholar]
- H. Lee, C. Lee, H. Seol, Y. Park, On the R&D priority setting in technology foresight: a DEA and ANP approach, International Journal of Innovation and Technology Management, 5(2), 201-219 (2008) [CrossRef] [Google Scholar]
- P. Yu, J.H. Lee, A hybrid approach using two-level SOM and combined AHP rating and AHP/DEA-AR method for selecting optimal promising emerging technology, Expert System with Applications, 40, 300-314 (2013) [CrossRef] [Google Scholar]
- G.R. Amin, A. Emrouznejad, A new DEA model for technology selection in the presence of ordinal data, International Journal of Advanced Manufacturing Technology, 65, 1567-1572 (2013) [CrossRef] [Google Scholar]
- E.E. Karsak, S.S. Ahiska, Practical common weight multicriteria decision-making approach with an improved discriminating power for technology selection, International Journal of Production Research, 43(8), 1537-1554 (2005) [CrossRef] [Google Scholar]
- A. Alinezhad, A. Makui, R. Kiani Mavi, M. Zohrehbandian, An MCDM-DEA approach for technology selection, Journal of Industrial Engineering International, 7(12), 32-38 (2011) [Google Scholar]
- M. Khouja, The use of data envelopment analysis for technology selection, Computers & Industrial Engineering, 28(1), 123-132 (1995) [CrossRef] [Google Scholar]
- R.F. Saen, Technology selection in the presence of imprecise data. weight restrictions. and nondiscretionary factors, The International Journal of Advanced Manufacturing Technology, 41(7-8), 827-838 (2009) [CrossRef] [Google Scholar]
- K. Klincewicz, A. Manikowski, Ocena. rankingowanie i selekcja technologii, Wydawnictwo Naukowe Wydziału Zarządzania Uniwersytetu Warszawskiego, Warszawa (2013) [CrossRef] [Google Scholar]
- K. Klusacek, Selection of research priorities – method of critical technologies, Technology Centre of the CAS, [online] (2003) https://www.tc.cz/files/istec_publications/unido-course-critical-technologies-1029-1.pdf [13.07.2018] [Google Scholar]
- J. Nazarko, A. Magruk (eds), Kluczowe Nanotechnologie w gospodarce Podlasia, Oficyna Wydawnicza Politechniki Białostockiej, Białystok (2013) [Google Scholar]
- N. Adler, B. Golany, Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe, European Journal of Operational Research, 132(2), 260-273 (2001) [CrossRef] [Google Scholar]
- N. Adler, B. Golany, Including Principal Component Weights to improve discrimination in Data Envelopment Analysis, Journal of the Operational Research Society, 53(9), 985-991 (2002) [CrossRef] [Google Scholar]
- K. Revett, Diagnostic Feature Analysis of a Dobutamine Stress Echocardiography Dataset Using Rough Sets, In: J.F. Peters, A. Skowron, H. Rybiński, eds, Transactions on Rough Sets IX, Springer, 318-327 (2008) [CrossRef] [Google Scholar]
- H.-H. Yang, Ch.-L. Wu, Rough sets to help medical diagnosis – Evidence from a Taiwan’s clinic, Expert Systems with Applications, 36, 9293-9298 (2009) [CrossRef] [Google Scholar]
- A.I. Dimitras, R. Slowinski, R. Susmaga, C. Zopounidis, Business failure prediction using rough sets, European Journal of Operational Research, 114, 263-280 (1999) [CrossRef] [Google Scholar]
- R.W. Swiniarski, A. Skowron, Rough set methods in feature selection and recognition, Pattern Recognition Letters, 24(6), 833-849 (2003) [CrossRef] [Google Scholar]
- E. Chodakowska, Rough and Fuzzy DEA in the Process of Prospective Technology Analysis, In: A. Emrouznejad, and E. Thanassoulis (eds), Data Envelopment Analysis and Performance Measurement: Recent Developments: Proceedings of the DEA40: International Conference of Data Envelopment Analysis, April 2018, Aston Business School, Birmingham, UK (2018) [Google Scholar]
- Z. Pawlak, Rough sets, International Journal of Information and Computer Science, 11, 344-356 (1982) [Google Scholar]
- Z. Pawlak, A. Skowron, Rudiments of rough sets, Information Sciences, 177, 3-27 (2007) [CrossRef] [MathSciNet] [Google Scholar]
- A. Charnes, W.W. Cooper, E. Rhodes, Measuring the efficiency of decision-making units, European Journal of Operational Research, 2(6), 429-444 (1978) [CrossRef] [Google Scholar]
- P. Andersen, N.C. Petersen, A procedure for ranking efficient units in data envelopment analysis, Management Science, 39(10), 1261-1264 (1993) [Google Scholar]
- A. Azadeh, S.M. Alem, A flexible deterministic. stochastic and fuzzy Data Envelopment Analysis approach for supply chain risk and vendor selection problem: Simulation analysis, Expert Systems with Applications, 37(12), 7438-7448 (2010) [CrossRef] [Google Scholar]
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