An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions

Keyu Lu; Huchang Liao; Edmundas Kazimieras Zavadskas

doi:10.3846/tede.2021.14433

An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions

Keyu Lu Info

Huchang Liao Info

Edmundas Kazimieras Zavadskas Info

Abstract

Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM.

Keywords:

supply chain management, fuzzy sets, decision making, multi-criteria decision making (MCDM), overview

How to Cite

Lu, K., Liao, H., & Zavadskas, E. K. (2021). An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions. Technological and Economic Development of Economy, 27(2), 402-458. https://doi.org/10.3846/tede.2021.14433

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April 12, 2021

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References

Abbasianjahromi, H., Sepehri, M., & Abbasi, O. (2018). A decision-making framework for subcontractor selection in construction projects. Engineering Management Journal, 30(2), 141–152. https://doi.org/10.1080/10429247.2018.1448967"> https://doi.org/10.1080/10429247.2018.1448967

Abdel-Basset, M., Mohamed, M., & Smarandache, F. (2018). A hybrid neutrosophic group ANP-TOPSIS framework for supplier selection problems. Symmetry, 10(6), 226. https://doi.org/10.3390/sym10060226"> https://doi.org/10.3390/sym10060226

Abdullah, L., & Otheman, A. (2017). Multi-criteria decision making method based on interval type-2 fuzzy sets for supplier selection. Journal of Informatics and Mathematical Sciences, 9(1), 45–56.

Afzali, A., Rafsanjani, M. K., & Saeid, A. B. (2016). A fuzzy multi-objective linear programming model based on interval-valued intuitionistic fuzzy sets for supplier selection. International Journal of Fuzzy Systems, 18(5), 864–874. https://doi.org/10.1007/s40815-016-0201-1"> https://doi.org/10.1007/s40815-016-0201-1

Akbarzadeh, Z., Ghadikolaei, A. H. S., Madhoushi, M., & Aghajani, H. (2019). A hybrid fuzzy multicriteria decision making model based on fuzzy DEMATEL with fuzzy analytical network process and interpretative structural model for prioritizing LARG supply chain practices. International Journal of Engineering, 32(3), 413–423. https://doi.org/10.5829/ije.2019.32.03c.09"> https://doi.org/10.5829/ije.2019.32.03c.09

Amid, A., Ghodsypour, S. H., & O’Brien, C. (2009). A weighted additive fuzzy multiobjective model for the supplier selection problem under price breaks in a supply Chain. International Journal of Production Economics, 131(1), 139–145. https://doi.org/10.1016/j.ijpe.2007.02.040"> https://doi.org/10.1016/j.ijpe.2007.02.040

Amid, A., Ghodsypour, S. H., & O’Brien, C. (2011). A weighted max-min model for fuzzy multiobjective supplier selection in a supply chain. International Journal of Production Economics, 121(2), 323–332. https://doi.org/10.1016/j.ijpe.2010.04.044"> https://doi.org/10.1016/j.ijpe.2010.04.044

Amin, S. H., & Zhang, G. Q. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems with Applications, 39(8), 6782– 6791. https://doi.org/10.1016/j.eswa.2011.12.056"> https://doi.org/10.1016/j.eswa.2011.12.056

Amin, S. H., & Zhang, G. Q. (2013). A three-stage model for closed-loop supply chain configuration under uncertainty. International Journal of Production Research, 51(5), 1405–1425. https://doi.org/10.1080/00207543.2012.693643"> https://doi.org/10.1080/00207543.2012.693643

Amindoust, A. (2018). Supplier selection considering sustainability measures: an application of weight restriction fuzzy-DEA approach. RAIRO-Operations Research, 52(3), 981–1001. https://doi.org/10.1051/ro/2017033"> https://doi.org/10.1051/ro/2017033

Amindoust, A., & Saghafinia, A. (2017). Textile supplier selection in sustainable supply chain using a modular fuzzy inference system model. Journal of the Textile Institute, 108(7), 1250–1258. https://doi.org/10.1080/00405000.2016.1238130"> https://doi.org/10.1080/00405000.2016.1238130

Amiri, M., Hashemi-Tabatabaei, M., Ghahremanloo, M., Keshavarz-Ghorabaee, M., Zavadskas, E. K., & Banaitis, A. (2021). A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management. International Journal of Sustainable Development & World Ecology, 28(2), 125–142. https://doi.org/10.1080/13504509.2020.1793424"> https://doi.org/10.1080/13504509.2020.1793424

Ariafar, S., Ahmed, S., Choudhury, I. A., & Bakar, M. A. (2014). Application of fuzzy optimization to production-distribution planning in supply chain management. Mathematical Problems in Engineering, 2014, 218132. https://doi.org/10.1155/2014/218132"> https://doi.org/10.1155/2014/218132

Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/s0165-0114(86)80034-3"> https://doi.org/10.1016/s0165-0114(86)80034-3

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"> https://doi.org/10.1016/j.eswa.2017.09.041

Balaman, S. Y., Matopoulos, A., Wright, D. G., & Scott, J. (2018). Integrated optimization of sustainable supply chains and transportation networks for multi technology bio-based production: A decision support system based on fuzzy epsilon-constraint method. Journal of Cleaner Production, 172, 2594–2617. https://doi.org/10.1016/j.jclepro.2017.11.150"> https://doi.org/10.1016/j.jclepro.2017.11.150

Bali, O., Gumus, S., & Kaya, I. (2015). A multi-period decision making procedure based on intuitionistic fuzzy sets for selection among third-party logistics providers. Journal of Multiple-Valued Logic and Soft Computing, 24(5–6), 547–569.

Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., & Omid, M. (2018). Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers & Operations Research, 89, 337–347. https://doi.org/10.1016/j.cor.2016.02.015"> https://doi.org/10.1016/j.cor.2016.02.015

Barbosa-Povoa, A. P., da Silva, C., & Carvalho, A. (2018). Opportunities and challenges in sustainable supply chain: An operations research perspective. European Journal of Operational Research, 268(2), 399–431. https://doi.org/10.1016/j.ejor.2017.10.036"> https://doi.org/10.1016/j.ejor.2017.10.036

Bhamu, J., & Sangwan, K. S. (2014). Lean manufacturing: literature review and research issues. International Journal of Operations & Production Management, 34(7), 876–940. https://doi.org/10.1108/ijopm-08-2012-0315"> https://doi.org/10.1108/ijopm-08-2012-0315

Boran, F. E., Genc, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36(8), 11363–11368. https://doi.org/10.1016/j.eswa.2009.03.039"> https://doi.org/10.1016/j.eswa.2009.03.039

Buyukozkan, G., & Cifci, G. (2013). An integrated QFD framework with multiple formatted and incomplete preferences: A sustainable supply chain application. Applied Soft Computing, 13(9), 3931–3941. https://doi.org/10.1016/j.asoc.2013.03.014"> https://doi.org/10.1016/j.asoc.2013.03.014

Calik, A. (2020). An integrated open-loop supply chain network configuration model with sustainable supplier selection: fuzzy multi-objective approach. SN Applied Sciences, 2(3), 405. https://doi.org/10.1007/s42452-020-2200-y"> https://doi.org/10.1007/s42452-020-2200-y

Canbulut, G., & Torun, H. (2020). Analysis of fuzzy supply chain performance based on different buyback contract configurations. Soft Computing, 24(3), 1673–1682. https://doi.org/10.1007/s00500-019-03996-3"> https://doi.org/10.1007/s00500-019-03996-3

Carrera, D. A., & Mayorga, R. V. (2008). Supply chain management: a modular fuzzy inference system approach in supplier selection for new product development. Journal of Intelligent Manufacturing, 19(1), 1–12. https://doi.org/10.1007/s10845-007-0041-9"> https://doi.org/10.1007/s10845-007-0041-9

Cavone, G., Dotoli, M., Epicoco, N., Morelli, D., & Seatzu, C. (2020). Design of modern supply chain networks using fuzzy bargaining game and data envelopment analysis. IEEE Transactions on Automation Science and Engineering, 17(3), 1221–1236. https://doi.org/10.1109/TASE.2020.2977452"> https://doi.org/10.1109/TASE.2020.2977452

Cebi, S., Ilbahar, E., & Atasoy, A. (2016). A fuzzy information axiom based method to determine the optimal location for a biomass power plant: a case study in Aegean Region of Turkey. Energy, 116, 894–907. https://doi.org/10.1016/j.energy.2016.10.024"> https://doi.org/10.1016/j.energy.2016.10.024

Centobelli, P., Cerchione, R., & Esposito, E. (2017). Environmental sustainability in the service industry of transportation and logistics service providers: Systematic literature review and research directions. Transportation Research Part D-Transport and Environment, 53, 454–470. https://doi.org/10.1016/j.trd.2017.04.032"> https://doi.org/10.1016/j.trd.2017.04.032

Chai, J. Y., Liu, J. N. K., & Xu, Z. S. (2012). A new rule-based sir approach to supplier selection under intuitionistic fuzzy environments. International Journal of Uncertainty Fuzziness and KnowledgeBased Systems, 20(3), 451–471. https://doi.org/10.1142/S0218488512500237"> https://doi.org/10.1142/S0218488512500237

Chakraborty, K., Mondal, S., & Mukherjee, K. (2018). Developing a causal model to evaluate the critical issues in reverse supply chain implementation. Benchmarking-An International Journal, 25(7), 1992–2017. https://doi.org/10.1108/BIJ-12-2016-0181"> https://doi.org/10.1108/BIJ-12-2016-0181

Chan, F. T. S., & Qi, H. J. (2002). A fuzzy basis channel-spanning performance measurement method for supply chain management. Proceedings of the Institution of Mechanical Engineers, Part B-Journal of Engineering Manufacture, 216(8), 1155–1167. https://doi.org/10.1243/095440502760272421"> https://doi.org/10.1243/095440502760272421

Chang, K. H. (2019). A novel supplier selection method that integrates the intuitionistic fuzzy weighted averaging method and a soft set with imprecise data. Annals of Operations Research, 272(1–2), 139–157. https://doi.org/10.1007/s10479-017-2718-6"> https://doi.org/10.1007/s10479-017-2718-6

Charles, V., Gupta, S., & Ali, I. (2019). A fuzzy goal programming approach for solving multi-objective supply chain network problems with pareto-distributed random variables. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 27(4), 559–593. https://doi.org/10.1142/S0218488519500259"> https://doi.org/10.1142/S0218488519500259

Chatterjee, K., & Kar, S. (2016). Multi-criteria analysis of supply chain risk management using interval valued fuzzy TOPSIS. Opsearch, 24(2), 765–791. https://doi.org/10.1007/s12597-015-0241-6"> https://doi.org/10.1007/s12597-015-0241-6

Chatterjee, K., & Kar, S. (2018). Supplier selection in telecom supply chain management: a fuzzy-rasch based COPRAS-G method. Technological and Economic Development of Economy, 53(3), 474–499. https://doi.org/10.3846/20294913.2017.1295289"> https://doi.org/10.3846/20294913.2017.1295289

Chatterjee, K., Pamucar, D., & Zavadskas, E. K. (2018). Evaluating the performance of suppliers based on using the R’AMATEL-MAIRCA method for green supply chain implementation in electronics industry. Journal of Cleaner Production, 184, 101–129. https://doi.org/10.1016/j.jclepro.2018.02.186"> https://doi.org/10.1016/j.jclepro.2018.02.186

Chavoshlou, A. S., Khamseh, A. A., & Naderi, B. (2019). An optimization model of three-player payoff based on fuzzy game theory in green supply chain. Computers & Industrial Engineering, 128, 782–794. https://doi.org/10.1016/j.cie.2018.12.057"> https://doi.org/10.1016/j.cie.2018.12.057

Chen, C. M. (2009). A fuzzy-based decision-support model for rebuy procurement. International Journal of Production Economics, 122(2), 714–724. https://doi.org/10.1016/j.ijpe.2009.06.037"> https://doi.org/10.1016/j.ijpe.2009.06.037

Chen, C. H. (2019). A new multi-criteria assessment model combining GRA techniques with intuitionistic fuzzy entropy-based TOPSIS method for sustainable building materials supplier selection. Sustainability, 11(8), 2265. https://doi.org/10.3390/su11082265"> https://doi.org/10.3390/su11082265

Chen, C. T., & Huang, S. F. (2006). Order-fulfillment ability analysis in the supply-chain system with fuzzy operation times. International Journal of Production Economics, 101(1), 185–193. https://doi.org/10.1016/j.ijpe.2005.05.003"> https://doi.org/10.1016/j.ijpe.2005.05.003

