A Multigranular Linguistic Additive Ratio Assessment Model in Group Decision Making
Most of the multi-criteria group decision making (MCGDM) problems dealing with qualitative criteria require consideration of the large background of expert information. It is common that experts have different degrees of knowledge for giving their alternative assessments according to criteria. So, it seems logical that they use different evaluation scales to express their judgment, i.e., multi granular linguistic scales. In this context, we propose the extension of the classical additive ratio assessment (ARAS) method to the case of a hierarchical linguistics term for managing multi granular linguistic scales in uncertain context where uncertainty is modeled by means in linguistic information. The proposed approach is called the extended hierarchical linguistics-ARAS method (ELH-ARAS). Within the ELH-ARAS approach, the decision maker (DMs) can diagnose the results (the ranking of the alternatives) in a decomposed style i.e., not only at one level of the hierarchy but also at the intermediate ones. Also, the developed approach allows a feedback transformation i.e., the collective final results of all experts are able to be transformed at any level of the extended linguistic hierarchy that each expert has previously used. Therefore, the ELH-ARAS technique makes it easier for decision-makers to understand the results. Finally, an MCGDM case study is given to illustrate the proposed approach.
[1] L. A. Zadeh, “The concept of a linguistic variable and its applications to approximate reasoning,” Information Sciences, Part I, II, III, Vol. 8, no. 3, pp. 199-249, 1975.
[2] R. Degani, G Bortolan, “The Problem of linguistic approximation in clinical decision making,” International Journal of Approximate Reasoning, Vol. 2, no. 2, pp. 143-162, 1988.
[3] M. Delgado, J. L. Verdegay, M. A. Vila, “On Aggregation operations of linguistic labels. International Journal of Intelligent Systems,” Vol. 2, no. 3, pp. 351-370, 1993.
[4] F. Herrera, L. Martinez, “A 2-Tuple fuzzy linguistic representation model for computing with words (Translation Journals style),” IEEE Transactions on Fuzzy Systems, Vol. 8, no. 6, pp. 746-752, December 2000.
[5] T. C. Wen, K. H. Chang, H. H. Lai, “Integrating the 2-tuple linguistic representation and soft set to solve supplier selection problems with incomplete information,” Engineering Applications of Artificial Intelligence Vol. 87, pp. 103248, 2020.
[6] Z. P. Fan, W. L. Suo, B. Feng, “Identifying risk factors of IT outsourcing using interdependent information: An extended DEMATEL method,” Expert Systems with Applications, Vol. 39, no, 3, pp. 3832-3840, 15 February 2012.
[7] X. Y. You, J. X. You, H. C. Liu, L. Zhen, “Group multi-criteria supplier selection using an extended VIKOR method with interval 2-tuple linguistic information,” Expert Systems with Applications, Vol. 42, no. 4, pp. 1906-1916, March 2015.
[8] S. Zhang, J. Zhu, X. Liu, Y. Chen, “Adaptive consensus model with multiplicative linguistic preferences based on fuzzy information granulation,” Applied Soft Computing, vol. 60, pp. 30-47, November 2017.
[9] S. P. Wan, G, l, Xua, J. Y. Dong, “Supplier selection using ANP and ELECTRE II in interval 2-tuple linguistic environment,” Information Sciences, Vol. 385–386, pp. 19-38, April 2017.
[10] N. Halouani, L. Martínez, H. Chabchoub, J. M. Martel, J. Liu, “A Multi-granular Linguistic Promethee Model,”. IFSA-EUSFLAT conference, ISBN: 978-989-95079-6-8, 2009.
[11] A. I. Maghsoodi, H. Rasoulipanah, L. M. López, H. Liao, E. K. Zavadskas, Integrating interval-valued multi-granular 2-tuple linguistic BWM-CODAS approach with target-based attributes: Site selection for a construction project,” Computers & Industrial Engineering, vol. 139, pp, 106147, November 2019.
[12] Qun Wu, X. Liu, J. Qin, W. Wang, L. Zhoud, “Linguistic distribution behavioral multi-criteria group decision making model integrating extended generalized TODIM and quantum decision theory,” Applied Soft Computing, Vol 98, pp. 106757, January 2021.
[13] E. K. Zavadskas, Z. Turskis, “A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy, Vol. 16, no, 2, pp. 159–172, April 2010.
[14] M. A. Prayogo, J. E. Suseno, D. M. K. Nugraheni, “Selecting palm oil cultivation land using ARAS method,” in International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) 2019, pp. 358‐362, December 2019.
[15] L Balezentiene, A. Kusta, “Reducing greenhouse gas emissions in grassland ecosystems of the central Lithuania: multi‐criteria evaluation on a basis of the ARAS method,” The Scientific World Journal, id. 908384 pp. 11, January 2012.
[16] J. Sliogeriene, Z. Turskis, D. Streimikiene, “Analysis and choice of energy generation technologies: the multiple criteria assessment on the case study of Lithuania (to be published),” Energy Procedia, vol. 32, pp. 11‐20, 2013.
