Share:


Hybrid rough set and data envelopment analysis approach to technology prioritisation

    Ewa Chodakowska   Affiliation
    ; Joanicjusz Nazarko   Affiliation

Abstract

The complexity and speed of change in technological systems pose new challenges to technology management. Particular attention should be given to the issue of modelling the uncertainty of assessments and creating rules for determining the weights of the technology assessment criteria. The article aims to present a comprehensive hybrid technology prioritisation model based on the Data Envelopment Analysis and the concept of Rough Sets. The technology prioritisation process that uses the proposed model includes three consecutive stages: (i) the formulation of technology assessment matrix, (ii) the removal of the criteria redundancy based on indiscernibility relation defined in the Rough Set Theory, (iii) the development of rough variables and prioritisation using the DEA super-efficiency model. The combination of DEA and RS is a unique proposal to classify and rank objects based on the tabular representation of their conditional attributes under circumstances of uncertainty. Application of the developed hybrid model to the real data of the technology foresight project “NT FOR Podlaskie 2020” positively verified the assumed effects of its use. The obtained results allow a more objective and rational justification of the chosen technology, simplification of interpretation and better authentication of results from the perspective of decision-makers.


First published online 8 May 2020

Keyword : Data Envelopment Analysis (DEA), Rough Sets (RS), hybrid model, technology prioritisation, technology management, technology assessment

How to Cite
Chodakowska, E., & Nazarko, J. (2020). Hybrid rough set and data envelopment analysis approach to technology prioritisation. Technological and Economic Development of Economy, 26(4), 885-908. https://doi.org/10.3846/tede.2020.12538
Published in Issue
Jun 12, 2020
Abstract Views
228
PDF Downloads
111
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Aaltonen, M., & Sanders, T. I. (2006). Identifying systems’ new initial conditions as influence points for the future. Foresight, 8(3), 28–35. https://doi.org/10.1108/14636680610668054

Amin, G.R., & Emrouznejad, A. (2013). A new DEA model for technology selection in the presence of ordinal data, International Journal of Advanced Manufacturing Technology, 65, 1567–1572. https://doi.org/10.1007/s00170-012-4280-3

Anderson, T. R., Hollingsworth, K., & Inman, L. (2001). Assessing the rate of change in the enterprise database system market over time using DEA. In PICMET ‘01. Portland International Conference on Management of Engineering and Technology. Proceedings, Vol. 1: Book of Summaries. IEEE. https://doi.org/10.1109/PICMET.2001.951928

Atanassov, K. T. (1983, June 7–9). Intuitionistic fuzzy sets. In Proceedings of the VII ITKR’s Session, Sofia, Bulgaria (reprinted in International Journal Bioautomation (2016), 20, 1–6). http://www.biomed.bas.bg/bioautomation/2016/vol_20.s1/files/20.s1_02.pdf

Atanassov, K. T. (2017). Type-1 Fuzzy Sets and Intuitionistic. Fuzzy Sets, Algorithms, 10(3), 106. https://doi.org/10.3390/a10030106

Bai, C., & Sarkis, J. (2017). Improving green flexibility through advanced manufacturing technology investment: Modeling the decision process. International Journal of Production Economics, 188, 86–104. https://doi.org/10.1016/j.ijpe.2017.03.013

Belton, V., & Vickers, S. P. (1993). Demystifying DEA – a visual interactive approach based on multiple criteria analysis. Journal of the Operational Research Society, 44(9), 883–896. https://doi.org/10.1057/jors.1993.157

Cagnin, C., Havas, A., & Saritas, O. (2013). Future-oriented technology analysis: Its potential to address disruptive transformations. Technological Forecasting & Social Change, 80(3), 379–385. https://doi.org/10.1016/j.techfore.2012.10.001

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8

Chodakowska, E. (2019). Hybrydowy model priorytetyzacji technologii. Oficyna Wydawnicza Politechniki Białostockiej. https://doi.org/10.24427/978-83-65596-91-8

Chodakowska, E., & Nazarko, J. (2017a). Environmental DEA method for assessing productivity of European countries. Technological and Economic Development of Economy, 23(4), 589–607. https://doi.org/10.3846/20294913.2016.1272069