Chen, C. L., & Lee, W. C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28(6–7), 1131–1144. https://doi.org/10.1016/j.compchemeng.2003.09.014"> https://doi.org/10.1016/j.compchemeng.2003.09.014

Chen, C. T., Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289–301. https://doi.org/10.1016/j.ijpe.2005.03.009"> https://doi.org/10.1016/j.ijpe.2005.03.009

Chen, S. P. (2011). A membership function approach to lot size re-order point inventory problems in fuzzy environments. International Journal of Production Research, 49(13), 3855–3871. https://doi.org/10.1080/00207543.2010.495956"> https://doi.org/10.1080/00207543.2010.495956

Chen, S. P., & Chang, P. C. (2006). A mathematical programming approach to supply chain models with fuzzy parameters. Engineering Optimization, 38(6), 647–669. https://doi.org/10.1080/03052150600716116"> https://doi.org/10.1080/03052150600716116

Chen, S. P., & Cheng, B. H. (2014). Optimal echelon stock policies for multi-stage supply chains in fuzzy environments. International Journal of Production Research, 52(11), 3431–3449. https://doi.org/10.1080/00207543.2014.885145"> https://doi.org/10.1080/00207543.2014.885145

Chen, Z. H., Ming, X. G., Zhou, T. T., & Chang, Y. (2020). Sustainable supplier selection for smart supply chain considering internal and external uncertainty: An integrated rough-fuzzy approach. Applied Soft Computing, 87, 106004. https://doi.org/10.1016/j.asoc.2019.106004"> https://doi.org/10.1016/j.asoc.2019.106004

Chiu, M. C., & Teng, L. W. (2013). Sustainable product and supply chain design decisions under uncertainties. International Journal of Precision Engineering and Manufacturing, 14(11), 1953–1960. https://doi.org/10.1007/s12541-013-0265-x"> https://doi.org/10.1007/s12541-013-0265-x

Chou, C. C. (2010). An integrated quantitative and qualitative FMCDM model for location choices. Soft Computing, 14(7), 757–771. https://doi.org/10.1007/s00500-009-0463-8"> https://doi.org/10.1007/s00500-009-0463-8

Chou, S. Y., & Chang, Y. H. (2008). A decision support system for supplier selection based on a strategyaligned fuzzy SMART approach. Expert Systems with Applications, 34(4), 2241–2253. https://doi.org/10.1016/j.eswa.2007.03.001"> https://doi.org/10.1016/j.eswa.2007.03.001

Chou, S. Y., Chang, Y. H., & Shen, C. Y. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189(1), 132–145. https://doi.org/10.1016/j.ejor.2007.05.006"> https://doi.org/10.1016/j.ejor.2007.05.006

Cifci, G., & Buyukozkan, G. (2011). A fuzzy MCDM approach to evaluate green suppliers. International Journal of Computational Intelligence Systems, 4(5), 894–909. https://doi.org/10.1080/18756891.2011.9727840"> https://doi.org/10.1080/18756891.2011.9727840

Cigolini, R., & Rossi, T. (2008). Evaluating supply chain integration: a case study using fuzzy logic. Production Planning & Control, 19(3), 242–255. https://doi.org/10.1080/09537280801916249"> https://doi.org/10.1080/09537280801916249

Dahooie, J. H., Babgohari, A. Z., Meidutė-Kavaliauskienė, I., & Govindan, K. (2020). Prioritising sustainable supply chain management practices by their impact on multiple interacting barriers. International Journal of Sustainable Development & World Ecology, 1–24. https://doi.org/10.1080/13504509.2020.1795004"> https://doi.org/10.1080/13504509.2020.1795004

Dai, L. N., & Bai, S. Z. (2020). An approach to selection of agricultural product supplier using Pythagorean fuzzy sets. Mathematical Problems in Engineering, 2020, 1816028. https://doi.org/10.1155/2020/1816028"> https://doi.org/10.1155/2020/1816028

Dai, Z., & Dai, H. M. (2016). Bi-objective closed-loop supply chain network design with risks in a fuzzy environment. Journal of Industrial and Production Engineering, 33(3), 169–180. https://doi.org/10.1080/21681015.2015.1126655"> https://doi.org/10.1080/21681015.2015.1126655

Dalman, H., & Sivri, M. (2017). Multi-objective solid transportation problem in uncertain environment. Iranian Journal of Science and Technology Transaction A-Science, 41(A2), 505–514. https://doi.org/10.1007/s40995-017-0254-5"> https://doi.org/10.1007/s40995-017-0254-5

Darbari, J. D., Kannan, D., Agarwal, V., & Jha, P. C. (2019). Fuzzy criteria programming approach for optimising the TBL performance of closed loop supply chain network design problem. Annals of Operations Research, 273(1–2), 693–738. https://doi.org/10.1007/s10479-017-2701-2"> https://doi.org/10.1007/s10479-017-2701-2

Das, A., Bera, U. K., & Maiti, M. (2016). A breakable multi-item multi stage solid transportation problem under budget with Gaussian type-2 fuzzy parameters. Applied Intelligence, 45(3), 923–951. https://doi.org/10.1007/s10489-016-0794-y"> https://doi.org/10.1007/s10489-016-0794-y

Debnath, A., Roy, J., Kar, S., Zavadskas, E. K., & Antucheviciene, J. (2017). A hybrid MCDM approach for strategic project portfolio selection of agro by-products. Sustainability, 9(8), 1302. https://doi.org/10.3390/su9081302"> https://doi.org/10.3390/su9081302

Deng, H. P., Luo, F., & Wibowo, S. (2018). Multi-criteria group decision making for green supply chain management under uncertainty. Sustainability, 10(9), 3150. https://doi.org/10.3390/su10093150"> https://doi.org/10.3390/su10093150

Deshpande, U., Gupta, A., & Basu, A. (2004). Task assignment with imprecise information for real-time operation in a supply chain. Applied Soft Computing, 5(1), 101–117. https://doi.org/10.1016/j.asoc.2004.06.001"> https://doi.org/10.1016/j.asoc.2004.06.001

Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2016). Warehouse location selection by fuzzy multicriteria decision making methodologies based on subjective and objective criteria. International Journal of Management Science and Engineering Management, 11(4), 262–278. https://doi.org/10.1080/17509653.2015.1086964"> https://doi.org/10.1080/17509653.2015.1086964

dos Santos, B. M., Neto, C. R. P., Ferreira, A. R., Bueno, W. P., Soares, M., Blesz, A. E., Borchardt, M., & Godoy, L. P. (2017). Performance of green suppliers in supply chain management. Interciencia, 42(12), 805–811.

Dou, Y. J., Zhu, Q. H., & Sarkis, J. (2015). Integrating strategic carbon management into formal evaluation of environmental supplier development programs. Business Strategy and the Environment, 24(8), 873–891. https://doi.org/10.1002/bse.1851"> https://doi.org/10.1002/bse.1851

Duric, G., Todorovic, G.., Dordevic, A., & Tisma, A. B. (2019). A new fuzzy risk management model for production supply chain economic and social sustainability. Economic Research-Ekonomska Istraživanja, 32(1), 1697–1715. https://doi.org/10.1080/1331677X.2019.1638287"> https://doi.org/10.1080/1331677X.2019.1638287

El-Baz, M. A. (2011). Fuzzy performance measurement of a supply chain in manufacturing companies. Expert Systems with Applications, 38(6), 6681–6688. https://doi.org/10.1016/j.eswa.2010.11.067"> https://doi.org/10.1016/j.eswa.2010.11.067

Ertay, T., Kahveci, A., & Tabanli, R. M. (2011). An integrated multi-criteria group decision-making approach to efficient supplier selection and clustering using fuzzy preference relations. International Journal of Computer Integrated Manufacturing, 24(12), 1152–167. https://doi.org/10.1080/0951192X.2011.615342"> https://doi.org/10.1080/0951192X.2011.615342

Ewbank, H., Roveda, J. A. F., Roveda, S. R. M. M., Ribeiro, A. I., Bressane, A., Hadi-Vencheh, A., & Wanke, P. (2020). Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques. Journal of Enterprise Information Management, 33(5), 1059–1076. https://doi.org/10.1108/JEIM-09-2019-0289"> https://doi.org/10.1108/JEIM-09-2019-0289

Fahimnia, B., Farahani, R. Z., Marian, R., & Luong, L. (2013). A review and critique on integrated production-distribution planning models and techniques. Journal of Manufacturing Systems, 32(1), 1–19. https://doi.org/10.1016/j.jmsy.2012.07.005"> https://doi.org/10.1016/j.jmsy.2012.07.005

Fei, L. G., Deng, Y., & Hu, Y. (2019a). DS-VIKOR: A new multi-criteria decision-making method for supplier selection. International Journal of Fuzzy Systems, 21(1), 157–175. https://doi.org/10.1007/s40815-018-0543-y"> https://doi.org/10.1007/s40815-018-0543-y

Fei, L. G., Xia, J., Feng, Y. Q., & Liu, L. N. (2019b). An ELECTRE-based multiple criteria decision making method for supplier selection using Dempster-Shafer theory. IEEE Access, 7, 84701–84716. https://doi.org/10.1109/ACCESS.2019.2924945"> https://doi.org/10.1109/ACCESS.2019.2924945

Figueroa-Garcia, J. C., Kalenatic, D., & Lopez-Bello, C. A. (2012). Multi-period mixed production planning with uncertain demands: fuzzy and interval fuzzy sets approach. Fuzzy Sets and Systems, 206, 21–38. https://doi.org/10.1016/j.fss.2012.03.005"> https://doi.org/10.1016/j.fss.2012.03.005

Foroozesh, N., Tavakkoli-Moghaddam, R., & Mousavi, S. M. (2018a). A novel group decision model based on mean-variance-skewness concepts and interval-valued fuzzy sets for a selection problem of the sustainable warehouse location under uncertainty. Neural Computing & Applications, 30(11), 3277–3293. https://doi.org/10.1007/s00521-017-2885-z"> https://doi.org/10.1007/s00521-017-2885-z

Foroozesh, N., Tavakkoli-Moghaddam, R., Mousavi, S. M., & Vahdani, B. (2017). Dispatching rule evaluation in fflexible manufacturing systems by a new fuzzy decision model with possibilisticstatistical uncertainties. Arabian Journal for Science and Engineering, 42(7), 2947–2960. https://doi.org/10.1007/s13369-017-2448-8"> https://doi.org/10.1007/s13369-017-2448-8

Foroozesh, N., Tavakkoli-Moghaddam, R., & Mousavi, S. M. (2018b). Sustainable-supplier selection for manufacturing services: a failure mode and effects analysis model based on interval-valued fuzzy group decision-making. International Journal of Advanced Manufacturing Technology, 95(9–12), 3609–3629. https://doi.org/10.1007/s00170-017-1308-8"> https://doi.org/10.1007/s00170-017-1308-8

Foroozesh, N., Tavakkoli-Moghaddam, R., Mousavi, S. M., & Vahdani, B. (2019). A new comprehensive possibilistic group decision approach for resilient supplier selection with mean-variance-skewnesskurtosis and asymmetric information under interval-valued fuzzy uncertainty. Neural Computing & Applications, 31(11), 6959–6979. https://doi.org/10.1007/s00521-018-3506-1"> https://doi.org/10.1007/s00521-018-3506-1

Garibaldi, J. M., Jaroszewski, M., & Musikasuwan, S. (2008). Nonstationary fuzzy sets. IEEE Transactions on Fuzzy Systems, 16(4), 1072–1086. https://doi.org/10.1109/tfuzz.2008.917308"> https://doi.org/10.1109/tfuzz.2008.917308

Ghoushchi, S. J., Khazaeili, M., Amini, A., & Osgooei, E. (2019). Multi-criteria sustainable supplier selection using piecewise linear value function and fuzzy best-worst method. Journal of Intelligent & Fuzzy Systems, 37(2), 2309–2325. https://doi.org/10.3233/JIFS-182609"> https://doi.org/10.3233/JIFS-182609

Giallanza, A., & Puma, G. L. (2020). Fuzzy green vehicle routing problem for designing a three echelons supply chain. Journal of Cleaner Production, 259, 120774. https://doi.org/10.1016/j.jclepro.2020.120774"> https://doi.org/10.1016/j.jclepro.2020.120774

