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A state-of-the-art review of the BWM method and future research agenda

    Fatih Ecer Affiliation

Abstract

The superiority of BWM over other weighting methods for obtaining the weight values of the attributes is that it achieves high-confidence results with a reasonable number of pairwise comparisons. Although the best-worst method (BWM) is a well-known multi-criteria decision-making (MCDM) method that has been successfully utilized in almost all scientific areas to solve challenging real-life problems, no research has comprehensively examined the state-of-the-art in this regard. The present study depicts a detailed overview of publications concerned with BWM during the period 2015–2022. Based on the information obtained from the Scopus database, this work presents a big picture of current research on BWM. In other words, this paper analyzes the existing literature about BWM and identifies thematic contexts, application areas, emerging trends, and remaining research gaps to shed light on future research agendas aligning with those gaps. Further, the most recent BWM research is analyzed in the top ten scientific areas, from engineering to materials science. “Engineering”, “computer science”, and “business, management, and accounting” are the hottest fields of BWM research. China is the most active country regarding “engineering” and “computer science”, whereas India is the leader in “business, management, and accounting”. The study also reveals that there are still many research gaps in BWM research. The big picture taken in this study will not only showcase the current situation of BWM research but will also positively impact the direction and quality of new research.

Keyword : best-worst method, bibliometric analysis, vosviewer, co-occurrence, co-citation, MCDM

How to Cite
Ecer, F. (2024). A state-of-the-art review of the BWM method and future research agenda. Technological and Economic Development of Economy, 30(4), 1165–1204. https://doi.org/10.3846/tede.2024.20761
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Jun 5, 2024
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References

Abbasi Kamardi, A., Amoozad Mahdiraji, H., Masoumi, S., & Jafari-Sadeghi, V. (2022). Developing sustainable competitive advantages from the lens of resource-based view: Evidence from IT sector of an emerging economy. Journal of Strategic Marketing, 1–23. https://doi.org/10.1080/0965254X.2022.2160485

Aboutorab, H., Saberi, M., Asadabadi, M. R., Hussain, O., & Chang, E. (2018). ZBWM: The Z-number extension of Best Worst Method and its application for supplier development. Expert Systems with Applications, 107, 115–125. https://doi.org/10.1016/j.eswa.2018.04.015

Ahmad, S., Masood, S., Khan, N. Z., Badruddin, I. A., Ompal, Ahmadian, A., Khan, Z. A., & Khan, A. H. (2023). Analysing the impact of COVID-19 pandemic on the psychological health of people using fuzzy MCDM methods. Operations Research Perspectives, 10, Article 100263. https://doi.org/10.1016/j.orp.2022.100263

Ahmad, W. N. K. W., Rezaei, J., Sadaghiani, S., & Tavasszy, L. A. (2017). Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method. Journal of Cleaner Production, 153, 242–252. https://doi.org/10.1016/j.jclepro.2017.03.166

Alamoodi, A. H., Zaidan, B. B., Albahri, O. S., Garfan, S., Ahmaro, I. Y., Mohammed, R. T., Zaidan, A. A. Ritahani Ismail, A., Albahri, A. S., Momani, F., Al-Samarraay, M. S., Najm Jasim, A., & Malik, R. Q. (2023). Systematic review of MCDM approach applied to the medical case studies of COVID-19: Trends, bibliographic analysis, challenges, motivations, recommendations, and future directions. Complex & Intelligent Systems, 9, 4705–4731. https://doi.org/10.1007/s40747-023-00972-1

Ali, S. S., Kaur, R., & Khan, S. (2023). Evaluating sustainability initiatives in warehouse for measuring sustainability performance: An emerging economy perspective. Annals of Operations Research, 324, 461–500. https://doi.org/10.1007/s10479-021-04454-w

Almutairi, K., Almutairi, M. S., Harb, K. M., & Marey, O. (2023). A thorough investigation of renewable energy development strategies through integrated approach: A case study. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45(1), 708–726. https://doi.org/10.1080/15567036.2023.2169786