Chodakowska, E., & Nazarko, J. (2017b). Network DEA models for evaluating couriers and messengers. Procedia Engineering, 182, 106–111. https://doi.org/10.1016/j.proeng.2017.03.130

Ciflikli, C., & Kahya-Ozyirmidokuz, E. (2012). Enhancing product quality of a process. Industrial Management & Data Systems, 112(8), 1181–1200. https://doi.org/10.1108/02635571211264618

Cuhls, K., Blind, K., & Grupp, H. (2002). Innovations for our Future, Delphi ’98: New foresight on science and technology. Publisher Physica-Verlag. https://doi.org/10.1007/978-3-642-57472-6

Decker, M., & Ladikas, M. (Eds.). (2004). Bridges between science, society and policy. Technology assessment – methods and impacts. Springer-Verlag Heidelberg. https://doi.org/10.1007/978-3-662-06171-8

Doyle, J., & Green, R. (1993). Data envelopment analysis and multiple criteria decision making. Omega, 21(6), 713–715. https://doi.org/10.1016/0305-0483(93)90013-B

Durand, T. (2003). Twelve lessons from ‘Key Technologies 2005’: the French technology foresight exercise. Journal of Forecasting, 22(2–3), 161–177. https://doi.org/10.1002/for.856

Ejdys, J. & Halicka, K. (2018). Sustainable adaptation of new technology – the case of humanoids used for the care of older adults. Sustainability, 10(10), 3770. https://doi.org/10.3390/su10103770

Ejdys, J., Matuszak-Flejszman, A., Szymanski, M., Ustinovicius, L., Shevchenko, G., & Lulewicz-Sas, A. (2016). Crucial factors for improving the ISO14001 Environmental Management System. Journal of Business Economics and Management, 17(1), 52–73. https://doi.org/10.3846/16111699.2015.1065905

Fan, J.-L., Zhang, X., Zhang, J., & Peng, S. (2015). Efficiency evaluation of CO2 utilization technologies in China: A super-efficiency DEA analysis based on expert survey. Journal of CO2 Utilization, 11, 54–62. https://doi.org/10.1016/j.jcou.2015.01.004

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253–290. https://doi.org/10.2307/2343100

Fodor, J., & Roubens, M. (1994). Fuzzy preference modelling and multicriteria decision support. Kluwer. https://doi.org/10.1007/978-94-017-1648-2

Gordon, T. J., & Glenn, J. C. (2004, May 13–14). Integration, comparisons, and frontier of futures research methods. In EU-US Seminar: New Technology Foresight, Forecasting & Assessment Methods, Seville, Spain.

Greco, S., Matarazzo, B., & Słowiński, R. (2001). Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 129(1), 1–47. https://doi.org/10.1016/S0377-2217(00)00167-3

Halicka, K. (2016). Innovative classification of methods of the future-oriented technology analysis. Technological and Economic Development of Economy, 22(4), 574–597. https://doi.org/10.3846/20294913.2016.1197164

He, Y., Pang, Y., Zhang, Q., Jiao, Z., & Chen, Q. (2018). Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory. Renewable Energy, 122, 643–653. https://doi.org/10.1016/j.renene.2018.02.028

Hemert, van P., & Nijkamp, P. (2010). Knowledge investments, business R&D and innovativeness of countries: A qualitative meta-analytic comparison. Technological Forecasting and Social Change, 77(3), 369–384. https://doi.org/10.1016/j.techfore.2009.08.007

Jian, L., Liu, S., & Liu, Y. (2010). The selection of regional key technology based on the hybrid model of grey fixed clustering and variable precision rough set. In ISTASC’10 Proceedings of the 10th WSEAS International Conference on Systems Theory and Scientific Computation (pp. 54–59).

Ju-Long, D. (1982). Control problems of grey systems. Systems and Control Letters, 1(5), 288–294. https://doi.org/10.1016/S0167-6911(82)80025-X

Karlsen, J. E., & Karlsen, H. (2013). Classification of tools and approaches applicable in foresight studies. In M. Giaoutzi, & B. Sapio (Eds.), Recent developments in foresight methodologies: Vol. 1. Complex networks and dynamic systems. Springer. https://doi.org/10.1007/978-1-4614-5215-7_3

Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. John Wiley & Sons.