Giannoccaro, I., Pontrandolfo, P., & Scozzi, B. (2003). A fuzzy echelon approach for inventory management in supply chains. European Journal of Operational Research, 149(1), 185–196. https://doi.org/10.1016/S0377-2217(02)00441-1"> https://doi.org/10.1016/S0377-2217(02)00441-1

Gill, A. (2009). Determining loading dock requirements in production-distribution facilities under uncertainty. Computers & Industrial Engineering, 57(1), 161–168. https://doi.org/10.1016/j.cie.2008.11.002"> https://doi.org/10.1016/j.cie.2008.11.002

Gitinavard, H., Shirazi, M. A., & Zarandi, M. H. F. (2020). Sustainable feedstocks selection and renewable products allocation: A new hybrid adaptive utility-based consensus model. Journal of Environmental Management, 264, 110428. https://doi.org/10.1016/j.jenvman.2020.110428"> https://doi.org/10.1016/j.jenvman.2020.110428

Goguen, J. A. (1967). L-fuzzy sets. Journal of Mathematical Analysis and Applications, 18(1), 145–174. https://doi.org/10.1016/0022-247x(67)90189-8"> https://doi.org/10.1016/0022-247x(67)90189-8

Goker, N., Dursun, M., & Cedolin, M. (2020). A novel IFCM integrated distance based hierarchical intuitionistic decision making procedure for agile supplier selection. Journal of Intelligent & Fuzzy Systems, 38(1), 653–662. https://doi.org/10.3233/JIFS-179438"> https://doi.org/10.3233/JIFS-179438

Govindan, K., Khodaverdi, R., & Jafarian, A. (2013). A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner Production, 47, 345–354. https://doi.org/10.1016/j.jclepro.2012.04.014"> https://doi.org/10.1016/j.jclepro.2012.04.014

Govindan, K., Khodaverdi, R., & Vafadarnikjoo, A. (2015). Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain. Expert Systems with Applications, 185(1), 146–158. https://doi.org/10.1016/j.eswa.2015.04.030"> https://doi.org/10.1016/j.eswa.2015.04.030

Grewal, D., & Levy, M. (2007). Retailing research: Past, present, and future. Journal of Retailing, 83(4), 447–464. https://doi.org/10.1016/j.jretai.2007.09.003"> https://doi.org/10.1016/j.jretai.2007.09.003

Grillo, H., Alemany, M. M. E., Ortiz, A., & Mula, J. (2018). A fuzzy order promising model with nonuniform finished goods. International Journal of Fuzzy Systems, 42(20), 7207–7220. https://doi.org/10.1007/s40815-017-0317-y"> https://doi.org/10.1007/s40815-017-0317-y

Grillo, H., Peidro, D., Alemany, M. M. E., & Mula, J. (2015). Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model. International Journal of Bio-Inspired Computation, 7(3), 157–169. https://doi.org/10.1504/IJBIC.2015.069557"> https://doi.org/10.1504/IJBIC.2015.069557

Gunduz, C., & Gunduz, G. S. (2019). Supplier selection under fuzzy environment. Tekstil Ve Konfeksiyon, 29(4), 344–352. https://doi.org/10.32710/tekstilvekonfeksiyon.551911"> https://doi.org/10.32710/tekstilvekonfeksiyon.551911

Gupta, M., & Mohanty, B. K. (2015). Multi-stage multi-objective production planning using linguistic and numeric data-a fuzzy integer programming model. Computers & Industrial Engineering, 87, 454–464. https://doi.org/10.1016/j.cie.2015.06.001"> https://doi.org/10.1016/j.cie.2015.06.001

Gupta, S., Soni, U., & Kumar, G. (2019). Green supplier selection using multi-criterion decision making under fuzzy environment: A case study in automotive industry. Computers & Industrial Engineering, 136, 663–680. https://doi.org/10.1016/j.cie.2019.07.038"> https://doi.org/10.1016/j.cie.2019.07.038

Haldar, A., Qamaruddin, U., Raut, R., Kamble, S., Kharat, M. G., & Kamble, S. J. (2017). 3PL evaluation and selection using integrated analytical modeling. Journal of Modelling in Management, 12(2), 224–242. https://doi.org/10.1108/JM2-04-2015-0016"> https://doi.org/10.1108/JM2-04-2015-0016

Handfield, R., Warsing, D., & Wu, X. M. (2009). (Q, r) inventory policies in a fuzzy uncertain supply chain environment. European Journal of Operational Research, 197(2), 609–619. https://doi.org/10.1016/j.ejor.2008.07.016"> https://doi.org/10.1016/j.ejor.2008.07.016

Hendiani, S., Liao, H. C., & Jabbour, C. J. C. (2020a). A new sustainability indicator for supply chains: theoretical and practical contribution towards sustainable operations. International Journal of Logistics-Research and Applications. https://doi.org/10.1080/13675567.2020.1761308"> https://doi.org/10.1080/13675567.2020.1761308

Hendiani, S., Mahmoudi, A., & Liao, H. C. (2020b). A multi-stage multi-criteria hierarchical decisionmaking approach for sustainable supplier selection. Applied Soft Computing, 94, 106456. https://doi.org/10.1016/j.asoc.2020.106456"> https://doi.org/10.1016/j.asoc.2020.106456

Ho, T. F., Lin, C. C., & Lin, C. L. (2020). Using fuzzy sets and Markov chain method to carry out inventory strategies with different recovery levels. Symmetry, 12(8), 1226. https://doi.org/10.3390/sym12081226"> https://doi.org/10.3390/sym12081226

Hou, Q., & Xie, L. (2019). Research on supplier evaluation in a green supply chain. Discrete Dynamics in Nature and Society, 2019, 2601301. https://doi.org/10.1155/2019/2601301"> https://doi.org/10.1155/2019/2601301

Igoulalene, I., Benyoucef, L., & Tiwari, M. K. (2015). Novel fuzzy hybrid multi-criteria group decision making approaches for the strategic supplier selection problem. Expert Systems with Applications, 42(7), 3342–3356. https://doi.org/10.1016/j.eswa.2014.12.014"> https://doi.org/10.1016/j.eswa.2014.12.014

Islam, M. S., Tseng, M. L., Karia, N., & Lee, C. H. (2018). Assessing green supply chain practices in Bangladesh using fuzzy importance and performance approach. Resources Conservation and Recycling, 131, 134–145. https://doi.org/10.1016/j.resconrec.2017.12.015"> https://doi.org/10.1016/j.resconrec.2017.12.015

Jafarian, E., Razmi, J., & Tavakkoli-Moghaddam, R. (2019). Forward and reverse flows pricing decisions for two competing supply chains with common collection centers in an intuitionistic fuzzy environment. Soft Computing, 23(17), 7865–7888. https://doi.org/10.1007/s00500-018-3418-0"> https://doi.org/10.1007/s00500-018-3418-0

Jain, V., Wadhwa, S., & Deshmukh, S. G. (2005). E-commerce and supply chains: modelling of dynamics through fuzzy enhanced high level petri net. Sadhana-Academy Proceedings in Engineering Sciences, 30, 403–429. https://doi.org/10.1007/BF02706253"> https://doi.org/10.1007/BF02706253

Jana, D. K., Pramanik, S., & Maiti, M. (2016). A parametric programming method on Gaussian type-2 fuzzy set and its application to a multilevel supply chain. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 24(3), 451–477. https://doi.org/10.1142/S0218488516500239"> https://doi.org/10.1142/S0218488516500239

Jana, D. K., Pramanik, S., & Maiti, M. (2017). Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network. Neural Computing & Applications, 28(9), 2703–2726. https://doi.org/10.1007/s00521-016-2202-2"> https://doi.org/10.1007/s00521-016-2202-2

Janssen, L., Claus, T., & Sauer, J. (2016). Literature review of deteriorating inventory models by key topics from 2012 to 2015. International Journal of Production Economics, 182, 86–112. https://doi.org/10.1016/j.ijpe.2016.08.019"> https://doi.org/10.1016/j.ijpe.2016.08.019

Jiang, W., & Huang, C. (2018). A multi-criteria decision-making model for evaluating suppliers in green SCM. International Journal of Computers Communications & Control, 13(3), 337–352. https://doi.org/10.15837/ijccc.2018.3.3283"> https://doi.org/10.15837/ijccc.2018.3.3283

Jung, H., & Jeong, S. J. (2012). Managing demand uncertainty through fuzzy inference in supply chain planning. International Journal of Production Research, 50(19), 5415–5429. https://doi.org/10.1080/00207543.2011.631606"> https://doi.org/10.1080/00207543.2011.631606

Kabak, O., & Ulengin, F. (2011). Possibilistic linear-programming approach for supply chain networking decisions. European Journal of Operational Research, 209(3), 253–264. https://doi.org/10.1016/j.ejor.2010.09.025"> https://doi.org/10.1016/j.ejor.2010.09.025

Kang, H. Y., Lee, A. H. I., & Chan, Y. C. (2019). An integrated fuzzy multi-criteria decision-making approach for evaluating business process information systems. Mathematics, 7(10), 982. https://doi.org/10.3390/math7100982"> https://doi.org/10.3390/math7100982

Kannan, D., Jabbour, A. B. L. D., & Jabbour, C. J. C. (2014). Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of Operational Research, 233(2), 432–447. https://doi.org/10.1016/j.ejor.2013.07.023"> https://doi.org/10.1016/j.ejor.2013.07.023

Ke, H., Wu, Y., Huang, H., & Chen, Z. Y. (2018). Optimal pricing decisions for a closed-loop supply chain with retail competition under fuzziness. Journal of the Operational Research Society, 69(9), 1468–1482. https://doi.org/10.1080/01605682.2017.1404184"> https://doi.org/10.1080/01605682.2017.1404184

Kefer, P., Milanovic, D. D., Misita, M., & Zunjic, A. (2016). Fuzzy multicriteria ABC supplier classification in global supply chain. Mathematical Problems in Engineering, 2016, 9139483. https://doi.org/10.1155/2016/9139483"> https://doi.org/10.1155/2016/9139483

Keshavarz Ghorabaee, M., Amiri, M., Sadaghiani, J. S., & Goodarzi, G. H. (2014). Multiple criteria group decision-making for supplier selection based on COPRAS method with interval type-2 fuzzy sets. International Journal of Advanced Manufacturing Technology, 75(5–8), 1115–1130. https://doi.org/10.1007/s00170-014-6142-7"> https://doi.org/10.1007/s00170-014-6142-7

Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., & Antucheviciene, J. (2017). Supplier evaluation and selection in fuzzy environments: a review of MADM approaches. Economic Research-Ekonomska Istraživanja, 30(1), 1073–1118. https://doi.org/10.1080/1331677x.2017.1314828"> https://doi.org/10.1080/1331677x.2017.1314828

Keshavarz Ghorabaee, M., Zavadskas, E. K., Amiri, M., & Antucheviciene, J. (2016a). A new method of assessment based on fuzzy ranking and aggregated weights (AFRAW) for MCDM problems under type-2 fuzzy environment. Economic Computation and Economic Cybernetics Studies and Research, 50(1), 39–68.