Altay, B. C., Celik, E., Okumus, A., Balin, A., & Gul, M. (2023). An integrated interval type-2 fuzzy BWM-MARCOS model for location selection of e-scooter sharing stations: The case of a university campus. Engineering Applications of Artificial Intelligence, 122, Article 106095. https://doi.org/10.1016/j.engappai.2023.106095

Aycin, E., Kayapinar Kaya, S., & Ecer, F. (2022). An IT2FBWM model to highlight the significance of factors utilized in determining pandemic hospital site selection. In Studies in Fuzziness and Soft Computing: vol. 420. Real life applications of multiple criteria decision making techniques in fuzzy domain (pp. 145–162). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-4929-6_7

Badri Ahmadi, B., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126, 99–106. https://doi.org/10.1016/j.resconrec.2017.07.020

Bonab, S. R., Haseli, G., Rajabzadeh, H., Ghoushchi, S. J., Hajiaghaei-Keshteli, M., & Tomaskova, H. (2023). Sustainable resilient supplier selection for IoT implementation based on the integrated BWM and TRUST under spherical fuzzy sets. Decision Making: Applications in Management and Engineering, 6(1), 153–185. https://doi.org/10.31181/dmame12012023b

Bongo, M. F., & Seva, R. R. (2023). Evaluating the performance-shaping factors of air traffic controllers using fuzzy DEMATEL and fuzzy BWM approach. Aerospace, 10(3), Article 252. https://doi.org/10.3390/aerospace10030252

Chang, J. P., Chen, Z. S., Wang, X. J., Martínez, L., Pedrycz, W., & Skibniewski, M. J. (2023). Requirement-driven sustainable supplier selection: Creating an integrated perspective with stakeholders’ interests and the wisdom of expert crowds. Computers & Industrial Engineering, 175, Article 108903. https://doi.org/10.1016/j.cie.2022.108903

Chauhan, A., Jakhar, S. K., & Mangla, S. K. (2022). Socio-technological framework for selecting suppliers of pharmaceuticals in a pandemic environment. Journal of Enterprise Information Management, 35(6), 1570–1591. https://doi.org/10.1108/JEIM-02-2021-0081

Chen, Z. H., Wan, S. P., & Dong, J. Y. (2022). An efficiency-based interval type-2 fuzzy multi-criteria group decision making for makeshift hospital selection. Applied Soft Computing, 115, Article 108243. https://doi.org/10.1016/j.asoc.2021.108243

Chen, Z. H., Wan, S. P., & Dong, J. Y. (2023). An integrated interval-valued intuitionistic fuzzy technique for resumption risk assessment amid COVID-19 prevention. Information Sciences, 619, 695–721. https://doi.org/10.1016/j.ins.2022.11.028

Darvazeh, S. S., Mooseloo, F. M., Vandchali, H. R., Tomaskova, H., & Tirkolaee, E. B. (2022). An integrated multi-criteria decision-making approach to optimize the number of leagile-sustainable suppliers in supply chains. Environmental Science and Pollution Research, 29(44), 66979–67001. https://doi.org/10.1007/s11356-022-20214-0

Dehshiri, S. J. H., Amiri, M., Olfat, L., & Pishvaee, M. S. (2023). A robust fuzzy stochastic multi-objective model for stone paper closed-loop supply chain design considering the flexibility of soft constraints based on Me measure. Applied Soft Computing, 134, Article 109944. https://doi.org/10.1016/j.asoc.2022.109944

Do, T. T. H., Ly, T. B. T., Hoang, N. T., & Tran, V. T. (2023). A new integrated circular economy index and a combined method for optimization of wood production chain considering carbon neutrality. Chemosphere, 311(Part 2), Article 137029. https://doi.org/10.1016/j.chemosphere.2022.137029

Ecer, F., & Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model. Journal of Cleaner Production, 266, Article 121981. https://doi.org/10.1016/j.jclepro.2020.121981

