ORIGINAL ARTICLE
Group multigranular linguistic QFD for prioritizing service designs with combined weighting method
Zhang-peng Tian,
Ru-xin Nie,
Jian-qiang Wang,
Lin Li,
Zhang-peng Tian
School of Business, Central South University, Changsha, China
Search for more papers by this authorRu-xin Nie
School of Business, Central South University, Changsha, China
Search for more papers by this authorCorresponding Author
Jian-qiang Wang
School of Business, Central South University, Changsha, China
Correspondence
Jian-qiang Wang, School of Business, Central South University, Changsha 410083, China.
Email: [email protected]
Search for more papers by this authorZhang-peng Tian,
Ru-xin Nie,
Jian-qiang Wang,
Lin Li,
Zhang-peng Tian
School of Business, Central South University, Changsha, China
Search for more papers by this authorRu-xin Nie
School of Business, Central South University, Changsha, China
Search for more papers by this authorCorresponding Author
Jian-qiang Wang
School of Business, Central South University, Changsha, China
Correspondence
Jian-qiang Wang, School of Business, Central South University, Changsha 410083, China.
Email: [email protected]
Search for more papers by this authorAbstract
Qualification for the importance degree of service designs (SDs) is an essential stage in promoting accommodation performances to satisfy customers and gain market shares. Although various methods have been applied to derive the priorities of SDs, they do not effectively deal with the vagueness of information and the heterogeneity of decision makers (DMs). This study presents an improved quality function deployment (QFD) for prioritizing SDs. Multigranular unbalanced linguistic term sets are used to capture evaluators' ratings to cope with vague information. The multigranular linguistic information is unified by using a novel uniform method. This kind of information then is represented as linguistic distribution assessments using the concept of random preferences based on possibility interpretations of weight information. A consensus-based mathematical programming model is established to determine the weights of DMs. Moreover, a combined structure that combines maximizing deviation and best worst methods is used to derive basic weights of SDs. Finally, an illustrative example of island accommodation management in Weizhou Island is provided to demonstrate the applicability and advantages of the proposed QFD framework.
REFERENCES
- Akao, Y. (1972). New product development and quality assurance-quality deployment system. Standardization and Quality Control, 25(4), 7–14.
- Akbaba, A. (2006). Measuring service quality in the hotel industry: A study in a business hotel in Turkey. International Journal of Hospitality Management, 25(2), 170–192. https://doi.org/10.1016/j.ijhm.2005.08.006
10.1016/j.ijhm.2005.08.006 Google Scholar
- Akkawuttiwanich, P., & Yenradee, P. (2018). Fuzzy QFD approach for managing SCOR performance indicators. Computers & Industrial Engineering, 122, 189–201. https://doi.org/10.1016/j.cie.2018.05.044
- Asadabadi, M. R. (2017). A customer based supplier selection process that combines quality function deployment, the analytic network process and a Markov chain. European Journal of Operational Research, 263(3), 1049–1062. https://doi.org/10.1016/j.ejor.2017.06.006
- Babbar, C., & Amin, S. H. (2018). A multi-objective mathematical model integrating environmental concerns for supplier selection and order allocation based on fuzzy QFD in beverages industry. Expert Systems with Applications, 92, 27–38. https://doi.org/10.1016/j.eswa.2017.09.041
- Beheshtinia, M. A., & Farzaneh Azad, M. (2017). A fuzzy QFD approach using SERVQUAL and Kano models under budget constraint for hotel services. Total Quality Management & Business Excellence, 1–23. https://doi.org/10.1080/14783363.2017.1340830