Keshavarz Ghorabaee, M., Zavadskas, E. K., Amiri, M., & Esmaeili, A. (2016b). Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets. Journal of Cleaner Production, 137, 213–229. https://doi.org/10.1016/j.jclepro.2016.07.031"> https://doi.org/10.1016/j.jclepro.2016.07.031

Khalifehzadeh, S., & Fakhrzad, M. B. (2019). A modified firefly algorithm for optimizing a multi stage supply chain network with stochastic demand and fuzzy production capacity. Computers & Industrial Engineering, 133, 42–56. https://doi.org/10.1016/j.cie.2019.04.048"> https://doi.org/10.1016/j.cie.2019.04.048

Khalili-Damghani, K., & Ghasemi, P. (2016). Uncertain centralized/decentralized production-distribution planning problem in multi-product supply chains: fuzzy mathematical optimization approaches. Industrial Engineering and Management Systems, 15(2), 156–172. https://doi.org/10.7232/iems.2016.15.2.156"> https://doi.org/10.7232/iems.2016.15.2.156

Khalili-Damghani, K., & Tavana, M. (2013). A new fuzzy network data envelopment analysis model for measuring the performance of agility in supply chains. International Journal of Advanced Manufacturing Technology, 69(1–4), 291–318. https://doi.org/10.1007/s00170-013-5021-y"> https://doi.org/10.1007/s00170-013-5021-y

Khalili-Damghani, K., Tavana, M., & Amirkhan, M. (2014). A fuzzy bi-objective mixed-integer programming method for solving supply chain network design problems under ambiguous and vague conditions. International Journal of Advanced Manufacturing Technology, 73(9–12), 1567–1595. https://doi.org/10.1007/s00170-014-5891-7"> https://doi.org/10.1007/s00170-014-5891-7

Khalilzadeh, M., Karami, A., & Hajikhani, A. (2020). The multi-objective supplier selection problem with fuzzy parameters and solving the order allocation problem with coverage. Journal of Modelling in Management, 15(3), 705–725. https://doi.org/10.1108/JM2-04-2018-0049"> https://doi.org/10.1108/JM2-04-2018-0049

Khemiri, R., Elbedoui-Maktouf, K., Grabot, B., & Zouari, B. (2017). A fuzzy multi-criteria decisionmaking approach for managing performance and risk in integrated procurement-production planning. International Journal of Production Research, 55(18), 5305–5329. https://doi.org/10.1080/00207543.2017.1308575"> https://doi.org/10.1080/00207543.2017.1308575

Ko, M. D. (2020). An intelligent, empty container dispatching system model using fuzzy set theory and genetic algorithm in the context of industry 4.0. Enterprise Information Systems, 2020, 1–24. https://doi.org/10.1080/17517575.2020.1807060"> https://doi.org/10.1080/17517575.2020.1807060

Kristianto, Y., Gunasekaran, A., Helo, P., & Hao, Y. Q. Q. (2014). A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path. Expert Systems with Applications, 41(1), 39–49. https://doi.org/10.1016/j.eswa.2013.07.009"> https://doi.org/10.1016/j.eswa.2013.07.009

Kulak, O., & Kahraman, C. (2005). Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process. Information Sciences, 170(2–4), 191–210. https://doi.org/10.1016/j.ins.2004.02.021"> https://doi.org/10.1016/j.ins.2004.02.021

Kumar, A., & Anbanandam, R. (2019). Location selection of multimodal freight terminal under STEEP sustainability. Research in Transportation Business and Management, 33, 100434. https://doi.org/10.1016/j.rtbm.2020.100434"> https://doi.org/10.1016/j.rtbm.2020.100434

Kumar, A., & Anbanandam, R. (2020). Environmentally responsible freight transport service providers’ assessment under data-driven information uncertainty. Journal of Enterprise Information Management, 34(1), 506–542. https://doi.org/10.1108/JEIM-12-2019-0403"> https://doi.org/10.1108/JEIM-12-2019-0403

Kumar, A., Mangla, S. K., Luthra, S., Rana, N. P., & Dwivedi, Y. K. (2018). Predicting changing pattern: building model for consumer decision making in digital market. Journal of Enterprise Information Management, 31(5), 674–703. https://doi.org/10.1108/JEIM-01-2018-0003"> https://doi.org/10.1108/JEIM-01-2018-0003

Kumar, A., Zavadskas, E. K., Mangla, S. K., Agrawal, V., Sharma, K., & Gupta, D. (2019). When risks need attention: adoption of green supply chain initiatives in the pharmaceutical industry. International Journal of Production Research, 57(11), 3554–3576. https://doi.org/10.1080/00207543.2018.1543969"> https://doi.org/10.1080/00207543.2018.1543969

Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection problem in a supply chain. Computers & Industrial Engineering, 46(1), 69–85. https://doi.org/10.1016/j.cie.2003.09.010"> https://doi.org/10.1016/j.cie.2003.09.010

Kumar, P., Singh, R. K., & Vaish, A. (2017). Suppliers’ green performance evaluation using fuzzy extended ELECTRE approach. Clean Technologies and Environmental Policy, 19(3), 809–821. https://doi.org/10.1007/s10098-016-1268-y"> https://doi.org/10.1007/s10098-016-1268-y

Kumar, R. S. (2018). Modelling a type-2 fuzzy inventory system considering items with imperfect quality and shortage backlogging. Sadhana-Academy Proceedings in Engineering Sciences, 43, 163. https://doi.org/10.1007/s12046-018-0920-0"> https://doi.org/10.1007/s12046-018-0920-0

Kuo, R. J., Lee, L. Y., & Hu, T. L. (2010). Developing a supplier selection system through integrating fuzzy AHP and fuzzy DEA: a case study on an auto lighting system company in Taiwan. Production Planning & Control, 21(5), 468–484. https://doi.org/10.1080/09537280903458348"> https://doi.org/10.1080/09537280903458348

Lee, C. S., Chung, C. C., Lee, H. S., Gan, G. Y., & Chou, M. T. (2016). An interval-valued fuzzy number approach for supplier selection. Journal of Marine Science and Technology-Taiwan, 24(3), 384–389. https://doi.org/10.6119/JMST-015-0521-8"> https://doi.org/10.6119/JMST-015-0521-8

Li, D. F., & Wan, S. P. (2014). A fuzzy inhomogenous multiattribute group decision making approach to solve outsourcing provider selection problems. Knowledge-Based Systems, 67, 71–89. https://doi.org/10.1016/j.knosys.2014.06.006"> https://doi.org/10.1016/j.knosys.2014.06.006

Li, F. C., Li, L., Jin, C. X., Wang, R. J., Wang, H., & Yang, L. L. (2012a). A 3PL supplier selection model based on fuzzy sets. Computers & Operations Research, 39(8), 1879–1884. https://doi.org/10.1016/j.cor.2011.06.022"> https://doi.org/10.1016/j.cor.2011.06.022

Li, M., Wu, C., Zhang, L., & You, L. N. (2015). An intuitionistic fuzzy-TODIM method to solve distributor evaluation and selection problem. International Journal of Simulation Modelling, 14(3), 511–524. https://doi.org/10.2507/IJSIMM14(3)CO12"> https://doi.org/10.2507/IJSIMM14(3)CO12

Li, Y., Liu, X. D., & Chen, Y. (2012b). Supplier selection using axiomatic fuzzy set and TOPSIS methodology in supply chain management. Fuzzy Optimization and Decision Making, 11(2), 147–176. https://doi.org/10.1007/s10700-012-9117-x"> https://doi.org/10.1007/s10700-012-9117-x

Li, Y., Liu, X. D., & Chen, Y. (2012c). Supplier evaluation and selection using axiomatic fuzzy set and DEA methodology in supply chain management. International Journal of Fuzzy Systems, 14(2), 215–225.

Liang, T. F. (2008). Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain. Computers & Industrial Engineering, 55(3), 676–694. https://doi.org/10.1016/j.cie.2008.02.008"> https://doi.org/10.1016/j.cie.2008.02.008

Liang, T. F. (2013). Imprecise multi-objective production/distribution planning decisions using possibilistic programming method. Journal of Intelligent & Fuzzy Systems, 25(1), 219–230. https://doi.org/10.3233/IFS-2012-0629"> https://doi.org/10.3233/IFS-2012-0629

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

Liao, H. C., Xu, Z. S., & Herrera, F. (2020). Applications of contemporary decision-making methods to the development of economy and technology. Technological and Economic Development of Economy, 26(3), 546–548. https://doi.org/10.3846/tede.2020.12476"> https://doi.org/10.3846/tede.2020.12476

Lin, R. J. (2013). Using fuzzy DEMATEL to evaluate the green supply chain management practices. Journal of Cleaner Production, 40, 32–39. https://doi.org/10.1016/j.jclepro.2011.06.010"> https://doi.org/10.1016/j.jclepro.2011.06.010

Liou, J. J. H., Tamošaitienė, J., Zavadskas, E. K., & Tzeng, G.-H. (2016). New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management. International Journal of Production Research, 54(1), 114–134. https://doi.org/10.1080/00207543.2015.1010747"> https://doi.org/10.1080/00207543.2015.1010747

Liu, A. J., Ji, X. H., Lu, H., & Liu, H. Y. (2019a). The selection of 3PRLs on self-service mobile recycling machine: Interval-valued pythagorean hesitant fuzzy best-worst multi-criteria group decisionmaking. Journal of Cleaner Production, 230, 734–750. https://doi.org/10.1016/j.jclepro.2019.04.257"> https://doi.org/10.1016/j.jclepro.2019.04.257

Liu, A. J., Ji, X. H., Xu, L., & Lu, H. (2019b). Research on the recycling of sharing bikes based on time dynamics series, individual regrets and group efficiency. Journal of Cleaner Production, 208, 666–687. https://doi.org/10.1016/j.jclepro.2018.10.146"> https://doi.org/10.1016/j.jclepro.2018.10.146

Liu, L. M., Cao, W. Z., Shi, B., & Tang, M. (2019c). Large-scale green supplier selection approach under a q-rung interval-valued orthopair fuzzy environment. Processes, 7(9), 573. https://doi.org/10.3390/pr7090573"> https://doi.org/10.3390/pr7090573

Liu, S. K., Gao, J., & Xu, Z. S. (2019d). Fuzzy supply chain coordination mechanism with imperfect quality items. Technological and Economic Development of Economy, 25(2), 239–257. https://doi.org/10.3846/tede.2019.6620"> https://doi.org/10.3846/tede.2019.6620

Liu, S. T., & Kao, C. (2004). Solving fuzzy transportation problems based on extension principle. European Journal of Operational Research, 153(3), 661–674. https://doi.org/10.1016/S0377-2217(02)00731-2"> https://doi.org/10.1016/S0377-2217(02)00731-2

Liu, Y. M., Zhou, P., Li, L. Y., & Zhu, F. (2020). An interactive decision-making method for third-party logistics provider selection under hybrid multi-criteria. Symmetry, 12(5), 729. https://doi.org/10.3390/sym12050729"> https://doi.org/10.3390/sym12050729

Lu, S., Su, H. Y., Xiao, L., & Zhu, L. (2015). Application of two-phase fuzzy optimization approach to multiproduct multistage integrated production planning with linguistic preference under uncertainty. Mathematical Problems in Engineering, 2015, 780830. https://doi.org/10.1155/2015/780830"> https://doi.org/10.1155/2015/780830

Mahdiraji, H. A., Beheshti, M., Hajiagha, S. H., & Zavadskas, E. K. (2018a). A fuzzy binary bi objective transportation model: Iranian steel supply network. Transport, 33(3), 810–820. https://doi.org/10.3846/transport.2018.5800"> https://doi.org/10.3846/transport.2018.5800

Mahdiraji, H. A., Hajiagha, S. H. R., Hashemi, S. S., & Zavadskas, E. K. (2018b). Bi-objective meanvariance method based on Chebyshev inequality bounds for multi-objective stochastic problems. Rairo-Operations Research, 52(4–5), 1201–1217. https://doi.org/10.1051/ro/2018018"> https://doi.org/10.1051/ro/2018018

Mahdiraji, H. A., Zavadskas, E. K., Skare, M., Kafshgar, F. Z. R., & Arab, A. (2020). Evaluating strategies for implementing industry 4.0: a hybrid expert oriented approach of BWM and interval valued intuitionistic fuzzy TODIM. Economic Research-Ekonomska Istraživanja, 33(1), 1600–1620. https://doi.org/10.1080/1331677x.2020.1753090"> https://doi.org/10.1080/1331677x.2020.1753090

Mahmoodirad, A., Niroomand, S., & Shafiee, M. (2020). A closed loop supply chain network design problem with multi-mode demand satisfaction in fuzzy environment. Journal of Intelligent & Fuzzy Systems, 39(1), 503–524. https://doi.org/10.3233/JIFS-191528"> https://doi.org/10.3233/JIFS-191528

Mahmoudi, A., Sadi-Nezhad, S., & Makui, A. (2016). An extended fuzzy VIKOR for group decisionmaking based on fuzzy distance to supplier selection. Scientia Iranica, 23(4), 1879–1892. https://doi.org/10.24200/sci.2016.3934"> https://doi.org/10.24200/sci.2016.3934

Mahnam, M., Yadollahpour, M. R., Famil-Dardashti, V., & Hejazi, S. R. (2009). Supply chain modeling in uncertain environment with bi-objective approach. Computers & Industrial Engineering, 56(4), 1535–1544. https://doi.org/10.1016/j.cie.2008.09.038"> https://doi.org/10.1016/j.cie.2008.09.038

Malviya, R. K., & Kant, R. (2016). Hybrid decision making approach to predict and measure the success possibility of green supply chain management implementation. Journal of Cleaner Production, 135, 387–409. https://doi.org/10.1016/j.jclepro.2016.06.046"> https://doi.org/10.1016/j.jclepro.2016.06.046

Malviya, R. K., Kant, R., & Gupta, A. D. (2018). Evaluation and selection of sustainable strategy for green supply chain management implementation. Business Strategy and the Environment, 27(4), 475–502. https://doi.org/10.1002/bse.2016"> https://doi.org/10.1002/bse.2016

Matawale, C. R., Datta, S., & Mahapatra, S. S. (2016). Supplier selection in agile supply chain application potential of FMLMCDM approach in comparison with fuzzy-TOPSIS and fuzzy-MOORA. Benchmarking-An International Journal, 23(7), 2027–2060. https://doi.org/10.1108/BIJ-07-2015-0067"> https://doi.org/10.1108/BIJ-07-2015-0067

Mavi, R. K., Kazemi, S., Najafabadi, A. F., & Mousaabadi, H. B. (2013). Identification and assessment of logistical factors to evaluate a green supplier using the fuzzy logic DEMATEL method. Polish Journal of Environmental Studies, 22(2), 445–455.