Ecer, F. (2021). Sustainability assessment of existing onshore wind plants in the context of triple bottom line: A best-worst method (BWM) based MCDM framework. Environmental Science and Pollution Research, 28, 19677–19693. https://doi.org/10.1007/s11356-020-11940-4

Ecer, F., Murat, T., Dinçer, H., & Yüksel, S. (2024). A fuzzy BWM and MARCOS integrated framework with Heronian function for evaluating cryptocurrency exchanges: a case study of Türkiye. Financial Innovation, 10(1), Article 31. https://doi.org/10.1186/s40854-023-00543-w

Elsevier. (2020). Content coverage guide. Retrieved January 8, 2023, from https://www.elsevier.com/solutions/scopus/how-scopus-works/content

Eskandari, M., Hamid, M., Masoudian, M., & Rabbani, M. (2022). An integrated lean production-sustainability framework for evaluation and improvement of the performance of pharmaceutical factory. Journal of Cleaner Production, 376, Article 134132. https://doi.org/10.1016/j.jclepro.2022.134132

Fard, M. B., Hamidi, D., Ebadi, M., Alavi, J., & Mckay, G. (2022). Optimum landfill site selection by a hybrid multi-criteria and multi-Agent decision-making method in a temperate and humid climate: BWM-GIS-FAHP-GT. Sustainable Cities and Society, 79, Article 103641. https://doi.org/10.1016/j.scs.2021.103641

Fazeli, H. R., & Peng, Q. (2023). Integrated approaches of BWM-QFD and FUCOM-QFD for improving weighting solution of design matrix. Journal of Intelligent Manufacturing, 34(3), 1003–1020. https://doi.org/10.1007/s10845-021-01832-w

Ferreira, F. A., & Santos, S. P. (2021). Two decades on the MACBETH approach: A bibliometric analysis. Annals of Operations Research, 296(1), 901–925. https://doi.org/10.1007/s10479-018-3083-9

Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23–31. https://doi.org/10.1016/j.knosys.2017.01.010

Guo, Y. M., Huang, Z. L., Guo, J., Li, H., Guo, X. R., & Nkeli, M. J. (2019). Bibliometric analysis on smart cities research. Sustainability, 11(13), Article 3606. https://doi.org/10.3390/su11133606

Gupta, H. (2018). Evaluating service quality of airline industry using hybrid best worst method and VIKOR. Journal of Air Transport Management, 68, 35–47. https://doi.org/10.1016/j.jairtraman.2017.06.001

Gupta, H., & Barua, M. K. (2016). Identifying enablers of technological innovation for Indian MSMEs using best–worst multi criteria decision making method. Technological Forecasting and Social Change, 107, 69–79. https://doi.org/10.1016/j.techfore.2016.03.028

Gupta, H., & Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 152, 242–258. https://doi.org/10.1016/j.jclepro.2017.03.125

Hafezalkotob, A., Hafezalkotob, A., Liao, H., & Herrera, F. (2019). An overview of MULTIMOORA for multi-criteria decision-making: Theory, developments, applications, and challenges. Information Fusion, 51, 145–177. https://doi.org/10.1016/j.inffus.2018.12.002

Hashemkhani Zolfani, S., Ecer, F., Pamučar, D., & Raslanas, S. (2020). Neighborhood selection for a newcomer via a novel BWM-based revised MAIRCA integrated model: A case from the Coquimbo-La Serena conurbation, Chile. International Journal of Strategic Property Management, 24(2), 102–118. https://doi.org/10.3846/ijspm.2020.11543

Hashemkhani Zolfani, S., Bazrafshan, R., Ecer, F., & Karamaşa, Ç. (2022). The suitability-feasibility-acceptability strategy integrated with Bayesian BWM-MARCOS methods to determine the optimal lithium battery plant located in South America. Mathematics, 10(14), Article 2401. https://doi.org/10.3390/math10142401

