An Intuitionistic Fuzzy DEA Cross-Efficiency Methodology with an Application to Production Group Decision-Making Problems | ||
| Journal of Quality Engineering and Production Optimization | ||
| مقاله 4، دوره 5، شماره 2، اسفند 2020، صفحه 69-86 اصل مقاله (580.86 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22070/jqepo.2020.5180.1132 | ||
| نویسندگان | ||
| Hamid Ghaderi1؛ Hossein Gitinavard* 2؛ Mansour Mehralizadeh3 | ||
| 1School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran | ||
| 2Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| 3Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran | ||
| چکیده | ||
| In decision-making situations, the opinions expressed by decision-makers (DMs) are often vague. Using linguistic variables expressed in intuitionistic fuzzy numbers is a more realistic approach to describing DMs’ judgments. The paper aims to develop a Group Decision Making (GDM) methodology based on the data envelopment analysis (DEA) method with intuitionistic fuzzy information. This method is utilized once a set of Decision-Making Units (DMUs) need to be ranked based on their efficiencies over a set of input and output measures considering DMs’ weights in an intuitionistic fuzzy environment. In the proposed method, concerning the input and output measures, each DM utilizes membership and non-membership degrees to determine the degrees of satisfiability and non-satisfiability of each DMU, respectively. Besides, a new technique is presented to determine the DMs’ weights. Different values of a DMU's efficiency obtained by individual DMs are converted into an aggregated efficiency based on the DMs’ weights. Finally, the extended DEA method is used to rank the DMUs based on their efficiencies. A case study on a production company is done for illustration and verification of the proposed approach. | ||
| کلیدواژهها | ||
| Data envelopment analysis؛ Intuitionistic fuzzy number؛ Production group decision problems | ||
| مراجع | ||
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ADLER, N., FRIEDMAN, L. & SINUANY-STERN, Z. 2002. Review of ranking methods in the data envelopment analysis context. European journal of operational research, 140, 249-265. AMIRI, M., ZANDIEH, M., VAHDANI, B., SOLTANI, R. & ROSHANAEI, V. 2010. An integrated eigenvector–DEA–TOPSIS methodology for portfolio risk evaluation in the FOREX spot market. Expert Systems with Applications, 37, 509-516. ANDERSEN, P. & PETERSEN, N. C. 1993. A procedure for ranking efficient units in data envelopment analysis. Management science, 39, 1261-1264. ARYA, A. & YADAV, S. P. 2019. Development of intuitionistic fuzzy data envelopment analysis models and intuitionistic fuzzy input–output targets. Soft Computing, 23, 8975-8993. ATANASSOV, K. T. 1986. Intuitionistic fuzzy sets. Fuzzy sets and Systems, 20, 87-96. ATANASSOV, K. T. 1989. More on intuitionistic fuzzy sets. Fuzzy sets and systems, 33, 37-45. ATANASSOV, K. T. 1999. Intuitionistic fuzzy sets, Springer. BANKER, R. D., CHARNES, A. & COOPER, W. W. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30, 1078-1092. BORUJENI, M. P. & GITINAVARD, H. 2017. Evaluating the sustainable mining contractor selection problems: An imprecise last aggregation preference selection index method. Journal of Sustainable Mining, 16, 207-218. BUSTINCE, H. & BURILLO, P. 1996. Vague sets are intuitionistic fuzzy sets. Fuzzy Sets and Systems, 79, 403-405. CHARNES, A., COOPER, W. W. & RHODES, E. 1978. Measuring the efficiency of decision making units. European journal of operational research, 2, 429-444. CHEN, N. & XU, Z. 2015. Hesitant fuzzy ELECTRE II approach: A new way to handle multi-criteria decision making problems. Information Sciences, 292, 175-197. CHEN, T.