Meksavang, P., Shi, H., Lin, S. M., & Liu, H. C. (2019). An extended picture fuzzy VIKOR approach for sustainable supplier management and its application in the beef industry. Symmetry, 11(4), 468. https://doi.org/10.3390/sym11040468"> https://doi.org/10.3390/sym11040468

Meng, Q. Q., Liu, X. W., Song, Y., & Wang, W. Z. (2019). An extended generalized TODIM method for risk assessment of supply chain in social commerce under interval type-2 fuzzy environment. Journal of Intelligent & Fuzzy Systems, 37(6), 8551–8565. https://doi.org/10.3233/JIFS-190061"> https://doi.org/10.3233/JIFS-190061

Mezei, J., & Bjork, K. M. (2015). An economic production quantity problem with backorders and fuzzy cycle times. Journal of Intelligent & Fuzzy Systems, 28(4), 1861–1868. https://doi.org/10.3233/IFS-141472"> https://doi.org/10.3233/IFS-141472

Mi, X. M., Liao, H. C., Liao, Y., Lin, Q., Lev, B., & Al-Barakati, A. (2020). Stochastic multi-criteria acceptability analysis integrated with MULTIMOORA method and its application in green suppler selection. Technological and Economic Development of Economy, 26(3), 549–572. https://doi.org/10.3846/tede.2020.11964"> https://doi.org/10.3846/tede.2020.11964

Mirakhorli, A. (2014). Fuzzy multi-objective optimization for closed loop logistics network design in bread-producing industries. International Journal of Advanced Manufacturing Technology, 70(1–4), 349–362. https://doi.org/10.1007/s00170-013-5264-7"> https://doi.org/10.1007/s00170-013-5264-7

Mirhedayatian, S. M., Azadi, M., & Saen, R. F. (2014). A novel network data envelopment analysis model for evaluating green supply chain management. International Journal of Production Economics, 147, 544–554. https://doi.org/10.1016/j.ijpe.2013.02.009"> https://doi.org/10.1016/j.ijpe.2013.02.009

Mishra, A. R., Rani, P., Pardasani, K. R., & Mardani, A. (2019). A novel hesitant fuzzy WASPAS method for assessment of green supplier problem based on exponential information measures. Journal of Cleaner Production, 238, 117901. https://doi.org/10.1016/j.jclepro.2019.117901"> https://doi.org/10.1016/j.jclepro.2019.117901

Mohaghar, A., Fathi, M. R., & Jafarzadeh, A. H. (2013). A supplier selection method using AR-DEA and fuzzy VIKOR. International Journal of Industrial Engineering, 20(5–6), 387–400.

Mohammadi, H., Farahani, F. V., Noroozi, M., & Lashgari, A. (2017). Green supplier selection by developing a new group decision-making method under type 2 fuzzy uncertainty. International Journal of Advanced Manufacturing Technology, 93(1–4), 1443–1462. https://doi.org/10.1007/s00170-017-0458-z"> https://doi.org/10.1007/s00170-017-0458-z

Mohammed, A., Harris, I., & Govindan, K. (2019). A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation. International Journal of Production Economics, 217, 171–184. https://doi.org/10.1016/j.ijpe.2019.02.003"> https://doi.org/10.1016/j.ijpe.2019.02.003

Mohd, W. R. W., Abdullah, L., Yusoff, B., Taib, C. M. I. C., & Merigo, J. M. (2019). An integrated MCDM model based on Pythagorean fuzzy sets for green supplier development program. Malaysian Journal of Mathematical Sciences, 13, 23–37.

Mousavi, S. M., Antucheviciene, 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. https://doi.org/10.3846/transport.2019.7442"> https://doi.org/10.3846/transport.2019.7442

Mousavi, S. M., Foroozesh, N., Zavadskas, E. K., & Antucheviciene, J. (2020). A new soft computing approach for green supplier selection problem with interval type-2 trapezoidal fuzzy statistical group decision and avoidance of information loss. Soft Computing, 24(16), 12313–12327. https://doi.org/10.1007/s00500-020-04675-4"> https://doi.org/10.1007/s00500-020-04675-4

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(1), 91–109. https://doi.org/10.1080/18756891.2016.1144156"> https://doi.org/10.1080/18756891.2016.1144156

Mula, J., Peidro, D., & Poler, R. (2010). The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. International Journal of Production Economics, 128(1), 136–143. https://doi.org/10.1016/j.ijpe.2010.06.007"> https://doi.org/10.1016/j.ijpe.2010.06.007

Muneeb, S. M., Nomani, M. A., Masmoudi, M., & Adhami, A. Y. (2020). A bi-level decision-making approach for the vendor selection problem with random supply and demand. Management Decision, 58(6), 1164–1189. https://doi.org/10.1108/MD-10-2017-1017"> https://doi.org/10.1108/MD-10-2017-1017

Nakandala, D., Lau, H., & Zhang, J. J. (2016). Cost-optimization modelling for fresh food quality and transportation. Industrial Management & Data Systems, 116(3), 564–583. https://doi.org/10.1108/IMDS-04-2015-0151"> https://doi.org/10.1108/IMDS-04-2015-0151

Narayanan, A. E., Sridharan, R., & Kumar, P. N. R. (2019). Analyzing the interactions among barriers of sustainable supply chain management practices: A case study. Journal of Manufacturing Technology Management, 30(6), 937–971. https://doi.org/10.1108/JMTM-06-2017-0114"> https://doi.org/10.1108/JMTM-06-2017-0114

Nestic, S., Ljepava, N., & Aleksic, A. (2018). Stakeholder management in reverse supply chains - the ranking of reverse supply chains entities upon requirements’ fulfillment. International Journal for Quality Research, 12(4), 975–987. https://doi.org/10.18421/IJQR12.04-12"> https://doi.org/10.18421/IJQR12.04-12

Ocampo, L. A., Clark, E. E., Tanudtanud, K. V. G., Ocampo, C. O. V., Impas, C. G., Vergara, V. G., Pastoril, J., & Tordillo, J. A. S. (2015). An integrated sustainable manufacturing strategy framework using fuzzy analytic network process. Advances in Production Engineering & Management, 10(3), 125–139. https://doi.org/10.14743/apem2015.3.197"> https://doi.org/10.14743/apem2015.3.197

Olfat, L., Pishdar, M., & Ghasemzadeh, F. (2019). A type-2 fuzzy network data envelopment analysis for FMCG distributors’ performance evaluation with sustainability approach. International Journal of Industrial Engineering, 26(5), 663–687.

Onar, S. C., & Ates, N. Y. (2008). A fuzzy model for operational supply chain optimization problems. Journal of Multiple-Valued Logic and Soft Computing, 14(3–5), 355–370.

Osiro, L., Lima, 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"> https://doi.org/10.1016/j.jclepro.2018.02.197

Ou, C. W., & Chou, S. Y. (2009). International distribution center selection from a foreign market perspective using a weighted fuzzy factor rating system. Expert Systems with Applications, 36(2), 1773–1782. https://doi.org/10.1016/j.eswa.2007.12.007"> https://doi.org/10.1016/j.eswa.2007.12.007

Ozbek, A., & Yildiz, A. (2020). Digital supplier sselection for a garment business using interval type-2 fuzzy TOPSIS. Tekstil Ve Konfeksiyon, 30(1), 61–72. https://doi.org/10.32710/tekstilvekonfeksiyon.569884"> https://doi.org/10.32710/tekstilvekonfeksiyon.569884

Ozkan, B., Basligil, H., Kaya, I., & Ozkir, V. (2015). A fuzzy mixed integer linear programming model for a reverse logistics system with a real case application. Journal of Multiple-Valued Logic and Soft Computing, 25(2–3), 269–289.

Paksoy, T., Pehlivan, N. Y., & Ozceylan, E. (2012). Application of fuzzy optimization to a supply chain network design: A case study of an edible vegetable oils manufacturer. Applied Mathematical Modelling, 36(6), 2762–2776. https://doi.org/10.1016/j.apm.2011.09.060"> https://doi.org/10.1016/j.apm.2011.09.060

Pamucar, D., Chatterjee, K., & Zavadskas, E. K. (2019). Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers. Computers & Industrial Engineering, 127, 383–407. https://doi.org/10.1016/j.cie.2018.10.023"> https://doi.org/10.1016/j.cie.2018.10.023

Pandey, P., Shah, B. J., & Gajjar, H. (2017). A fuzzy goal programming approach for selecting sustainable suppliers. Benchmarking-An International Journal, 24(5), 1138–1165. https://doi.org/10.1108/BIJ-11-2015-0110"> https://doi.org/10.1108/BIJ-11-2015-0110

Pang, Q. H., Yang, T. T., Li, M. Z., & Shen, Y. (2017). A fuzzy-grey multicriteria decision making approach for green supplier selection in low-carbon supply chain. Mathematical Problems in Engineering, 2017, 9653261. https://doi.org/10.1155/2017/9653261"> https://doi.org/10.1155/2017/9653261

Pang, J. H., Zhang, Q., & Yang, G. W. (2006). Coalition and distribution in fuzzy dynamic supply chain systems. Dynamics of Continuous Discrete and Impulsive Systems-Series A-Mathematical Analysis, 13, 698–702.

Peidro, D., Mula, J., Jimenez, M., & Botella, M. D. (2010a). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205(1), 65–80. https://doi.org/10.1016/j.ejor.2009.11.031"> https://doi.org/10.1016/j.ejor.2009.11.031

Peidro, D., Mula, J., & Poler, R. (2010b). Fuzzy linear programming for supply chain planning under uncertainty. International Journal of Information Technology & Decision Making, 9(3), 373–392. https://doi.org/10.1142/S0219622010003865"> https://doi.org/10.1142/S0219622010003865

Peidro, D., Mula, J., Poler, R., & Verdegay, J. L. (2009). Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160(18), 2640–2657. https://doi.org/10.1016/j.fss.2009.02.021"> https://doi.org/10.1016/j.fss.2009.02.021

Petrovic, D. (2001). Simulation of supply chain behaviour and performance in an uncertain environment. International Journal of Production Economics, 71(1–3), 429–438. https://doi.org/10.1016/S0925-5273(00)00140-7"> https://doi.org/10.1016/S0925-5273(00)00140-7

Petrovic, D., Roy, R., & Petrovic, R. (1998). Modelling and simulation of a supply chain in an uncertain environment. European Journal of Operational Research, 59(1–3), 443–453. https://doi.org/10.1016/S0377-2217(98)00058-7"> https://doi.org/10.1016/S0377-2217(98)00058-7

Petrovic, D., Roy, R., & Petrovic, R. (1999). Supply chain modelling using fuzzy sets. International Journal of Production Economics, 109(2), 299–309. https://doi.org/10.1016/S0925-5273(98)00109-1"> https://doi.org/10.1016/S0925-5273(98)00109-1

Petrovic, D., Xie, Y., Burnham, K., & Petrovic, R. (2008). Coordinated control of distribution supply chains in the presence of fuzzy customer demand. European Journal of Operational Research, 185(1), 146–158. https://doi.org/10.1016/j.ejor.2006.12.020"> https://doi.org/10.1016/j.ejor.2006.12.020

Phochanikorn, P., & Tan, C. Q. (2019). A new extension to a multi-criteria decision-making model for sustainable supplier selection under an intuitionistic fuzzy environment. Sustainability, 11(19), 5413. https://doi.org/10.3390/su11195413"> https://doi.org/10.3390/su11195413

Pinar, A., & Boran, F. E. (2020). A q-rung orthopair fuzzy multi-criteria group decision making method for supplier selection based on a novel distance measure. International Journal of Machine Learning and Cybernetics, 11(8), 1749–1780. https://doi.org/10.1007/s13042-020-01070-1"> https://doi.org/10.1007/s13042-020-01070-1