Kwon, D. S., Cho, J. H., & Sohn, S. Y. (2017). Comparison of technology efficiency for CO2 emissions reduction among European countries based on DEA with decomposed factors. Journal of Cleaner Production, 151, 109–120. https://doi.org/10.1016/j.jclepro.2017.03.065

Lai, X., Liu, J. X., & Georgiev, G. (2016). Low carbon technology integration innovation assessment index review based on rough set theory – an evidence from construction industry in China. Journal of Cleaner Production, 126, 88–96. https://doi.org/10.1016/j.jclepro.2016.03.035

Lee, C., Lee, H., Seol, H., & Park, Y. (2012). Evaluation of new service concepts using rough set theory and group analytic hierarchy process. Expert Systems with Applications, 39, 3404–3412. https://doi.org/10.1016/j.eswa.2011.09.028

Liang, X., & Dijk, van M. P. (2016). Identification of decisive factors determining the continued use of rainwater harvesting systems for agriculture irrigation in Beijing. Water, 8(1), 7. https://doi.org/10.3390/w8010007

Lim, D.-J., Jahromi, S. R., Anderson, T. R., & Tudorie, A.-A. (2015). Comparing technological advancement of hybrid electric vehicles (HEV) in different market segments. Technological Forecasting & Social Change, 97, 140–153. https://doi.org/10.1016/j.techfore.2014.05.008

Liu, B. (2004). Uncertain theory: An introduction to its axiomatic foundation. Springer.

Liu, Y., Sun, C., & Xu, S. (2013). Eco-efficiency assessment of water systems in China. Water Resource Management, 27(14), 4927–4939. https://doi.org/10.1007/s11269-013-0448-3

Łunarski, J. (2009). Zarządzenie technologiami. Ocena i doskonalenie. Oficyna Wydawnicza Politechniki Rzeszowskiej.

Luo, J.-L., & Hu, Z.-H. (2015). Risk paradigm and risk evaluation of farmers cooperatives’ technology innovation. Economic Modelling, 44, 80–85. https://doi.org/10.1016/j.econmod.2014.10.024

Magruk, A. (2011). Innovative classification of technology foresight methods. Technological and Economic Development of Economy, 17(4), 700–716. https://doi.org/10.3846/20294913.2011.649912

Magruk, A. (2017). Concept of uncertainty in relation to the foresight research. Engineering Management in Production and Services, 9(1), 46–55. https://doi.org/10.1515/emj-2017-0005

Martin, B. (1995). Foresight in science and technology. Technology Analysis and Strategic Management, 7(2), 139–168. https://doi.org/10.1080/09537329508524202

Martin, B. R. (2010), The origins of the concept of ‘foresight’ in science and technology: An insider’s perspective. Technological Forecasting and Social Change, 77(9), 1438–1447. https://doi.org/10.1016/j.techfore.2010.06.009

Miles, I. (2008). From futures to foresight. In L. Georghiou, J. C. Harper, M. Keenan, I. Miles, & R. Popper (Eds.), The handbook of technology foresight. Concepts and practice. Edward Elgar Publishing Limited.

Miles, I., & Keenan, M. (2003). Overview of methods used in foresight. The Technology Foresight for Organisers Training Course, Ankara, United Nations Industrial Development Organisation.

Molodtsov, D. A. (1999). Soft set theory – First results. Computers & Mathematics with Applications, 37(4), 19–31. https://doi.org/10.1016/S0898-1221(99)00056-5

Nazarko, J., & Magruk, A. (Eds.). (2013). Kluczowe nanotechnologie w gospodarce Podlasia. Oficyna Wydawnicza Politechniki Białostockiej.

Nazarko, Ł. (2015). Technology assessment in construction sector as a strategy towards sustainability. Procedia Engineering, 122, 290–295. https://doi.org/10.1016/j.proeng.2015.10.038

Nazarko, Ł. (2017). Future-oriented technology assessment. Procedia Engineering, 182, 504–509. https://doi.org/10.1016/j.proeng.2017.03.144

Opricovic, S., & Tzeng, G. H. (2003). Comparing DEA and MCDM Method. In Multi-Objective Programming and Goal Programming. Advances in Soft Computing 21. Springer, Heidelberg. https://doi.org/10.1007/978-3-540-36510-5_32

Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11(5), 341– 356. https://doi.org/10.1007/BF01001956