He, J., Wu, Y., Yong, X., Ke, Y., Tan, Q., & Liu, F. (2022). Lifecycle risk assessment on the sustainable development of upgrading energy projects using abandoned mines: An ISM-BWM method. Sustainable Energy Technologies and Assessments, 54, Article 102833. https://doi.org/10.1016/j.seta.2022.102833

Hsu, H. Y., Hwang, M. H., & Tsou, P. H. (2023). Applications of BWM and GRA for evaluating the risk of picking and material-handling accidents in warehouse facilities. Applied Sciences, 13(3), Article 1263. https://doi.org/10.3390/app13031263

Huang, G., Xiao, L., Pedrycz, W., Pamucar, D., Zhang, G., & Martínez, L. (2022). Design alternative assessment and selection: A novel Z-cloud rough number-based BWM-MABAC model. Information Sciences, 603, 149–189. https://doi.org/10.1016/j.ins.2022.04.040

Jain, R., Rana, K. B., & Meena, M. L. (2023). An integrated multi-criteria decision-making approach for identifying the risk level of musculoskeletal disorders among handheld device users. Soft Computing, 27, 3283–3293. https://doi.org/10.1007/s00500-021-05592-w

Karakuş, C. B. (2023). Groundwater potential assessment based on GIS-based Best–Worst Method (BWM) and Step-Wise Weight Assessment Ratio Analysis (SWARA) Method. Environmental Science and Pollution Research, 30(11), 31851–31880. https://doi.org/10.1007/s11356-022-24425-3

Karbassi Yazdi, A., Mehdiabadi, A., Wanke, P. F., Monajemzadeh, N., Correa, H. L., & Tan, Y. (2023). Developing supply chain resilience: A robust multi-criteria decision analysis method for transportation service provider selection under uncertainty. International Journal of Management Science and Engineering Management, 18(1), 51–64. https://doi.org/10.1080/17509653.2022.2098543

Koca, G., & Yıldırım, S. (2021). Bibliometric analysis of DEMATEL method. Decision Making: Applications in Management and Engineering, 4(1), 85–103. https://doi.org/10.31181/dmame2104085g

Koohathongsumrit, N., & Chankham, W. (2023). Route selection in multimodal supply chains: A fuzzy risk assessment model-BWM-MARCOS framework. Applied Soft Computing, 137, Article 110167. https://doi.org/10.1016/j.asoc.2023.110167

Koppiahraj, K., Bathrinath, S., Venkatesh, V. G., Mani, V., & Shi, Y. (2023). Optimal sustainability assessment method selection: A practitioner perspective. Annals of Operations Research, 324, 629–662. https://doi.org/10.1007/s10479-021-03946-z

Kumar, S., Patnaik, L., Shafi, S. M., Venkatesh, V. S. S., & Maity, S. R. (2023). Wear parameter optimization for CrN/TiAlSiN coating using novel BWM integrated TODIM decision-making approach. International Journal on Interactive Design and Manufacturing (IJIDeM), 17(2), 579–601. https://doi.org/10.1007/s12008-022-00902-4

Kusi-Sarpong, S., Gupta, H., & Sarkis, J. (2019). A supply chain sustainability innovation framework and evaluation methodology. International Journal of Production Research, 57(7), 1990–2008. https://doi.org/10.1080/00207543.2018.1518607

Lampe, H. W., & Hilgers, D. (2015). Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA. European Journal of Operational Research, 240(1), 1–21. https://doi.org/10.1016/j.ejor.2014.04.041

Li, H., & Yazdi, M. (2022). Advanced decision-making Neutrosophic fuzzy evidence-based Best–Worst Method. In Studies in systems, decision and control: vol. 211. Advanced decision-making methods and applications in system safety and reliability problems: Approaches, case studies, multi-criteria decision-making, multi-objective decision-making, fuzzy risk-based models (pp. 153–184). Springer International Publishing. https://doi.org/10.1007/978-3-031-07430-1_9