Pirayesh, M., & Yazdi, M. M. (2010). Modeling (r, Q) policy in a two-level supply chain system with fuzzy demand. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 18(6), 819–841. https://doi.org/10.1142/S0218488510006817"> https://doi.org/10.1142/S0218488510006817

Poornikoo, M., & Qureshi, M. A. (2019). System dynamics modeling with fuzzy logic application to mitigate the bullwhip effect in supply chains. Journal of Modelling in Management, 14(3), 610–627. https://doi.org/10.1108/JM2-04-2018-0045"> https://doi.org/10.1108/JM2-04-2018-0045

Pourjavad, E., & Shahin, A. (2018a). Hybrid performance evaluation of sustainable service and manufacturing supply chain management: An integrated approach of fuzzy dematel and fuzzy inference system. Intelligent Systems in Accounting Finance & Management, 25(3), 134–147. https://doi.org/10.1002/isaf.1431"> https://doi.org/10.1002/isaf.1431

Pourjavad, E., & Shahin, A. (2018b). The application of Mamdani fuzzy inference system in evaluating green supply chain management performance. International Journal of Fuzzy Systems, 20(3), 901–912. https://doi.org/10.1007/s40815-017-0378-y"> https://doi.org/10.1007/s40815-017-0378-y

Pournader, M., Rotaru, K., Kach, A. P., & Hajiagha, S. H. R. (2016). An analytical model for systemwide and tier-specific assessment of resilience to supply chain risks. Supply Chain Management-An International Journal, 21(5), 589–609. https://doi.org/10.1108/SCM-11-2015-0430"> https://doi.org/10.1108/SCM-11-2015-0430

Pournamazi, S. R., Yaghin, R. G., & Jolai, F. (2020). Positioning push-pull boundary in a hesitant fuzzy environment. Expert Systems, 38(2), e12616. https://doi.org/10.1111/exsy.12616"> https://doi.org/10.1111/exsy.12616

Qin, J. D., Liu, X. W., & Pedrycz, W. (2017a). A multiple attribute interval type-2 fuzzy group decision making and its application to supplier selection with extended LINMAP method. Soft Computing, 21(12), 3207–3226. https://doi.org/10.1007/s00500-015-2004-y"> https://doi.org/10.1007/s00500-015-2004-y

Qin, J. D., Liu, X. W., & Pedrycz, W. (2017b). An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. European Journal of Operational Research, 258(2), 626–638. https://doi.org/10.1016/j.ejor.2016.09.059"> https://doi.org/10.1016/j.ejor.2016.09.059

Rabbani, M., & Talebi, E. (2019). A comprehensive mathematical model for designing an organ transplant supply chain network under uncertainty. International Journal of Engineering, 32(6), 835–841. https://doi.org/10.5829/ije.2019.32.06c.06"> https://doi.org/10.5829/ije.2019.32.06c.06

Rahmanzadeh, S., Pishvaee, M. S., & Rasouli, M. R. (2020). Integrated innovative product design and supply chain tactical planning within a blockchain platform. International Journal of Production Research, 58(7), 2242–2262. https://doi.org/10.1080/00207543.2019.1651947"> https://doi.org/10.1080/00207543.2019.1651947

Ramezani, M., Kimiagari, A. M., Karimi, B., & Hejazi, T. H. (2014). Closed-loop supply chain network design under a fuzzy environment. Knowledge-Based Systems, 59, 108–120. https://doi.org/10.1016/j.knosys.2014.01.016"> https://doi.org/10.1016/j.knosys.2014.01.016

Rani, P., Mishra, A. R., Krishankumar, R., Mardani, A., Cavallaro, F., Ravichandran, K. S., & Balasubramanian, K. (2020a). Hesitant fuzzy SWARA-complex proportional assessment approach for sustainable supplier selection (HF-SWARA-COPRAS). Symmetry, 12(7), 1152. https://doi.org/10.3390/sym12071152"> https://doi.org/10.3390/sym12071152

Rani, P., Mishra, A. R., Rezaei, G., Liao, H. C., & Mardani, A. (2020b). Extended Pythagorean fuzzy TOPSIS method based on similarity measure for sustainable recycling partner selection. International Journal of Fuzzy Systems, 22(2), 735–747. https://doi.org/10.1007/s40815-019-00689-9"> https://doi.org/10.1007/s40815-019-00689-9

Reimann, F., Kosmol, T., & Kaufmann, L. (2017). Responses to supplier-induced disruptions: a fuzzy-set analysis. Journal of Supply Chain Management, 53(4), 37–66. https://doi.org/10.1111/jscm.12141"> https://doi.org/10.1111/jscm.12141

Rodríguez, R. M., Martinez, L., & Herrera, F. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1), 109–119. https://doi.org/10.1109/tfuzz.2011.2170076"> https://doi.org/10.1109/tfuzz.2011.2170076

Rostamzadeh, R., Esmaeili, A., Nia, A. S., Saparauskas, J., & Keshavarz Ghorabaee, M. (2017). A fuzzy ARAS method for supply chain management performance measurement in SMEs under uncertainty. Transformations in Business & Economics, 16(2A), 319–348.

Rostamzadeh, R., Govindan, K., Esmaeili, A., & Sabaghi, M. (2015a). Application of fuzzy VIKOR for evaluation of green supply chain management practices. Ecological Indicators, 49, 188–203. https://doi.org/10.1016/j.ecolind.2014.09.045"> https://doi.org/10.1016/j.ecolind.2014.09.045

Rostamzadeh, R., Keshavarz Ghorabaee, M., Govindan, K., Esmaeili, A., & Nobar, H. B. K. (2018). Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS- CRITIC approach. Journal of Cleaner Production, 175, 651–669. https://doi.org/10.1016/j.jclepro.2017.12.071"> https://doi.org/10.1016/j.jclepro.2017.12.071

Rostamzadeh, R., Sabaghi, M., Sofian, S., & Ismail, Z. (2015b). Hybrid GA for material routing optimization in supply chain. Applied Soft Computing, 26, 107–122. https://doi.org/10.1016/j.asoc.2014.09.033"> https://doi.org/10.1016/j.asoc.2014.09.033

Rouyendegh, B. D., Yildizbasi, A., & Ustunyer, P. (2020). Intuitionistic fuzzy TOPSIS method for green supplier selection problem. Soft Computing, 24(3), 2215–2228. https://doi.org/10.1007/s00500-019-04054-8"> https://doi.org/10.1007/s00500-019-04054-8

Ruppert, T., Dorgo, G., & Abonyi, J. (2020). Fuzzy activity time-based model predictive control of openstation assembly lines. Journal of Manufacturing Systems, 54, 12–23. https://doi.org/10.1016/j.jmsy.2019.11.005"> https://doi.org/10.1016/j.jmsy.2019.11.005

Ryu, K., & Yucesan, E. (2010). A fuzzy newsvendor approach to supply chain coordination. European Journal of Operational Research, 200(2), 421–438. https://doi.org/10.1016/j.ejor.2009.01.011"> https://doi.org/10.1016/j.ejor.2009.01.011

Sahin, B., & Soylu, A. (2020). Multi-layer, multi-segment iterative optimization for maritime supply chain operations in a dynamic fuzzy environment. IEEE Access, 8, 144993–145005. https://doi.org/10.1109/ACCESS.2020.3014968"> https://doi.org/10.1109/ACCESS.2020.3014968

Sahu, A. K., Sahu, N. K., & Sahu, A. K. (2016). Application of integrated TOPSIS in ASC index: partners benchmarking perspective. Benchmarking: An International Journal, 23(3), 540–563. https://doi.org/10.1108/BIJ-03-2014-0021"> https://doi.org/10.1108/BIJ-03-2014-0021

Sahu, A. K., Narang, H. K., Rajput, M. S., Sahu, N. K., & Sahu, A. K. (2018). Performance modeling and benchmarking of green supply chain management: An integrated fuzzy approach. Benchmarking: An International Journal, 25(7), 2248–2271. https://doi.org/10.1108/BIJ-02-2017-0032"> https://doi.org/10.1108/BIJ-02-2017-0032

Salam, M. A., Ali, M., & Seny Kan, K. A. (2017). Analyzing supply chain uncertainty to deliver sustainable operational performance: symmetrical and asymmetrical modeling approaches. Sustainability, 9(12), 2217. https://doi.org/10.3390/su9122217"> https://doi.org/10.3390/su9122217

Samantra, C., Datta, S., Mishra, S., & Mahapatra, S. S. (2013). Agility appraisal for integrated supply chain using generalized trapezoidal fuzzy numbers set. International Journal of Advanced Manufacturing Technology, 68(5–8), 1491–1503. https://doi.org/10.1007/s00170-013-4937-6"> https://doi.org/10.1007/s00170-013-4937-6

Sanayei, A., Mousavi, S. F., & Yazdankhah, A. (2010). Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Systems with Applications, 37(1), 24–30. https://doi.org/10.1016/j.eswa.2009.04.063"> https://doi.org/10.1016/j.eswa.2009.04.063

Sang, S. J. (2016a). Revenue sharing contract in a multi-echelon supply chain with fuzzy demand and asymmetric information. International Journal of Computational Intelligence Systems, 9(6), 1028– 1040. https://doi.org/10.1080/18756891.2016.1256569"> https://doi.org/10.1080/18756891.2016.1256569

Sang, S. J. (2016b). The coordinating contracts of supply chain in a fuzzy decision environment. SpringerPlus, 5, 953. https://doi.org/10.1186/s40064-016-2401-4"> https://doi.org/10.1186/s40064-016-2401-4

Sang, S. J. (2017). Decentralized channel decisions of green supply chain in a fuzzy decision making environment. International Journal of Computational Intelligence Systems, 10(1), 986–1001. https://doi.org/10.2991/ijcis.2017.10.1.66"> https://doi.org/10.2991/ijcis.2017.10.1.66

Sang, X. Z., & Liu, X. W. (2016). An interval type-2 fuzzy sets-based TODIM method and its application to green supplier selection. Journal of the Operational Research Society, 67(5), 722–734. https://doi.org/10.1057/jors.2015.86"> https://doi.org/10.1057/jors.2015.86

Sen, D. K., Datta, S., & Mahapatra, S. S. (2018). Sustainable supplier selection in intuitionistic fuzzy environment: a decision-making perspective. Benchmarking: An International Journal, 25(2), 545–574. https://doi.org/10.1108/BIJ-11-2016-0172"> https://doi.org/10.1108/BIJ-11-2016-0172

Sengupta, D., Das, A., & Bera, U. K. (2018). A gamma type-2 defuzzification method for solving a solid transportation problem considering carbon emission. Applied Intelligence, 48(11), 3995–4022. https://doi.org/10.1007/s10489-018-1173-7"> https://doi.org/10.1007/s10489-018-1173-7

Shamout, M. D. (2020). Supply chain data analytics and supply chain agility: a fuzzy sets (fsQCA) approach. International Journal of Organizational Analysis, 28(5), 1055–1067. https://doi.org/10.1108/IJOA-05-2019-1759"> https://doi.org/10.1108/IJOA-05-2019-1759

Shankar, R., Choudhary, D., & Jharkharia, S. (2018). An integrated risk assessment model: A case of sustainable freight transportation systems. Transportation Research Part D-Transport and Environment, 63, 662–676. https://doi.org/10.1016/j.trd.2018.07.003"> https://doi.org/10.1016/j.trd.2018.07.003

Sharahi, S. J., Khalili-Damghani, K., Abtahi, A. R., & Rashidi-Komijan, A. (2018). Type-II fuzzy multiproduct, multi-level, multi-period location-allocation, production-distribution problem in supply chains: modelling and optimisation approach. Fuzzy Information and Engineering, 10(2), 260–283. https://doi.org/10.1080/16168658.2018.1517978"> https://doi.org/10.1080/16168658.2018.1517978

Sharanlou, H., Kashan, A. H., & Tavakkoli-Moghaddam, R. (2021). Determining the price and refund of products in a supply chain with quality and advertising costs in a fuzzy environment. Soft Computing, 25, 2351–2370. https://doi.org/10.1007/s00500-020-05307-7"> https://doi.org/10.1007/s00500-020-05307-7

Shekarian, E., Olugu, E. U., Abdul-Rashid, S. H., & Bottani, E. (2016). A fuzzy reverse logistics inventory system integrating economic order/production quantity models. International Journal of Fuzzy Systems, 18(6), 1141–1161. https://doi.org/10.1007/s40815-015-0129-x"> https://doi.org/10.1007/s40815-015-0129-x