- Cabrerizo, F. J., Herrera-Viedma, E., & Pedrycz, W. (2013). A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts. European Journal of Operational Research, 230(3), 624–633. https://doi.org/10.1016/j.ejor.2013.04.046
- Chan, L., & Wu, M. (2002). Quality function deployment: A literature review. European Journal of Operational Research, 143(3), 463–497. https://doi.org/10.1016/S0377-2217(02)00178-9
- Chen, X., Zhang, H., & Dong, Y. (2015). The fusion process with heterogeneous preference structures in group decision making: A survey. Information Fusion, 24, 72–83. https://doi.org/10.1016/j.inffus.2014.11.003
- Cheng, C., Tsai, M., & Lin, C. (2016). Quality education service: put your feet in their shoes. Current Issues in Tourism, 19(11), 1120–1135. https://doi.org/10.1080/13683500.2013.839633
- Dong, Y., Wu, Y., Zhang, H., & Zhang, G. (2015). Multi-granular unbalanced linguistic distribution assessments with interval symbolic proportions. Knowledge-Based Systems, 82, 139–151. https://doi.org/10.1016/j.knosys.2015.03.003
- Dong, Y., Zhang, G., Hong, W., & Yu, S. (2013). Linguistic computational model based on 2-tuples and intervals. IEEE Transactions on Fuzzy Systems, 21(6), 1006–1018. https://doi.org/10.1109/TFUZZ.2013.2239650
- Fick, G. R., & Ritchie, J. R. B. (1991). Measuring service quality in the travel and tourism industry. Journal of Travel Research, 30(2), 2–9. https://doi.org/10.1177/004728759103000201
10.1177/004728759103000201 Google Scholar
- Gomezelj, D. O. (2016). A systematic review of research on innovation in hospitality and tourism. International Journal of Contemporary Hospitality Management, 28(3), 516–558. https://doi.org/10.1108/IJCHM-10-2014-0510
- Haber, N., Fargnoli, M., & Sakao, T. (2018). Integrating QFD for product-service systems with the Kano model and fuzzy AHP. Total Quality Management & Business Excellence, 1–26. https://doi.org/10.1080/14783363.2018.1470897
10.1080/14783363.2018.1470897 Google Scholar
- Herrera, F., Herrera-Viedma, E., & Martinez, L. (2000). A fusion approach for managing multi-granularity linguistic term sets in decision making. Fuzzy Sets and Systems, 114(1), 43–58. https://doi.org/10.1016/S0165-0114(98)00093-1
- Herrera, F., Herrera-Viedma, E., & Martinez, L. (2008). A fuzzy linguistic methodology to deal with unbalanced linguistic term sets. IEEE Transactions on Fuzzy Systems, 16(2), 354–370. https://doi.org/10.1109/TFUZZ.2007.896353
- Herrera, F., & Martínez, L. (2000). A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 8(6), 746–752.
- Herrera-Viedma, E., Cabrerizo, F. J., Kacprzyk, J., & Pedrycz, W. (2014). A review of soft consensus models in a fuzzy environment. Information Fusion, 17(1), 4–13. https://doi.org/10.1016/j.inffus.2013.04.002
- Hsu, C., Chang, A., & Luo, W. (2017). Identifying key performance factors for sustainability development of SMEs—Integrating QFD and fuzzy MADM methods. Journal of Cleaner Production, 161, 629–645. https://doi.org/10.1016/j.jclepro.2017.05.063
- Huang, J., You, X., Liu, H., & Si, S. (2019). New approach for quality function deployment based on proportional hesitant fuzzy linguistic term sets and prospect theory. International Journal of Production Research, 57(5), 1283–1299. https://doi.org/10.1080/00207543.2018.1470343
- Jia, W., Liu, Z., Lin, Z., Qiu, C., & Tan, J. (2016). Quantification for the importance degree of engineering characteristics with a multi-level hierarchical structure in QFD. International Journal of Production Research, 54(6), 1627–1649. https://doi.org/10.1080/00207543.2015.1041574
- Kuo, C., Chen, H., & Boger, E. (2016). Implementing city hotel service quality enhancements: Integration of Kano and QFD analytical models. Journal of Hospitality Marketing & Management, 25(6), 748–770. https://doi.org/10.1080/19368623.2016.1096225
- Kurtulmuşoğlu, F. B., & Pakdil, F. (2017). Combined analysis of service expectations and perceptions in lodging industry through quality function deployment. Total Quality Management & Business Excellence, 28(11–12), 1393–1413. https://doi.org/10.1080/14783363.2016.1147945