[17] K. Jaukovic Jocic, G, Jocic, D. Karabasevic et al, “A novel integrated PIPRECIA‐interval‐valued triangular fuzzy ARAS model: E‐learning course selection,” Symmetry (Basel), vol 12, no. 6, pp. 928, 2020.
[18] B. F. Yildirim, B. A. Mercangoz, “Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS‐G,” Eurasian Econ Rev, vol. 10, no. 1, pp. 27‐45, 2020.
[19] H. Prajapati, R. Kant, S.M. Tripathi, “An integrated framework for prioritizing the outsourcing performance outcomes,” J Glob Oper Strategic Sourcing, vol. 13, no. 4, pp. 301‐325, 2020.
[20] A. K. Yazdi, P. F. Wanke, T. Hanne, E. Bottani. A decision‐support approach under uncertainty for evaluating reverse logistics capabilities of healthcare providers in Iran,” J Enterp Inf Manage, vol. 33, no. 5, pp. 991‐1022, 2020.
[21] J. H. Dahooie, E.K, Zavadskas, A. S. Vanaki, H. R. Firoozfar, M. Lari, Z. Turskis, “A new evaluation model for corporate financial performance using integrated CCSD and FCM‐ARAS approach,” Econ Res Istraz, vol. 32, no. 1, pp. 1088‐1113, 2019.
[22] F. Rajabi, M. Jahangiri, F. Bagherifard, S. Banaee, P. Farhadi,” Strategies for controlling violence against health care workers: application of fuzzy analytical hierarchy process and fuzzy additive ratio assessment,” J Nurs Manage, vol. 28, no. 4, pp. 777‐786, 2020.
[23] M. Varmazyar, M. Dehghanbaghi, M. Afkhami, “A novel hybrid MCDM model for performance evaluation of research and technology organizations based on BSC approach,” Eval Program Plann, vol. 58, pp. 125‐140, October 2016.
[24] M. Espinilla, J. Liu, L. Martínez, “An extended hierarchical linguistic model for decision-making problems,” vol. 27, no. 3, pp. 489-512, August 2011.
[25] F. Herrera, L, Martinez, “A Model based on linguistic 2-Tuples for dealing with multigranularity hierarchical linguistic contexts in multiexpert decision-making,” IEEE Transactions on Systems, Man and Cybernetics, vol. 31, no. 2, pp. 227 – 234. April 2001.
[1] L. A. Zadeh, “The concept of a linguistic variable and its applications to approximate reasoning,” Information Sciences, Part I, II, III, Vol. 8, no. 3, pp. 199-249, 1975.
[2] R. Degani, G Bortolan, “The Problem of linguistic approximation in clinical decision making,” International Journal of Approximate Reasoning, Vol. 2, no. 2, pp. 143-162, 1988.
[3] M. Delgado, J. L. Verdegay, M. A. Vila, “On Aggregation operations of linguistic labels. International Journal of Intelligent Systems,” Vol. 2, no. 3, pp. 351-370, 1993.
[4] F. Herrera, L. Martinez, “A 2-Tuple fuzzy linguistic representation model for computing with words (Translation Journals style),” IEEE Transactions on Fuzzy Systems, Vol. 8, no. 6, pp. 746-752, December 2000.
[5] T. C. Wen, K. H. Chang, H. H. Lai, “Integrating the 2-tuple linguistic representation and soft set to solve supplier selection problems with incomplete information,” Engineering Applications of Artificial Intelligence Vol. 87, pp. 103248, 2020.
[6] Z. P. Fan, W. L. Suo, B. Feng, “Identifying risk factors of IT outsourcing using interdependent information: An extended DEMATEL method,” Expert Systems with Applications, Vol. 39, no, 3, pp. 3832-3840, 15 February 2012.
[7] X. Y. You, J. X. You, H. C. Liu, L. Zhen, “Group multi-criteria supplier selection using an extended VIKOR method with interval 2-tuple linguistic information,” Expert Systems with Applications, Vol. 42, no. 4, pp. 1906-1916, March 2015.
[8] S. Zhang, J. Zhu, X. Liu, Y. Chen, “Adaptive consensus model with multiplicative linguistic preferences based on fuzzy information granulation,” Applied Soft Computing, vol. 60, pp. 30-47, November 2017.
[9] S. P. Wan, G, l, Xua, J. Y. Dong, “Supplier selection using ANP and ELECTRE II in interval 2-tuple linguistic environment,” Information Sciences, Vol. 385–386, pp. 19-38, April 2017.
[10] N. Halouani, L. Martínez, H. Chabchoub, J. M. Martel, J. Liu, “A Multi-granular Linguistic Promethee Model,”. IFSA-EUSFLAT conference, ISBN: 978-989-95079-6-8, 2009.
[11] A. I. Maghsoodi, H. Rasoulipanah, L. M. López, H. Liao, E. K. Zavadskas, Integrating interval-valued multi-granular 2-tuple linguistic BWM-CODAS approach with target-based attributes: Site selection for a construction project,” Computers & Industrial Engineering, vol. 139, pp, 106147, November 2019.