Shen, C. Y., & Yu, K. T. (2009). A generalized fuzzy approach for strategic problems: the empirical study on facility location selection of authors’ management consultation client as an example. Expert Systems with Applications, 36(3), 4709–4716. https://doi.org/10.1016/j.eswa.2008.06.035"> https://doi.org/10.1016/j.eswa.2008.06.035

Shen, L. X., Olfat, L., Govindan, K., Khodaverdi, R., & Diabat, A. (2013). A fuzzy multi criteria approach for evaluating green supplier’s performance in green supply chain with linguistic preferences. Resources Conservation and Recycling, 74, 170–179. https://doi.org/10.1016/j.resconrec.2012.09.006"> https://doi.org/10.1016/j.resconrec.2012.09.006

Shete, P. C., Ansari, Z. N., & Kant, R. (2020). A Pythagorean fuzzy AHP approach and its application to evaluate the enablers of sustainable supply chain innovation. Sustainable Production and Consumption, 23, 77–93. https://doi.org/10.1016/j.spc.2020.05.001"> https://doi.org/10.1016/j.spc.2020.05.001

Shi, H., Quan, M. Y., Liu, H. C., & Duan, C. Y. (2018). A novel integrated approach for green supplier selection with interval-valued intuitionistic uncertain linguistic information: a case study in the agri-food industry. Sustainability, 10(3), 733. https://doi.org/10.3390/su10030733"> https://doi.org/10.3390/su10030733

Singh, A. (2015). Multi-period demand allocation among supplier in a supply chain. Journal of Modelling in Management, 10(2), 138–157. https://doi.org/10.1108/JM2-09-2013-0046"> https://doi.org/10.1108/JM2-09-2013-0046

Soheilirad, S., Govindan, K., Mardani, A., Zavadskas, E. K., Nilashi, M., & Zakuan, N. (2018). Application of data envelopment analysis models in supply chain management: a systematic review and meta-analysis. Annals of Operations Research, 271(2), 915–969. https://doi.org/10.1007/s10479-017-2605-1"> https://doi.org/10.1007/s10479-017-2605-1

Sremac, S., Zavadskas, E. K., Matic, B., Kopic, M., & Stevic, Z. (2019). Neuro-fuzzy inference systems approach to decision support system for economic order quantity. Economic Research-Ekonomska Istraživanja, 32(1), 1114–1137. https://doi.org/10.1080/1331677x.2019.1613249"> https://doi.org/10.1080/1331677x.2019.1613249

Sun, Y. (2020a). Fuzzy approaches and simulation-based reliability modeling to solve a road-rail intermodal routing problem with soft delivery time windows when demand and capacity are uncertain. International Journal of Fuzzy Systems, 22(7), 2119–2148. https://doi.org/10.1007/s40815-020-00905-x"> https://doi.org/10.1007/s40815-020-00905-x

Sun, Y. (2020b). Green and reliable freight routing problem in the road-rail intermodal transportation network with uncertain parameters: a fuzzy goal programming approach. Journal of Advanced Transportation, 2020, 7570686. https://doi.org/10.1155/2020/7570686"> https://doi.org/10.1155/2020/7570686

Sun, Y., & Li, X. Y. (2019). Fuzzy programming approaches for modeling a customer-centred freight routing problem in the road-rail intermodal hub-and-spoke network with fuzzy soft time windows and multiple sources of time uncertainty. Mathematics, 7(8), 739. https://doi.org/10.3390/math7080739"> https://doi.org/10.3390/math7080739

Sun, Y., Liang, X., Li, X. Y., & Zhang, C. (2019a). A fuzzy programming method for modeling demand uncertainty in the capacitated road-rail multimodal routing problem with time windows. Symmetry, 11(1), 91. https://doi.org/10.3390/sym11010091"> https://doi.org/10.3390/sym11010091

Sun, Y., Lu, Y., & Zhang, C. (2019b). Fuzzy linear programming models for a green logistics center location and allocation problem under mixed uncertainties based on different carbon dioxide emission reduction methods. Sustainability, 11(22), 6448. https://doi.org/10.3390/su11226448"> https://doi.org/10.3390/su11226448

Tabrizi, B. H., & Razmi, J. (2013). Introducing a mixed-integer non-linear fuzzy model for risk management in designing supply chain networks. Journal of Manufacturing Systems, 32(2), 295–307. https://doi.org/10.1016/j.jmsy.2012.12.001"> https://doi.org/10.1016/j.jmsy.2012.12.001

Tamošaitienė, J., Zavadskas, E. K., Šileikaitė, I., & Turskis, Z. (2017). A novel hybrid MCDM approach for complicated supply chain management problems in construction. Modern Building Materials, Structures and Techniques, 172, 1137–1145. https://doi.org/10.1016/j.proeng.2017.02.168"> https://doi.org/10.1016/j.proeng.2017.02.168

Temur, G. T. (2016). A novel multi attribute decision making approach for location decision under high uncertainty. Applied Soft Computing, 40, 674–682. https://doi.org/10.1016/j.asoc.2015.12.027"> https://doi.org/10.1016/j.asoc.2015.12.027

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"> https://doi.org/10.15388/Informatica.2018.192

Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529–539. https://doi.org/10.1002/int.20418"> https://doi.org/10.1002/int.20418

Tsai, K. M., You, S. Y., Lin, Y. H., & Tsai, C. H. (2008). A fuzzy goal programming approach with priority for channel allocation problem in steel industry. Expert Systems with Applications, 34(3), 1870–1876. https://doi.org/10.1016/j.eswa.2007.02.034"> https://doi.org/10.1016/j.eswa.2007.02.034

Tseng, M. L. (2011). Green supply chain management with linguistic preferences and incomplete information. Applied Soft Computing, 11(8), 4894–4903. https://doi.org/10.1016/j.asoc.2011.06.010"> https://doi.org/10.1016/j.asoc.2011.06.010

Tseng, M. L., Islam, M. S., Karia, N., Fauzi, F. A., & Afrin, S. (2019). A literature review on green supply chain management: Trends and future challenges. Resources Conservation and Recycling, 141, 145–162. https://doi.org/10.1016/j.resconrec.2018.10.009"> https://doi.org/10.1016/j.resconrec.2018.10.009

Tseng, M. L., Lim, M. K., Wong, W. P., Chen, Y. C., & Zhan, Y. Z. (2018). A framework for evaluating the performance of sustainable service supply chain management under uncertainty. International Journal of Production Economics, 195, 359–372. https://doi.org/10.1016/j.ijpe.2016.09.002"> https://doi.org/10.1016/j.ijpe.2016.09.002

Tseng, M. L., Lin, R. J., Lin, Y. H., Chen, R. H., & Tan, K. (2014a). Close-loop or open hierarchical structures in green supply chain management under uncertainty. Expert Systems with Applications, 41(7), 3250–3260. https://doi.org/10.1016/j.eswa.2013.10.062"> https://doi.org/10.1016/j.eswa.2013.10.062

Tseng, M. L., Lin, Y. H., Tan, K., Chen, R. H., & Chen, Y. H. (2014b). Using TODIM to evaluate green supply chain practices under uncertainty. Applied Mathematical Modelling, 38(11–12), 2983–2995. https://doi.org/10.1016/j.apm.2013.11.018"> https://doi.org/10.1016/j.apm.2013.11.018

Tseng, M. L., Tan, K. H., & Chiu, A. S. F. (2016). Identifying the competitive determinants of firms’ green supply chain capabilities under uncertainty. Clean Technologies and Environmental Policy, 18(5), 1247–1262. https://doi.org/10.1007/s10098-015-1064-0"> https://doi.org/10.1007/s10098-015-1064-0

Tseng, M. L., Tan, K. H., Lim, M., Lin, R. J., & Geng, Y. (2014c). Benchmarking eco-efficiency in green supply chain practices in uncertainty. Production Planning & Control, 25(13–14), 1079–1090. https://doi.org/10.1080/09537287.2013.808837"> https://doi.org/10.1080/09537287.2013.808837

Tseng, M. L., Tran, T. P. T., Wu, K. J., Tan, R. D. R., & Bui, T. D. (2020). Exploring sustainable seafood supply chain management based on linguistic preferences: collaboration in the supply chain and lean management drive economic benefits. International Journal of Logistics-Research and Applications, 2020, 1–23. https://doi.org/10.1080/13675567.2020.1800608"> https://doi.org/10.1080/13675567.2020.1800608

Tuzkaya, G., Ozgen, A., Ozgen, D., & Tuzkaya, U. R. (2009). Environmental performance evaluation of suppliers: A hybrid fuzzy multi-criteria decision approach. International Journal of Environmental Science and Technology, 6(3), 477–490. https://doi.org/10.1007/BF03326087"> https://doi.org/10.1007/BF03326087

Tyagi, M., Kumar, P., & Kumar, D. (2015). Permutation of fuzzy AHP and AHP methods to prioritizing the alternatives of supply chain performance system. International Journal of Industrial EngineeringTheory Applications and Practice, 22(6), 729–752.

Ulutas, A. (2019). Supplier selection by using a fuzzy integrated model for a textile company. Inzinerine Ekonomika-Engineering Economics, 30(5), 579–590. https://doi.org/10.5755/j01.ee.30.5.20546"> https://doi.org/10.5755/j01.ee.30.5.20546

Vahdani, B., & Zandieh, M. (2010). Selecting suppliers using a new fuzzy multiple criteria decision model: the fuzzy balancing and ranking method. International Journal of Production Research, 48(18), 5307–5326. https://doi.org/10.1080/00207540902933155"> https://doi.org/10.1080/00207540902933155

Van Eck, N. J., & Waltman, L. (2020). VOSviewer (version 1.6.16) [Computer software]. Centre for Science and Technology Studies, Leiden University. https://www.vosviewer.com/"> https://www.vosviewer.com/

Van, L. H., Yu, V. F., Dat, L. Q., Dung, C. C., Chou, S. Y., & Loc, N. V. (2018). New integrated quality function deployment approach based on interval neutrosophic set for green supplier evaluation and selection. Sustainability, 10(3), 838. https://doi.org/10.3390/su10030838"> https://doi.org/10.3390/su10030838

Villa Silva, A. J., Dominguez, L. A. P., Gomez, E. M., Alvarado-Iniesta, A., & Olguin, I. J. C. P. (2019). Dimensional analysis under Pythagorean fuzzy approach for supplier selection. Symmetry, 11(3), 336. https://doi.org/10.3390/sym11030336"> https://doi.org/10.3390/sym11030336

Wadhwa, S., Madaan, J., & Chan, F. T. S. (2009). Flexible decision modeling of reverse logistics system: A value adding MCDM approach for alternative selection. Robotics and Computer-Integrated Manufacturing, 25(2), 460–469. https://doi.org/10.1016/j.rcim.2008.01.006"> https://doi.org/10.1016/j.rcim.2008.01.006

Wan, S. P., & Li, D. F. (2013). Fuzzy LINMAP approach to heterogeneous MADM considering comparisons of alternatives with hesitation degrees. Omega, 41(6), 925–940. https://doi.org/10.1016/j.omega.2012.12.002"> https://doi.org/10.1016/j.omega.2012.12.002

Wan, S. P., & Li, D. F. (2015). Fuzzy mathematical programming approach to heterogeneous multiattribute decision-making with interval-valued intuitionistic fuzzy truth degrees. Information Sciences, 325, 484–503. https://doi.org/10.1016/j.ins.2015.07.014"> https://doi.org/10.1016/j.ins.2015.07.014

Wan, Y. C., & Chen, Q. C. (2020). Research on risk-averse retailer’s spot procurement decision under fuzzy demand. Engineering Letters, 28(2), 433–444.