- Li, C., Dong, Y., & Herrera, F. (2019). A consensus model for large-scale linguistic group decision making with a feedback recommendation based on clustered personalized individual semantics and opposing consensus groups. IEEE Transactions on Fuzzy Systems, 27(2), 221–233. https://doi.org/10.1109/TFUZZ.2018.2857720
- Li, C., Rodríguez, R. M., Martínez, L., Dong, Y., & Herrera, F. (2018). Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions. Knowledge-Based Systems, 145, 156–165. https://doi.org/10.1016/j.knosys.2018.01.011
- Li, J., Wang, J., & Hu, J. (2018). Multi-criteria decision-making method based on dominance degree and BWM with probabilistic hesitant fuzzy information. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-018-0845-2
- Li, J., Wang, J., & Hu, J. (2019). Consensus building for hesitant fuzzy preference relations with multiplicative consistency. Computers & Industrial Engineering, 128, 387–400. https://doi.org/10.1016/j.cie.2018.12.051
- Li, X., & He, Z. (2017). Determining importance ratings of patients' requirements with multi-granular linguistic evaluation information. International Journal of Production Research, 55(14), 4110–4122. https://doi.org/10.1080/00207543.2016.1253890
- Li, Y., Du, Y., & Chin, K. (2018). Determining the importance ratings of customer requirements in quality function deployment based on interval linguistic information. International Journal of Production Research, 56(14), 4692–4708. https://doi.org/10.1080/00207543.2017.1417650
- Lin, L. Z., Huang, L. C., & Yeh, H. R. (2012). Fuzzy group decision-making for service innovations in quality function deployment. Group Decision and Negotiation, 21(4), 495–517. https://doi.org/10.1007/s10726-010-9223-5
- Luo, S., Zhang, H., Wang, J., & Li, L. (2019). Group decision-making approach for evaluating the sustainability of constructed wetlands with probabilistic linguistic preference relations. Journal of the Operational Research Society, 1–17. https://doi.org/10.1080/01605682.2018.1510806
10.1080/01605682.2018.1510806 Google Scholar
- Lupo, T. (2016). A fuzzy framework to evaluate service quality in the healthcare industry: An empirical case of public hospital service evaluation in Sicily. Applied Soft Computing, 40, 468–478. https://doi.org/10.1016/j.asoc.2015.12.010
- Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (2019). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next? Omega. https://doi.org/10.1016/j.omega.2019.01.009
- Miyoung, J., & Haemoon, O. (1998). Quality function deployment: An extended framework for service quality and customer satisfaction in the hospitality industry. International Journal of Hospitality Management, 17(4), 375–390.
10.1016/S0278-4319(98)00024-3 Google Scholar
- Morente-Molinera, J. A., Kou, G., González-Crespo, R., Corchado, J. M., & Herrera-Viedma, E. (2017). Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowledge-Based Systems, 137, 54–64. https://doi.org/10.1016/j.knosys.2017.09.010
- Morente-Molinera, J. A., Kou, G., Pérez, I. J., Samuylov, K., Selamat, A., & Herrera-Viedma, E. (2018). A group decision making support system for the Web: How to work in environments with a high number of participants and alternatives. Applied Soft Computing, 68, 191–201. https://doi.org/10.1016/j.asoc.2018.03.047
- Morente-Molinera, J. A., Pérez, I. J., Ureña, M. R., & Herrera-Viedma, E. (2015). On multi-granular fuzzy linguistic modeling in group decision making problems: A systematic review and future trends. Knowledge-Based Systems, 74, 49–60. https://doi.org/10.1016/j.knosys.2014.11.001
- Nie, R. X., Tian, Z. P., Wang, J. Q., Zhang, H. Y., & Wang, T. L. (2018). Water security sustainability evaluation: Applying a multistage decision support framework in industrial region. Journal of Cleaner Production, 196, 1681–1704. https://doi.org/10.1016/j.jclepro.2018.06.144