Li, H., Guo, J. Y., Yazdi, M., Nedjati, A., & Adesina, K. A. (2021). Supportive emergency decision-making model towards sustainable development with fuzzy expert system. Neural Computing and Applications, 33(22), 15619–15637. https://doi.org/10.1007/s00521-021-06183-4

Liang, D., Tang, W., & Fu, Y. (2023). Sustainable modern agricultural technology assessment by a multistakeholder transdisciplinary approach. IEEE Transactions on Engineering Management, 70(3), 1061–1075. https://doi.org/10.1109/TEM.2021.3097333

Liang, Y., Ju, Y., Dong, P., Martínez, L., Zeng, X. J., Gonzalez, E. D. S., Giannakis, M., Dong, J., & Wang, A. (2022). Sustainable evaluation of energy storage technologies for wind power generation: A multistage decision support framework under multi-granular unbalanced hesitant fuzzy linguistic environment. Applied Soft Computing, 131, Article 109768. https://doi.org/10.1016/j.asoc.2022.109768

Liao, H., Wen, Z., & Liu, L. (2019). Integrating BWM and ARAS under hesitant linguistic environment for digital supply chain finance supplier section. Technological and Economic Development of Economy, 25(6), 1188–1212. https://doi.org/10.3846/tede.2019.10716

Liu, R., Liu, Z., Liu, H. C., & Shi, H. (2021). An improved alternative queuing method for occupational health and safety risk assessment and its application to construction excavation. Automation in Construction, 126, Article 103672. https://doi.org/10.1016/j.autcon.2021.103672

Liu, P., Wang, X., Wang, P., Wang, F., & Teng, F. (2022). Sustainable medical supplier selection based on multi-granularity probabilistic linguistic term sets. Technological and Economic Development of Economy, 28(2), 381–418. https://doi.org/10.3846/tede.2022.15940

Liu, Y., & Tahera, K. (2023). A fuzzy decision-making approach for testing activity prioritisation and its application in an engine company. Applied Soft Computing, Article 110367. https://doi.org/10.1016/j.asoc.2023.110367

Liu, Z., Zhao, Y., & Liu, P. (2023). An integrated FMEA framework considering expert reliability for classification and its application in aircraft power supply system. Engineering Applications of Artificial Intelligence, 123, Article 106319. https://doi.org/10.1016/j.engappai.2023.106319

Lo, H.-W., Liou, J. J., Wang, H.-S., & Tsai, Y.-S. (2018). An integrated model for solving problems in green supplier selection and order allocation. Journal of Cleaner Production, 190, 339–352. https://doi.org/10.1016/j.jclepro.2018.04.105

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, 87, 205–225. https://doi.org/10.1016/j.omega.2019.01.009

Mohammed, A., Zubairu, N., Yazdani, M., Diabat, A., & Li, X. (2023). Resilient supply chain network design without lagging sustainability responsibilities. Applied Soft Computing, 140, Article 110225. https://doi.org/10.1016/j.asoc.2023.110225

Moktadir, M. A., Ali, S. M., Kusi-Sarpong, S., & Shaikh, M. A. A. (2018). Assessing challenges for implementing Industry 4.0: Implications for process safety and environmental protection. Process Safety and Environmental Protection, 117, 730–741. https://doi.org/10.1016/j.psep.2018.04.020

Nasiri Khiavi, A., Vafakhah, M., & Sadeghi, S. H. (2023). Flood-based critical sub-watershed mapping: Comparative application of multi-criteria decision making methods and hydrological modeling approach. Stochastic Environmental Research and Risk Assessment, 37, 2757–2775. https://doi.org/10.21203/rs.3.rs-1711435/v1

Navaei, J., Sardar, S., & Saati, S. (2023). How to implemented Knowledge management in supply chain management Best-Worst with D-number. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3253785

Nghiem, T. B. H., & Chu, T. C. (2022). Evaluating lean facility layout designs using a BWM-based fuzzy ELECTRE I method. Axioms, 11(9), Article 447. https://doi.org/10.3390/axioms11090447