Wang, C. N., Viet, V. T. H., Ho, T. P., Nguyen, V. T., & Nguyen, V. T. (2020). Multi-criteria decision model for the selection of suppliers in the textile industry. Symmetry, 12(6), 979. https://doi.org/10.3390/sym12060979"> https://doi.org/10.3390/sym12060979

Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110. https://doi.org/10.1016/j.ijpe.2016.03.014"> https://doi.org/10.1016/j.ijpe.2016.03.014

Wang, J., & Lin, H. Y. (2006). A fuzzy hybrid decision-aid model for selecting partners in the design chain. International Journal of Production Research, 44(10), 2047–2069. https://doi.org/10.1080/00207540500354028"> https://doi.org/10.1080/00207540500354028

Wang, J. L. (2009). A supply chain application of fuzzy set theory to inventory control models – DRP system analysis. Expert Systems with Applications, 36(5), 9229–9239. https://doi.org/10.1016/j.eswa.2008.12.047"> https://doi.org/10.1016/j.eswa.2008.12.047

Wang, J. T., & Shu, Y. F. (2005). Fuzzy decision modeling for supply chain management. Fuzzy Sets and Systems, 150(1), 107–127. https://doi.org/10.1016/j.fss.2004.07.005"> https://doi.org/10.1016/j.fss.2004.07.005

Wang, J. T., & Shu, Y. F. (2007). A possibilistic decision model for new product supply chain design. European Journal of Operational Research, 177(2), 1044–1061. https://doi.org/10.1016/j.ejor.2005.12.032"> https://doi.org/10.1016/j.ejor.2005.12.032

Wang, K. Q., Liu, H. C., Liu, L. P., & Huang, J. (2017a). Green supplier evaluation and selection using cloud model theory and the QUALIFLEX method. Sustainability, 9(5), 688. https://doi.org/10.3390/su9050688"> https://doi.org/10.3390/su9050688

Wang, L., Ma, L., Wu, K. J., Chiu, A. S. F., & Nathaphan, S. (2018). Applying fuzzy interpretive structural modeling to evaluate responsible consumption and production under uncertainty. Industrial Management & Data Systems, 118(2), 432–462. https://doi.org/10.1108/IMDS-03-2017-0109"> https://doi.org/10.1108/IMDS-03-2017-0109

Wang, R., & Li, Y. L. (2018). A novel approach for green supplier selection under a q-Rung orthopair fuzzy environment. Symmetry, 10(12), 687. https://doi.org/10.3390/sym10120687"> https://doi.org/10.3390/sym10120687

Wang, W., Huang, L., Zhu, Y. L., Jiang, L. P., Sahu, A. K., Sahu, A. K., & Sahu, N. K. (2019). Decision support system toward evaluation of resilient supplier: A novel fuzzy gain-loss computational approach. Kybernetes, 49(6), 1741–1765. https://doi.org/10.1108/K-05-2019-0345"> https://doi.org/10.1108/K-05-2019-0345

Wang, X. J., & Chan, H. K. (2013). A hierarchical fuzzy TOPSIS approach to assess improvement areas when implementing green supply chain initiatives. International Journal of Production Research, 51(10), 3117–3130. https://doi.org/10.1080/00207543.2012.754553"> https://doi.org/10.1080/00207543.2012.754553

Wang, X. J., Li, D., & Shi, X. L. (2012). A fuzzy model for aggregative food safety risk assessment in food supply chains. Production Planning & Control, 23(5), 377–395. https://doi.org/10.1080/09537287.2011.561812"> https://doi.org/10.1080/09537287.2011.561812

Wang, X. J., Tiwari, P., & Chen, X. (2017b). Communicating supply chain risks and mitigation strategies: a comprehensive framework. Production Planning & Control, 28(13), 1023–1036. https://doi.org/10.1080/09537287.2017.1329562"> https://doi.org/10.1080/09537287.2017.1329562

Wang, Y., Ma, X. L., Lao, Y. T., & Wang, Y. H. (2014). A fuzzy-based customer clustering approach with hierarchical structure for logistics network optimization. Expert Systems with Applications, 41(2), 521–534. https://doi.org/10.1016/j.eswa.2013.07.078"> https://doi.org/10.1016/j.eswa.2013.07.078

Wicher, P., Zapletal, F., Lenort, R., & Stas, D. (2016). Measuring the metallurgical supply chain resilience using fuzzy analytic network process. Metalurgija, 55(4), 783–786.

Wen, Z., Liao, H. C., Zavadskas, E. K., & Al-Barakati, A. (2019). Selection third-party logistics service providers in supply chain finance by a hesitant fuzzy linguistic combined compromise solution method. Economic Research-Ekonomska Istraživanja, 32(1), 4033–4058. https://doi.org/10.1080/1331677x.2019.1678502"> https://doi.org/10.1080/1331677x.2019.1678502

Woo, H. S., & Saghiri, S. (2011). Order assignment considering buyer, third-party logistics provider, and suppliers. International Journal of Production Economics, 130(2), 144–152. https://doi.org/10.1016/j.ijpe.2010.10.019"> https://doi.org/10.1016/j.ijpe.2010.10.019

Wu, C. W., Liao, M. Y., & Lin, C. Y. (2014). On ranking multiple touch-screen panel suppliers through the CTQ: applied fuzzy techniques for inspection with unavoidable measurement errors. Neural Computing & Applications, 25(2), 481–490. https://doi.org/10.1007/s00521-013-1500-1"> https://doi.org/10.1007/s00521-013-1500-1

Wu, C., & Barnes, D. (2014). Partner selection in agile supply chains: a fuzzy intelligent approach. Production Planning & Control, 25(10), 821–839. https://doi.org/10.1080/09537287.2013.766037"> https://doi.org/10.1080/09537287.2013.766037

Wu, C., & Barnes, D. (2016). Partner selection for reverse logistics centres in green supply chains: a fuzzy artificial immune optimization approach. Production Planning & Control, 27(16), 1356–1372. https://doi.org/10.1080/09537287.2016.1221159"> https://doi.org/10.1080/09537287.2016.1221159

Wu, D. X., Wu, D. D., Zhang, Y. D., & Olson, D. L. (2013). Supply chain outsourcing risk using an integrated stochastic-fuzzy optimization approach. Information Sciences, 235, 242–258. https://doi.org/10.1016/j.ins.2013.02.002"> https://doi.org/10.1016/j.ins.2013.02.002

Wu, K. J., Liao, C. J., Tseng, M. L., & Chiu, A. S. F. (2015). Exploring decisive factors in green supply chain practices under uncertainty. International Journal of Production Economics, 159, 147–157. https://doi.org/10.1016/j.ijpe.2014.09.030"> https://doi.org/10.1016/j.ijpe.2014.09.030

Wu, K. J., Tseng, M. L., Chiu, A. S. F., & Lim, M. K. (2017). Achieving competitive advantage through supply chain agility under uncertainty: A novel multi-criteria decision-making structure. International Journal of Production Economics, 190, 96–107. https://doi.org/10.1016/j.ijpe.2016.08.027"> https://doi.org/10.1016/j.ijpe.2016.08.027

Wu, Q., Zhou, L. G., Chen, Y., & Chen, H. Y. (2019). An integrated approach to green supplier selection based on the interval type-2 fuzzy best-worst and extended VIKOR methods. Information Sciences, 502, 394–417. https://doi.org/10.1016/j.ins.2019.06.049"> https://doi.org/10.1016/j.ins.2019.06.049

Wu, X. L., & Liao, H. C. (2020). Utility-based hybrid fuzzy axiomatic design and its application in supply chain finance decision making with credit risk assessments. Computers in Industry, 114, 103144. https://doi.org/10.1016/j.compind.2019.103144"> https://doi.org/10.1016/j.compind.2019.103144

Wu, Y. N., Xu, C. B., Huang, Y., & Li, X. Y. (2020). Green supplier selection of electric vehicle charging based on Choquet integral and type-2 fuzzy uncertainty. Soft Computing, 24(5), 3781–3795. https://doi.org/10.1007/s00500-019-04147-4"> https://doi.org/10.1007/s00500-019-04147-4

Xie, Y., Petrovic, D., & Burnham, K. (2006). A heuristic procedure for the two-level control of serial supply chains under fuzzy customer demand. International Journal of Production Economics, 102(1), 37–50. https://doi.org/10.1016/j.ijpe.2005.01.016"> https://doi.org/10.1016/j.ijpe.2005.01.016

Xiong, L., Zhong, S. Q., Liu, S., Zhang, X., & Li, Y. F. (2020). An approach for resilient-green supplier selection based on WASPAS, BWM, and TOPSIS under intuitionistic fuzzy sets. Mathematical Problems in Engineering, 2020, 1761893. https://doi.org/10.1155/2020/1761893"> https://doi.org/10.1155/2020/1761893

Xu, J. P., He, Y. A., & Gen, M. (2009). A class of random fuzzy programming and its application to supply chain design. Computers & Industrial Engineering, 56(3), 937–950. https://doi.org/10.1016/j.cie.2008.09.045"> https://doi.org/10.1016/j.cie.2008.09.045

Xu, Z., Qin, J. D., Liu, J., & Martinez, L. (2019). Sustainable supplier selection based on AHPSort II in interval type-2 fuzzy environment. Information Sciences, 483, 273–293. https://doi.org/10.1016/j.ins.2019.01.013"> https://doi.org/10.1016/j.ins.2019.01.013

Yazdani, M., Zarate, P., Coulibaly, A., & Zavadskas, E. K. (2017). A group decision making support system in logistics and supply chain management. Expert Systems with Applications, 88, 376–392. https://doi.org/10.1016/j.eswa.2017.07.014"> https://doi.org/10.1016/j.eswa.2017.07.014

Yazici, E., Buyukozkan, G., & Baskak, M. (2016). A new extended MILP MRP approach to production planning and its application in the jewelry industry. Mathematical Problems in Engineering, 2016, 7915673. https://doi.org/10.1155/2016/7915673"> https://doi.org/10.1155/2016/7915673

Yu, G. D., Zhang, L., & Sun, H. P. (2018). A method for partner selection of supply chain using intervalvalued fuzzy sets – fuzzy choquet integral and improved Dempster-Shafer theory. International Journal of Information Technology & Decision Making, 17(6), 1777–1804. https://doi.org/10.1142/S0219622018500438"> https://doi.org/10.1142/S0219622018500438

Yu, Y., & Jin, T. D. (2011). The return policy model with fuzzy demands and asymmetric information. Applied Soft Computing, 11(2), 1669–1678. https://doi.org/10.1016/j.asoc.2010.05.004"> https://doi.org/10.1016/j.asoc.2010.05.004

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/s0019-9958(65)90241-x"> https://doi.org/10.1016/s0019-9958(65)90241-x

Zadeh, L. A. (1975). Concept of a linguistic variable and its application to approximate reasoning – 1. Information Sciences, 8(3), 199–249. https://doi.org/10.1016/0020-0255(75)90036-5"> https://doi.org/10.1016/0020-0255(75)90036-5

Zandi, F., Tavana, M., & Martin, D. (2011). A fuzzy group Electre method for electronic supply chain management framework selection. International Journal of Logistics-Research and Applications, 14(1), 35–60. https://doi.org/10.1080/13675567.2010.550872"> https://doi.org/10.1080/13675567.2010.550872

Zhang, G. Q., Shang, J., & Li, W. L. (2011). Collaborative production planning of supply chain under price and demand uncertainty. European Journal of Operational Research, 215(3), 590–603. https://doi.org/10.1016/j.ejor.2011.07.007"> https://doi.org/10.1016/j.ejor.2011.07.007

Zhao, J. S., You, X. Y., Liu, H. C., & Wu, S. M. (2017). An extended VIKOR method using intuitionistic fuzzy sets and combination weights for supplier selection. Symmetry, 9(9), 169. https://doi.org/10.3390/sym9090169"> https://doi.org/10.3390/sym9090169

Zhao, X., Xia, X. H., Wang, L., & Cao, J. H. (2019). A fuzzy multi-objective immune genetic algorithm for the strategic location planning problem. Cluster Computing-the Journal of Networks Software Tools and Applications, 22(2), S3621–S3641. https://doi.org/10.1007/s10586-018-2212-1"> https://doi.org/10.1007/s10586-018-2212-1

Zheng, H. Y., Deng, Y., & Hu, Y. (2017). Fuzzy evidential influence diagram and its evaluation algorithm. Knowledge-Based Systems, 131, 28–45. https://doi.org/10.1016/j.knosys.2017.05.024"> https://doi.org/10.1016/j.knosys.2017.05.024

Zhou, X. Y., Pedrycz, W., Kuang, Y. X., & Zhang, Z. (2016). Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation. Applied Soft Computing, 46, 424–440. https://doi.org/10.1016/j.asoc.2016.04.038"> https://doi.org/10.1016/j.asoc.2016.04.038

Zhou, X. Y., Wang, Y., Chai, J., Wang, L. Q., Wang, S. Y., & Lev, B. (2019). Sustainable supply chain evaluation: A dynamic double frontier network DEA model with interval type-2 fuzzy data. Information Sciences, 504, 394–421. https://doi.org/10.1016/j.ins.2019.07.033"> https://doi.org/10.1016/j.ins.2019.07.033

An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions

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