- Nie, R. X., Tian, Z. P., Wang, X. K., Wang, J. Q., & Wang, T. L. (2018). Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment. Knowledge-Based Systems, 162, 185–201. https://doi.org/10.1016/j.knosys.2018.05.030
- Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
- Osiro, L., Lima-Junior, F. R., & Carpinetti, L. C. R. (2018). A group decision model based on quality function deployment and hesitant fuzzy for selecting supply chain sustainability metrics. Journal of Cleaner Production, 183, 964–978. https://doi.org/10.1016/j.jclepro.2018.02.197
- Palomares, I., Estrella, F. J., Martínez, L., & Herrera, F. (2014). Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study. Information Fusion, 20(15), 252–271. https://doi.org/10.1016/j.inffus.2014.03.002
- Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
- Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001
- Rezaei, J., Kothadiya, O., Tavasszy, L., & Kroesen, M. (2018). Quality assessment of airline baggage handling systems using SERVQUAL and BWM. Tourism Management, 66, 85–93. https://doi.org/10.1016/j.tourman.2017.11.009
- Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 135, 577–588. https://doi.org/10.1016/j.jclepro.2016.06.125
- Sivasamy, K., Arumugam, C., Devadasan, S. R., Murugesh, R., & Thilak, V. M. M. (2016). Advanced models of quality function deployment: A literature review. Quality & Quantity, 50(3), 1399–1414. https://doi.org/10.1007/s11135-015-0212-2
10.1007/s11135-015-0212-2 Google Scholar
- Tian, Z. P., Nie, R. X., Wang, J. Q., & Zhang, H. Y. (2019). Signed distance-based ORESTE for multi-criteria group decision-making with multi-granular unbalanced hesitant fuzzy linguistic information. Expert Systems, 36. https://doi.org/10.1111/exsy.12350
- Tian, Z. P., Wang, J. Q., & Zhang, H. Y. (2018). An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Applied Soft Computing, 72, 636–646. https://doi.org/10.1016/j.asoc.2018.03.037
- Tian, Z. P., Wang, J. Q., Zhang, H. Y., & Wang, T. L. (2018). Signed distance-based consensus in multi-criteria group decision-making with multi-granular hesitant unbalanced linguistic information. Computers & Industrial Engineering, 124, 125–138. https://doi.org/10.1016/j.cie.2018.07.017
- Tian, Z. P., Zhang, H. Y., Wang, J. Q., & Wang, T. L. (2018). Green supplier selection using improved TOPSIS and best-worst method under intuitionistic fuzzy environment. Informatica, 29(4), 773–800. https://doi.org/10.15388/Informatica.2018.192
- Wan, S., Wang, F., & Dong, J. (2017). Additive consistent interval-valued Atanassov intuitionistic fuzzy preference relation and likelihood comparison algorithm based group decision making. European Journal of Operational Research, 263(2), 571–582. https://doi.org/10.1016/j.ejor.2017.05.022
- Wang, H., Xu, Z., & Zeng, X. (2018a). Linguistic terms with weakened hedges: A model for qualitative decision making under uncertainty. Information Sciences, 433-434, 37–54. https://doi.org/10.1016/j.ins.2017.12.036
- Wang, H., Xu, Z., & Zeng, X. (2018b). Modeling complex linguistic expressions in qualitative decision making: An overview. Knowledge-Based Systems, 144, 174–187. https://doi.org/10.1016/j.knosys.2017.12.030
- Wang, J., & Hao, J. (2006). A new version of 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 14(3), 435–445. https://doi.org/10.1109/TFUZZ.2006.876337
- Wang, J., Wang, J., Chen, Q., Zhang, H., & Chen, X. (2014). An outranking approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets. Information Sciences, 280, 338–351. https://doi.org/10.1016/j.ins.2014.05.012
- Wang, S., & Hung, K. (2015). Customer perceptions of critical success factors for guest houses. International Journal of Hospitality Management, 48, 92–101. https://doi.org/10.1016/j.ijhm.2015.05.002
- Wang, Y. M. (1997). Using the method of maximizing deviation to make decision for multiindices. Journal of Systems Engineering and Electronics, 8(3), 21–26.
