Development a Quantitative Framework for Multilayer Fuzzy Cognitive Maps by combining "Self-Organizing Map" and "Graph Theory and Matrix Approach" (SOM-GTMA)

References

Aguilar, J. (2013). Different dynamic causal relationship approaches for cognitive maps. Applied Soft Computing, 13(1), 271-282.

Aguilar, J. (2016). Multilayer Cognitive Maps in the Resolution of Problems using the FCM Designer Tool. Applied Artificial Intelligence, 30(7), 720-743.

Azar A., Khosravani F., Jalali R., (2013). Soft Operational Research, Industrial Management Organization Publishing. (In Persian)

Azar A., Khosravani F., Jalali R., (2013). Soft Operational Research, Industrial Management Organization Publishing. (In Persian)

Badri Ahmadi, H., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126, 99–106.

Baykasoglu, A. (2014). A review and analysis of “graph theoretical-matrix permanent” approach to decision making with example applications. Artificial intelligence review, 42(4), 573-605.

Baykasoğlu, A., & Gölcük, İ. (2017). Comprehensive fuzzy FMEA model: a case study of ERP implementation risks. Operational Research, 1-32.

Beske, P., Land, A., & Seuring, S. (2014). Sustainable supply chain management practices and dynamic capabilities in the food industry: A critical analysis of the literature. International Journal of Production Economics, 152, 131–143.

Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for social network analysis.

Buede, D. M., & Ferrell, D. O. (1993). Convergence in problem solving: a prelude to quantitative analysis. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 746-765.

Chardine-Baumann, E., & Botta-Genoulaz, V. (2014). A framework for sustainable performance assessment of supply chain management practices. Computers and Industrial Engineering, 76(1), 138–147.

Christoforou, A., & Andreou, A. S. (2016). A Framework for Static and Dynamic Analysis of Multi-Layer Fuzzy Cognitive Maps. Neurocomputing, 232, 133-145.

Das, D. (2017). Development and validation of a scale for measuring Sustainable Supply Chain Management practices and performance. Journal of Cleaner Production, 164, 1344–1362.

Diabat, A., Kannan, D., & Mathiyazhagan, K. (2014). Analysis of enablers for implementation of sustainable supply chain management - A textile case. Journal of Cleaner Production, 83, 391–403.

Dickerson, J. A., & Kosko, B. (1994). Virtual worlds as fuzzy cognitive maps. Presence: Teleoperators & Virtual Environments, 3(2), 173-189.

Ding H., Liu Q., Zheng L. (2016). “Assessing the Economic Performance of an Environmental Sustainable Supply Chain in Reducing Environmental Externalities”, European Journal of Operational Research.

Dodurka, M. F., Yesil, E., & Urbas, L. (2017). Causal effect analysis for fuzzy cognitive maps designed with non-singleton fuzzy numbers. Neurocomputing, 232, 122-132.

Froelich, W., & Salmeron, J. L. (2017). Advances in fuzzy cognitive maps theory. Neurocomputing, 100(232), 1-2.

Garg, C. P., Sharma, A., & Goyal, G. (2017). A hybrid decision model to evaluate critical factors for successful adoption of GSCM practices under fuzzy environment. Uncertain Supply Chain Management, 5, 59–70.

Giunipero, L. C., Hooker, R. E., & Denslow, D. (2012). Purchasing and supply management sustainability: Drivers and barriers. Journal of Purchasing and Supply Management, 18(4), 258–269.

Glykas, M. (Ed.). (2010). Fuzzy cognitive maps: Advances in theory, methodologies, tools and applications (Vol. 247). Springer Science & Business Media.

Gong, M., Simpson, A., Koh, L., & Tan, K. H. (2018). Inside out: The interrelationships of sustainable performance metrics and its effect on business decision making: Theory and practice. Resources, Conservation and Recycling, 128, 155–166.

Gosling, J., Jia, F., Gong, Y., & Brown, S. (2017). The role of supply chain leadership in the learning of sustainable practice: Toward an integrated framework. Journal of Cleaner Production, 140, 239–250.

Harary, F. (2005). Structural models: An introduction to the theory of directed graphs.

Homenda, W., & Jastrzebska, A. (2017). Clustering Techniques for Fuzzy Cognitive Map Design for Time Series Modeling. Neurocomputing, 232, 3-15.

Hong, J., Zhang, Y., & Ding, M. (2018). Sustainable supply chain management practices, supply chain dynamic capabilities, and enterprise performance. Journal of Cleaner Production, 172, 3508–3519.

Hussain, M., Awasthi, A., & Tiwari, M. K. (2016). Interpretive structural modeling-analytic network process integrated framework for evaluating sustainable supply chain management alternatives. Applied Mathematical Modelling, 40(5–6), 3671–3687.

Jia, P., Diabat, A., & Mathiyazhagan, K. (2015). Analyzing the SSCM practices in the mining and mineral industry by ISM approach. Resources Policy, 46, 76–85.