-Y. 2014. An ELECTRE-based outranking method for multiple criteria group decision making using interval type-2 fuzzy sets. Information Sciences, 263, 1-21. DANESHVAR ROUYENDEGH, B. 2011. The DEA and intuitionistic fuzzy TOPSIS approach to departments' performances: a pilot study. Journal of Applied Mathematics, 2011. DAVOUDABADI, R., MOUSAVI, S.M., MOHAGHEGHI, V., 2020. A new last aggregation method of multi-attributes group decision making based on concepts of TODIM, WASPAS and TOPSIS under interval-valued intuitionistic fuzzy uncertainty, Knowledge and Information Systems, 62, 1371–1391. DU, Y. & LIU, P. 2011. Extended fuzzy VIKOR method with intuitionistic trapezoidal fuzzy numbers. Information-An International Interdisciplinary Journal, 14, 2575-2583. EBRAHIMNEJAD, S., MOUSAVI, S., TAVAKKOLI-MOGHADDAM, R., HASHEMI, H. & VAHDANI, B. 2012. A novel two-phase group decision making approach for construction project selection in a fuzzy environment. Applied Mathematical Modelling, 36, 4197-4217. GAU, W.-L. & BUEHRER, D. J. 1993. Vague sets. IEEE transactions on systems, man, and cybernetics, 23, 610-614. GHADERI, H., BABAZADEH, R., MOINI, A. & PISHVAEE, M. S. 2019. Efficiency assessment of switchgrass cultivation areas using sustainable indicators under epistemic uncertainty. Computers and electronics in agriculture, 157, 12-22. GHADERI, H., GITINAVARD, H., MOUSAVI, S. M. & VAHDANI, B. 2017. A hesitant fuzzy cognitive mapping approach with risk preferences for student accommodation problems. International Journal of Applied Management Science, 9, 253-293. GITINAVARD, H. & AKBARPOUR SHIRAZI, M. 2018. An extended intuitionistic fuzzy modified group complex proportional assessment approach. Journal of Industrial and Systems Engineering, 11, 229-246. GITINAVARD, H., GHADERI, H. & PISHVAEE, M. S. 2018. Green supplier evaluation in manufacturing systems: a novel interval-valued hesitant fuzzy group outranking approach. Soft Computing, 22, 6441-6460. GITINAVARD, H., MOUSAVI, S. M. & VAHDANI, B. 2017. Soft computing based on hierarchical evaluation approach and criteria interdependencies for energy decision-making problems: A case study. Energy, 118, 556-577. GITINAVARD, H. & ZARANDI, M. H. F. 2016. A mixed expert evaluation system and dynamic interval-valued hesitant fuzzy selection approach. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 10, 337-345. HAJIAGHA, S. H. R., AKRAMI, H., ZAVADSKAS, E. K. & HASHEMI, S. S. 2013. An intuitionistic fuzzy data envelopment analysis for efficiency evaluation under uncertainty: case of a finance and credit institution. E&M Ekonomie a Management, 16, 128-137. HAJIGHASEMI, Z., MOUSAVI, S.M., 2018. A new approach in failure modes and effects analysis based on compromise solution by considering objective and subjective weights with interval-valued intuitionistic fuzzy sets, Iranian Journal of Fuzzy Systems, 15(1), 2018, 139-161. LIM, S., OH, K. W. & ZHU, J. 2014. Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market. European Journal of Operational Research, 236, 361-368. LIU, J., SONG, J., XU, Q., TAO, Z. & CHEN, H. 2019. Group decision making based on DEA cross-efficiency with intuitionistic fuzzy preference relations. Fuzzy Optimization and Decision Making, 18, 345-370. MA, R., YAO, L., JIN, M. & REN, P. 2014. The DEA Game Cross-efficiency Model for Supplier Selection Problem under Competition. Appl. Math, 8, 811-818. MANSOURI, A., EBRAHIMI, N. & RAMAZANI, M. 2014. Ranking of Companies Considering TOPSIS-DEA Approach Methods (Evidence from Cement Industry in Tehran Stock Exchange). Pakistan Journal of Statistics & Operation Research, 10. MENG, D. & PEI, Z. 2013. On weighted unbalanced linguistic aggregation operators in group decision making. Information Sciences, 223, 31-41. MOUSAVI, S. M. & VAHDANI, B. 