Ogundoyin, S. O., & Kamil, I. A. (2023). An integrated Fuzzy-BWM, Fuzzy-LBWA and V-Fuzzy-CoCoSo-LD model for gateway selection in fog-bolstered Internet of Things. Applied Soft Computing, 143. Article 110393. https://doi.org/10.1016/j.asoc.2023.110393

Pamučar, D., Petrović, I., & Ćirović, G. (2018). Modification of the Best–Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers. Expert Systems with Applications, 91, 89–106. https://doi.org/10.1016/j.eswa.2017.08.042

Polat, E. G., Yücesan, M., & Gül, M. (2023). A comparative framework for criticality assessment of strategic raw materials in Turkey. Resources Policy, 82, Article 103511. https://doi.org/10.1016/j.resourpol.2023.103511

Rahimi, S., Hafezalkotob, A., Monavari, S. M., Hafezalkotob, A., & Rahimi, R. (2020). Sustainable landfill site selection for municipal solid waste based on a hybrid decision-making approach: Fuzzy group BWM-MULTIMOORA-GIS. Journal of Cleaner Production, 248, Article 119186. https://doi.org/10.1016/j.jclepro.2019.119186

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., 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

Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications, 42(23), 9152–9164. https://doi.org/10.1016/j.eswa.2015.07.073

Riahi, S., Bahroudi, A., Abedi, M., Lentz, D. R., & Aslani, S. (2023). Application of data-driven multi-index overlay and BWM-MOORA MCDM methods in mineral prospectivity mapping of porphyry Cu mineralization. Journal of Applied Geophysics, 213, Article 105025. https://doi.org/10.1016/j.jappgeo.2023.105025

Sahraei, R., Kanani‐Sadat, Y., Homayouni, S., Safari, A., Oubennaceur, K., & Chokmani, K. (2023). A novel hybrid GIS‐based multi‐criteria decision‐making approach for flood susceptibility analysis in large ungauged watersheds. Journal of Flood Risk Management, 16(2), Article e12879. https://doi.org/10.1111/jfr3.12879

Shaw, K., Lahri, V., Shankar, R., & Ishizaka, A. (2023). Joint multi-item multi-supplier sustainable lot-sizing model applying combined BWM, TOPSIS, possibilistic programming, and ϵ-constraint method. IEEE Transactions on Engineering Management, 1–18. https://doi.org/10.1109/TEM.2022.3230752

Sudha, S., & Martin, N. (2022, November). Comparison of plithogenic and neutrosophic approaches in decision making via best–worst method. AIP Conference Proceedings, 2516(1), 200017. https://doi.org/10.1063/5.0108499

Tanrıverdi, G., Ecer, F., & Durak, M. Ş. (2022). Exploring factors affecting airport selection during the COVID-19 pandemic from air cargo carriers’ perspective through the triangular fuzzy Dombi-Bonferroni BWM methodology. Journal of Air Transport Management, 105, Article 102302. https://doi.org/10.1016/j.jairtraman.2022.102302

Tavakoli Haji Abadi, Y., & Avakh Darestani, S. (2023). Evaluation of sustainable supply chain risk: Evidence from the Iranian food industry. Journal of Science and Technology Policy Management, 14(1), 127–156. https://doi.org/10.1108/JSTPM-08-2020-0121

Torkayesh, A. E., Pamucar, D., Ecer, F., & Chatterjee, P. (2021). An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe. Socio-Economic Planning Sciences, 78, Article 101052. https://doi.org/10.1016/j.seps.2021.101052

Ulutaş, A., Topal, A., Pamučar, D., Stević, Ž., Karabašević, D., & Popović, G. (2022). A new integrated multi-criteria decision-making model for sustainable supplier selection based on a novel grey WISP and grey BWM methods. Sustainability, 14(24), Article 16921. https://doi.org/10.3390/su142416921