Pawlak, Z., & Skowron, A. (2007). Rough sets: Some extensions. Information Sciences, 177, 28–40. https://doi.org/10.1016/j.ins.2006.06.006

Papagapiou, A., Mingers, J., & Thanassoulis, E. (1997). Would you buy a used car with DEA? OR Insight, 10(1), 13–19. https://doi.org/10.1057/ori.1997.3

Pietrobelli, C., & Puppato, F. (2016). Technology foresight and industrial strategy. Technological Forecasting and Social Change, 110, 117–125. https://doi.org/10.1016/j.techfore.2015.10.021

Popper R. (2009). Mapping foresight revealing how Europe and other world regions navigate into the future. http://www.forschungsnetzwerk.at/downloadpub/2009_efmn_mappingForesight_EU.pdf

Popper, R. (2008). How are foresight methods selected? Foresight, 10(6), 62–89. https://doi.org/10.1108/14636680810918586

Popper, R., & Korte, W. B. (2004, May 13–14). Xtreme Euforia: combining foresight methods. In EU-US Seminar: New Technology Foresight. Forecasting & Assessment Methods, Seville, Spain. https://doi.org/10.14512/tatup.13.2.132

Porter, A. L. (1995). Technology assessment. Impact Assessment, 13(2), 135–151. https://doi.org/10.1080/07349165.1995.9726087

Porter, A. L. (2010). Technology foresight: types and methods. International Journal of Foresight and Innovation Policy, 6(1–3). https://doi.org/10.1504/IJFIP.2010.032664

Porter, A. L., Ashton, W. B., Clar, G., Coates, J. F., Cuhls, K., Cunningham, S. W., Ducatel, K., Duin, van der P., Georghiou, L., Gordon, T., Linstone, H., Marchau, V., Massari, G., Miles, I., Mogee, M., Salo, A., Scapolo, F., Smits, R., & Thissen, W. [Technology Futures Analysis Methods Working Group] (2004). Technology futures analysis: Toward integration of the field and new methods. Technological Forecasting and Social Change, 71(3), 287–303. https://doi.org/10.1016/j.techfore.2003.11.004

Predki, B., Słowiński, R., Stefanowski, J., Susmaga, R., & Wilk, S. (1998). ROSE – Software Implementation of the Rough Set Theory. In L. Polkowski & A. Skowron (Eds.), Rough Sets and Current Trends in Computing. Lecture Notes in Artificial Intelligence, 1424, 605–608. Springer-Verlag. https://doi.org/10.1007/3-540-69115-4_85

Rohrbeck, R., & Gemünden, H. G. (2011). Corporate foresight: Its three roles in enhancing the innovation capacity of a firm. Technological Forecasting & Social Change, 78(2), 231–243. https://doi.org/10.1016/j.techfore.2010.06.019

Roosth, S., & Silbey, S. (2009). Science and technology studies: From controversies to Posthumanist Social Theory. In B. S. Turner (Ed.), The New Blackwell Companion to Social Theory. Blackwell Publishing Ltd. https://doi.org/10.1002/9781444304992.ch23

Roy, B. (1990). Wielokryterialne wspomaganie decyzji. Wydawnictwa Naukowo-Techniczne.

Sambuc, R. (1975). Fonctions φ-floues. Application à l’aide au diagnostic en pathologie thyroidienne (Doctoral dissertation). Université Marseille, France.

Sánchez-Torres, J. M., & Miles, I. (2017). The role of future-oriented technology analysis in e-Government: a systematic review. European Journal of Futures Research, 5(1), 1–18. https://doi.org/10.1007/s40309-017-0131-7

Shabani, A., Saen, R. F., & Torabipour, S. M. R. (2014). 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. https://doi.org/10.1007/s10098-013-0652-0

Sharma, S., Dua, A., Singh, M., Kumar, N., & Prakash, S. (2018). Fuzzy rough set-based energy management system for self-sustainable smart city. Renewable & Sustainable Energy Reviews, 82, 3633– 3644. https://doi.org/10.1016/j.rser.2017.10.099

Shiau, T.-A., & Chuen-Yu, J.-K. (2016). Developing an indicator system for measuring the social sustainability of offshore wind power farms. Sustainability, 8(5), 470. https://doi.org/10.3390/su8050470