2016. Cross-docking location selection in distribution systems: a new intuitionistic fuzzy hierarchical decision model. International Journal of computational intelligence Systems, 9, 91-109. MOUSAVI, S. M., VAHDANI, B. & BEHZADI, S. S. 2016. Designing a model of intuitionistic fuzzy VIKOR in multi-attribute group decision-making problems. Iranian Journal of Fuzzy Systems, 13, 45-65. MOUSAVI, S.M., ANTUCHEVIČIENĖ, J., ZAVADSKAS, E. K., VAHDANI, B., HASHEMI, H., 2019. A new decision model for cross-docking center location in logistics networks under interval-valued intuitionistic fuzzy uncertainty, Transport, 34(1), 30-40. OTAY, İ., OZTAYSI, B., ONAR, S. C. & KAHRAMAN, C. 2017. Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Systems, 133, 90-106. QIN, J. & LIU, X. 2015. Multi-attribute group decision making using combined ranking value under interval type-2 fuzzy environment. Information Sciences, 297, 293-315. SZMIDT, E. & KACPRZYK, J. 2000. Distances between intuitionistic fuzzy sets. Fuzzy sets and systems, 114, 505-518. TOLOO, M. 2015. Alternative minimax model for finding the most efficient unit in data envelopment analysis. Computers & Industrial Engineering, 81, 186-194. VAHDANI, B., MOUSAVI, S. M., TAVAKKOLI-MOGHADDAM, R. & HASHEMI, H. 2013. A new design of the elimination and choice translating reality method for multi-criteria group decision-making in an intuitionistic fuzzy environment. Applied Mathematical Modelling, 37, 1781-1799. WEI, G., ZHAO, X. & LIN, R. 2010. Some induced aggregating operators with fuzzy number intuitionistic fuzzy information and their applications to group decision making. International Journal of Computational Intelligence Systems, 3, 84-95. WU, H., YANG, J., CHEN, Y., LIANG, L. & CHEN, Y. 2019a. DEA-based production planning considering production stability. INFOR: Information Systems and Operational Research, 57, 477-494. WU, P., WANG, Y., CHIU, Y.-H., LI, Y. & LIN, T.-Y. 2019b. Production efficiency and geographical location of Chinese coal enterprises-undesirable EBM DEA. Resources Policy, 64, 101527. XIA, P., WU, J., JI, X. & XI, P. 2020. A DEA-based empirical analysis for dynamic performance of China's regional coke production chain. Science of The Total Environment, 717, 136890. XU, Z.-S. & CHEN, J. 2007. An interactive method for fuzzy multiple attribute group decision making. Information Sciences, 177, 248-263. XU, Z. 2007. Intuitionistic fuzzy aggregation operators. Fuzzy Systems, IEEE Transactions on, 15, 1179-1187. XU, Z. 2010. A deviation-based approach to intuitionistic fuzzy multiple attribute group decision making. Group Decision and Negotiation, 19, 57-76. XU, Z. & YAGER, R. R. 2006. Some geometric aggregation operators based on intuitionistic fuzzy sets. International journal of general systems, 35, 417-433. YUE, Z. 2014. TOPSIS-based group decision-making methodology in intuitionistic fuzzy setting. Information Sciences, 277, 141-153. ZADEH, L. 1965. Fuzzy set theory and its applications. Kluwer-Nijhoff, Boston. ZHANG, X. & LIU, P. 2010. Method for aggregating triangular fuzzy intuitionistic fuzzy information and its application to decision making. Technological and economic development of economy, 280-290. ZHANG, X. & XU, Z. 2014. Interval programming method for hesitant fuzzy multi-attribute group decision making with incomplete preference over alternatives. Computers & Industrial Engineering, 75, 217-229 SALARPOUR, H., GHODRATI AMIRI, G., & MOUSAVI, S.M., 2019. Criteria assessment in sustainable macromanagement of housing provision problem by a multi-phase decision approach with DEMATEL and dynamic uncertainty, Arabian Journal for Science and Engineering 44, 7313–733. | ||
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