- Wang, Z., Fung, R. Y. K., Li, Y., & Pu, Y. (2016). A group multi-granularity linguistic-based methodology for prioritizing engineering characteristics under uncertainties. Computers & Industrial Engineering, 91, 178–187. https://doi.org/10.1016/j.cie.2015.11.012
- Wang, Z., Fung, R. Y. K., Li, Y., & Pu, Y. (2018). An integrated decision-making approach for designing and selecting product concepts based on QFD and cumulative prospect theory. International Journal of Production Research, 56(5), 2003–2018. https://doi.org/10.1080/00207543.2017.1351632
- Wu, S., Liu, H., & Wang, L. (2017). Hesitant fuzzy integrated MCDM approach for quality function deployment: A case study in electric vehicle. International Journal of Production Research, 55(15), 4436–4449. https://doi.org/10.1080/00207543.2016.1259670
- Wu, X., & Liao, H. (2018). An approach to quality function deployment based on probabilistic linguistic term sets and ORESTE method for multi-expert multi-criteria decision making. Information Fusion, 43, 13–26. https://doi.org/10.1016/j.inffus.2017.11.008
- Wu, Y., Li, C., Chen, X., & Dong, Y. (2018). Group decision making based on linguistic distributions and hesitant assessments: Maximizing the support degree with an accuracy constraint. Information Fusion, 41, 151–160. https://doi.org/10.1016/j.inffus.2017.08.008
- Xu, Z. (2009). An interactive approach to multiple attribute group decision making with multigranular uncertain linguistic information. Group Decision and Negotiation, 18(2), 119–145. https://doi.org/10.1007/s10726-008-9131-0
- Yan, H. B., & Ma, T. J. (2015). A group decision-making approach to uncertain quality function deployment based on fuzzy preference relation and fuzzy majority. European Journal of Operational Research, 241(3), 815–829. https://doi.org/10.1016/j.ejor.2014.09.017
- Yan, H. B., Ma, T. J., & Li, Y. S. (2013). A novel fuzzy linguistic model for prioritising engineering design requirements in quality function deployment under uncertainties. International Journal of Production Research, 51(21), 6336–6355. https://doi.org/10.1080/00207543.2013.796423
- Yazdani, M., Chatterjee, P., Zavadskas, E. K., & Hashemkhani Zolfani, S. (2017). Integrated QFD-MCDM framework for green supplier selection. Journal of Cleaner Production, 142, 3728–3740. https://doi.org/10.1016/j.jclepro.2016.10.095
- Yu, P. L. (1973). A class of solutions for group decision problems. Management Science, 19(8), 936–946. https://doi.org/10.1287/mnsc.19.8.936
- Yu, W., Zhang, Z., Zhong, Q., & Sun, L. (2017). Extended TODIM for multi-criteria group decision making based on unbalanced hesitant fuzzy linguistic term sets. Computers & Industrial Engineering, 114, 316–328. https://doi.org/10.1016/j.cie.2017.10.029
- Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
- Zhang, C., Zhang, H., & Wang, J. (2018). Personalized restaurant recommendation method combining group correlations and customer preferences. Information Sciences, 454-455, 128–143. https://doi.org/10.1016/j.ins.2018.04.061
- Zhang, G., Dong, Y., & Xu, Y. (2014). Consistency and consensus measures for linguistic preference relations based on distribution assessments. Information Fusion, 17, 46–55. https://doi.org/10.1016/j.inffus.2012.01.006
- Zhang, H., Dong, Y., Chiclana, F., & Yu, S. (2019). Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design. European Journal of Operational Research, 275(2), 580–598. https://doi.org/10.1016/j.ejor.2018.11.052
- Zhang, X., Zhang, H., & Wang, J. (2019). Discussing incomplete 2-tuple fuzzy linguistic preference relations in multi-granular linguistic MCGDM with unknown weight information. Soft Computing, 23(6), 2015–2032. https://doi.org/10.1007/s00500-017-2915-x
- Zhang, Z., Guo, C., & Martínez, L. (2017). Managing multi-granular linguistic distribution assessments in large-scale multi-attribute group decision making. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(11), 3063–3076. https://doi.org/10.1109/TSMC.2016.2560521