Shiraz, R. K., Fukuyama, H., Tavana, M., & Caprio, Di D. (2016). An integrated data envelopment analysis and free disposal hull framework for cost-efficiency measurement using rough sets. Applied Soft Computing, 46, 204–219. https://doi.org/10.1016/j.asoc.2016.04.043

Shuai, J. J., & Li, H. L. (2005). Using rough set and worst practice DEA in business failure prediction. In D. Ślęzak, J. Yao, J. F. Peters, W. Ziarko, & X. Hu (Eds.), Lecture notes in computer science: Vol. 3642. Rough sets, fuzzy sets, data mining, and granular computing (pp. 503–510). Springer, Heidelberg. https://doi.org/10.1007/11548706_53

Sokolov, A., Veselitskaya, N., Carabias, V., & Yildirim, O. (2019). Scenario-based identification of key factors for smart cities development policies. Technological Forecasting & Social Change, 148. https://doi.org/10.1016/j.techfore.2019.119729

Srivastava, S., & Misra, M. (2016). Assessing and forecasting technology dynamics in smartphones: a TFDEA approach. Technology Analysis & Strategic Management, 28(7), 783–797. https://doi.org/10.1080/09537325.2016.1143094

Stewart, T. J. (1996). Relationships between DEA and MCDM. Journal of the Operational Research Society, 47(5), 654–665. https://doi.org/10.1057/jors.1996.77

Sueyoshi, T., & Goto, M. (2014). Environmental assessment for corporate sustainability by resource utilization and technology innovation: DEA radial measurement on Japanese industrial sectors. Energy Economics, 46, 295–307. https://doi.org/10.1016/j.eneco.2014.09.021

Tohidi, G., & Valizadeh, P. (2011). A non-radial rough DEA model. International Journal of Mathematical Modelling & Computation, 1(4), 257–261. https://www.sid.ir/FileServer/JE/1033520110405

Voros, J. 2006. Introducing a classification framework for prospective methods. Foresight, 8(2), 43–56. https://doi.org/10.1108/14636680610656174

Wang, X., Jia, F., & Wang, Y. (2015). Evaluation of clean coal technologies in China: Based on rough set theory. Energy & Environment, 26(6–7), 985–995. https://doi.org/10.1260/0958-305X.26.6-7.985

Wang, C.-H., Chin, Y.-C., & Tzeng, G.-H. (2010). Mining the R&D innovation performance processes for high-tech firms based on rough set theory. Technovation, 30(7–8), 447–458. https://doi.org/10.1016/j.technovation.2009.11.001

Weber, K. M., Gudowsky, N., & Aichholzer, G. (2019). Foresight and technology assessment for the Austrian parliament – Finding new ways of debating the future of industry 4.0. Futures 109, 240251. https://doi.org/10.1016/j.futures.2018.06.018

Wu, H.-Y., & Lin, H.-Y. (2012). A hybrid approach to develop an analytical model for enhancing the service quality of e learning. Computers & Education 58(4), 1318-1338. https://doi.org/10.1016/j.compedu.2011.12.025

Xu J., Li, B., & Wu, D. (2009). Rough data envelopment analysis and its application to supply chain performance evaluation. International Journal of Production Economics 122(2), 628-638. https://doi.org/10.1016/j.ijpe.2009.06.026

Yu, P., & Lee, J. H. (2013). 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. https://doi.org/10.1016/j.eswa.2012.07.043

Zadeh, L. A. (1965). Fuzzy sets, Information and Control 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X

Zavadskas, E. K., Antucheviciene, J. & Kar, S. (2019). Multi-Objective and Multi-Attribute Optimization for Sustainable Development Decision Aiding. Sustainability 11, 3069. https://doi.org/10.3390/su11113069

Zavadskas, E. K., & Turskis, Z. (2011). Multiple Criteria Decision Making (MCDM) methods in economics: An overview. Technological and Economic Development of Economy 17(2), 397-427. https://doi.org/10.3846/20294913.2011.593291

Zeng, X. T., Huang, G. H., Yang, X. L., Wang, X., Fu, H., Li, Y.P., & Li, Z. (2016). A developed fuzzystochastic optimization for coordinating human activity and eco-environmental protection in a regional wetland ecosystem under uncertainties. Ecological Engineering, 97, 207-230. https://doi.org/10.1016/j.ecoleng.2016.09.002