Wu, J., Liu, C., Wu, Y., Cao, M., & Liu, Y. (2022). A novel hotel selection decision support model based on the online reviews from opinion leaders by best worst method. International Journal of Computational Intelligence Systems, 15(1), Article 19. https://doi.org/10.1007/s44196-022-00073-w

Wu, Y., Yong, X., Tao, Y., Zhou, J., He, J., Chen, W., & Yang, Y. (2023). Investment monitoring key points identification model of big science research infrastructures – Fuzzy BWM-entropy-PROMETHEE II method. Socio-Economic Planning Sciences, 86, Article 101461. https://doi.org/10.1016/j.seps.2022.101461

Xian, S., Qing, K., Li, C., Luo, M., & Liu, R. (2023). Probabilistic double hierarchy linguistic Maclaurin symmetric mean-MultiCriteria Border Approximation area Comparison method for multi-criteria group decision making and its application in a selection of traditional Chinese medicine prescriptions. Artificial Intelligence in Medicine, 141, Article 102558. https://doi.org/10.1016/j.artmed.2023.102558

Xiao, L., Huang, G., Pedrycz, W., Pamucar, D., Martínez, L., & Zhang, G. (2022). A q-rung orthopair fuzzy decision-making model with new score function and best-worst method for manufacturer selection. Information Sciences, 608, 153–177. https://doi.org/10.1016/j.ins.2022.06.061

Xu, Z., Li, P., & Wei, C. (2022). Evaluation on service quality in institutional pensions based on a novel hierarchical DEMATEL method for PLTSs. Journal of Intelligent & Fuzzy Systems, 43(5), 6229–6251. https://doi.org/10.3233/JIFS-220181

Yadav, A. K., & Kumar, D. (2023). A LAG-based framework to overcome the challenges of the sustainable vaccine supply chain: An integrated BWM–MARCOS approach. Journal of Humanitarian Logistics and Supply Chain Management, 13(2), 173–198. https://doi.org/10.1108/JHLSCM-09-2021-0091

Yadav, G., Luthra, S., Jakhar, S. K., Mangla, S. K., & Rai, D. P. (2020). A framework to overcome sustainable supply chain challenges through solution measures of industry 4.0 and circular economy: An automotive case. Journal of Cleaner Production, 254, Article 120112. https://doi.org/10.1016/j.jclepro.2020.120112

Yaran Ögel, İ., Aygün Özgöz, A., & Ecer, F. (2023). Prioritizing causes and drivers of retail food waste through a fuzzy Dombi-Bonferroni operators-based best–worst approach: An emerging economy perspective. Environmental Science and Pollution Research, 30(2), 4899–4916. https://doi.org/10.1007/s11356-022-22553-4

Yu, D., Kou, G., Xu, Z., & Shi, S. (2021). Analysis of collaboration evolution in AHP research: 1982–2018. International Journal of Information Technology & Decision Making, 20(1), 7–36. https://doi.org/10.1142/S0219622020500406

Zeng, S., Gu, J., & Peng, X. (2023). Low-carbon cities comprehensive evaluation method based on Fermatean fuzzy hybrid distance measure and TOPSIS. Artificial Intelligence Review, 56, 8591–8607. https://doi.org/10.1007/s10462-022-10387-y

Zhang, X., Sun, B., Chen, X., Chu, X., & Yang, J. (2020). An approach to evaluating sustainable supply chain risk management based on BWM and linguistic value soft set theory. Journal of Intelligent & Fuzzy Systems, 39(3), 4369–4382. https://doi.org/10.3233/JIFS-200372

Zheng, C., Peng, B., Zhao, X., Wan, A., & Yue, M. (2023). A novel assessment approach based on group evidential reasoning and risk attitude. Group Decision and Negotiation, 32, 925–964. https://doi.org/10.1007/s10726-023-09830-4

Zyoud, S. H., & Fuchs-Hanusch, D. (2017). A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications, 78, 158–181. https://doi.org/10.1016/j.eswa.2017.02.016