[12] Qun Wu, X. Liu, J. Qin, W. Wang, L. Zhoud, “Linguistic distribution behavioral multi-criteria group decision making model integrating extended generalized TODIM and quantum decision theory,” Applied Soft Computing, Vol 98, pp. 106757, January 2021.
[13] E. K. Zavadskas, Z. Turskis, “A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy, Vol. 16, no, 2, pp. 159–172, April 2010.
[14] M. A. Prayogo, J. E. Suseno, D. M. K. Nugraheni, “Selecting palm oil cultivation land using ARAS method,” in International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) 2019, pp. 358‐362, December 2019.
[15] L Balezentiene, A. Kusta, “Reducing greenhouse gas emissions in grassland ecosystems of the central Lithuania: multi‐criteria evaluation on a basis of the ARAS method,” The Scientific World Journal, id. 908384 pp. 11, January 2012.
[16] J. Sliogeriene, Z. Turskis, D. Streimikiene, “Analysis and choice of energy generation technologies: the multiple criteria assessment on the case study of Lithuania (to be published),” Energy Procedia, vol. 32, pp. 11‐20, 2013.
[17] K. Jaukovic Jocic, G, Jocic, D. Karabasevic et al, “A novel integrated PIPRECIA‐interval‐valued triangular fuzzy ARAS model: E‐learning course selection,” Symmetry (Basel), vol 12, no. 6, pp. 928, 2020.
[18] B. F. Yildirim, B. A. Mercangoz, “Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS‐G,” Eurasian Econ Rev, vol. 10, no. 1, pp. 27‐45, 2020.
[19] H. Prajapati, R. Kant, S.M. Tripathi, “An integrated framework for prioritizing the outsourcing performance outcomes,” J Glob Oper Strategic Sourcing, vol. 13, no. 4, pp. 301‐325, 2020.
[20] A. K. Yazdi, P. F. Wanke, T. Hanne, E. Bottani. A decision‐support approach under uncertainty for evaluating reverse logistics capabilities of healthcare providers in Iran,” J Enterp Inf Manage, vol. 33, no. 5, pp. 991‐1022, 2020.
[21] J. H. Dahooie, E.K, Zavadskas, A. S. Vanaki, H. R. Firoozfar, M. Lari, Z. Turskis, “A new evaluation model for corporate financial performance using integrated CCSD and FCM‐ARAS approach,” Econ Res Istraz, vol. 32, no. 1, pp. 1088‐1113, 2019.
[22] F. Rajabi, M. Jahangiri, F. Bagherifard, S. Banaee, P. Farhadi,” Strategies for controlling violence against health care workers: application of fuzzy analytical hierarchy process and fuzzy additive ratio assessment,” J Nurs Manage, vol. 28, no. 4, pp. 777‐786, 2020.
[23] M. Varmazyar, M. Dehghanbaghi, M. Afkhami, “A novel hybrid MCDM model for performance evaluation of research and technology organizations based on BSC approach,” Eval Program Plann, vol. 58, pp. 125‐140, October 2016.
[24] M. Espinilla, J. Liu, L. Martínez, “An extended hierarchical linguistic model for decision-making problems,” vol. 27, no. 3, pp. 489-512, August 2011.
[25] F. Herrera, L, Martinez, “A Model based on linguistic 2-Tuples for dealing with multigranularity hierarchical linguistic contexts in multiexpert decision-making,” IEEE Transactions on Systems, Man and Cybernetics, vol. 31, no. 2, pp. 227 – 234. April 2001.
@article{"International Journal of Information, Control and Computer Sciences:80919", author = "Wiem Daoud Ben Amor and Luis Martínez López and Jr. and Hela Moalla Frikha", title = "A Multigranular Linguistic Additive Ratio Assessment Model in Group Decision Making", abstract = "Most of the multi-criteria group decision making (MCGDM) problems dealing with qualitative criteria require consideration of the large background of expert information. It is common that experts have different degrees of knowledge for giving their alternative assessments according to criteria. So, it seems logical that they use different evaluation scales to express their judgment, i.e., multi granular linguistic scales. In this context, we propose the extension of the classical additive ratio assessment (ARAS) method to the case of a hierarchical linguistics term for managing multi granular linguistic scales in uncertain context where uncertainty is modeled by means in linguistic information. The proposed approach is called the extended hierarchical linguistics-ARAS method (ELH-ARAS). Within the ELH-ARAS approach, the decision maker (DMs) can diagnose the results (the ranking of the alternatives) in a decomposed style i.e., not only at one level of the hierarchy but also at the intermediate ones. Also, the developed approach allows a feedback transformation i.e., the collective final results of all experts are able to be transformed at any level of the extended linguistic hierarchy that each expert has previously used. Therefore, the ELH-ARAS technique makes it easier for decision-makers to understand the results. Finally, an MCGDM case study is given to illustrate the proposed approach.", keywords = "Additive ratio assessment, extended hierarchical linguistic, multi-criteria group decision making problems, multi granular linguistic contexts.", volume = "16", number = "8", pages = "357-6", }