Jurkat, W. ., & Ryser, H. (1966). Matrix factorizations of determinants and permanents. Journal of Algebra, 3(1), 1–27.

Kosko, B. (1986). Fuzzy cognitive maps. International journal of man-machine studies, 24(1), 65-75.

Kosko, B. (1987, June). Adaptive inference in fuzzy knowledge networks. In Proc. 1st Int. Conf. Neural Networks (Vol. 2, pp. 261-268).

Kosko, B. (1992). Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence (No. QA76. 76. E95 K86).

Kumar, D., & Rahman, Z. (2015). Sustainability adoption through buyer supplier relationship across supply chain: A literature review and conceptual framework. International Strategic Management Review, 3(1–2), 110–127.

Lim, M. K., Tseng, M. L., Tan, K. H., & Bui, T. D. (2017). Knowledge management in sustainable supply chain management: Improving performance through an interpretive structural modelling approach. Journal of Cleaner Production,

Luthra, S., Garg, D., & Haleem, A. (2016). The impacts of critical success factors for implementing green supply chain management towards sustainability: An empirical investigation of Indian automobile industry. Journal of Cleaner Production, 121, 142–158.

Luthra, S., Govindan, K., & Mangla, S. K. (2017). Structural model for sustainable consumption and production adoption—A grey-DEMATEL based approach. Resources, Conservation and Recycling, 125, 198–207.

Mani, V., Gunasekaran, A., & Delgado, C. (2018). Enhancing supply chain performance through supplier social sustainability: An emerging economy perspective. International Journal of Production Economics, 195, 259–272.

Mateou, N. H., & Andreou, A. S. (2005, November). Tree-structured multi-layer fuzzy cognitive maps for modelling large scale, complex problems. In Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on (Vol. 2, pp. 131-139).

Mateou, N., Andreou, A., & Stylianou, C. (2006). Evolutionary multilayered fuzzy cognitive maps: a hybrid system design to handle large-scale, complex, real-world problems. In Information and Communication Technologies, 2006. ICTTA'06. 2nd (Vol. 1, pp. 1663-1668).

Mathivathanan, D., Kannan, D., & Haq, A. N. (2018). Sustainable supply chain management practices in Indian automotive industry: A multi-stakeholder view. Resources, Conservation and Recycling, 128, 284–305.

Minc, H. (1984). Permanents (Vol. 6). Cambridge University Press.

Moktadir, M. A., Rahman, T., Rahman, M. H., Ali, S. M., & Paul, S. K. (2018). Drivers to sustainable manufacturing practices and circular economy: A perspective of leather industries in Bangladesh. Journal of Cleaner Production, 174, 1366–1380.

Moradi, M., & Jolai, F. (2018). Purchasing Planning and Order Allocation in the Pharmaceutical Sustainable Supply Chain Using the Theoretical-Graph (GT-MP-DM) (Case Study: Supplying the clotting factor for patients with hemophilia). International Journal of Supply and Operations Management, 5(4), 361-378.

Obiedat, M., & Samarasinghe, S. (2016). A novel semi-quantitative fuzzy cognitive map model for complex systems for addressing challenging participatory real life problems. Applied Soft Computing, 48, 91-110.

Özesmi, U., & Özesmi, S. L. (2004). Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach. Ecological modelling, 176(1), 43-64.

Papageorgiou, E. I. (2011, June). Review study on fuzzy cognitive maps and their applications during the last decade. In Fuzzy Systems (FUZZ), 2011 IEEE International Conference on (pp. 828-835).

Papageorgiou, E. I., & Salmeron, J. L. (2014). Methods and algorithms for fuzzy cognitive map-based modeling. In Fuzzy cognitive maps for applied sciences and engineering (pp. 1-28). Springer, Berlin, Heidelberg.

Papageorgiou, E. I., Hatwágner, M. F., Buruzs, A., & Kóczy, L. T. (2017). A Concept Reduction Approach for Fuzzy Cognitive Map Models in Decision Making and Management. Neurocomputing, 232, 16-33.

Raut, R. D., Narkhede, B., & Gardas, B. B. (2017, February 1). To identify the critical success factors of sustainable supply chain management practices in the context of oil and gas industries: ISM approach. Renewable and Sustainable Energy Reviews, 68, 33-47.

Rosenhead, J., & Mingers, J., Translated by: Azar A. & Anvari A., (2013). Soft modeling in management, methods for constructing a problem in terms of the complexity of conflict uncertainty, Negahe Danesh Publishing. (in persian)

Samarasinghe, S., & Strickert, G. (2013). Mixed-method integration and advances in fuzzy cognitive maps for computational policy simulations for natural hazard mitigation. Environmental modelling & software, 39, 188-200.

Sancha, C., Longoni, A., & Giménez, C. (2015). Sustainable supplier development practices: Drivers and enablers in a global context. Journal of Purchasing and Supply Management, 21(2), 95–102.

Shah Hoseini, M.A., Javaheri Shalmani, S.F., Hasangholipour Yasouri, T., & Rostami, A. (2019). Evaluating and Comparing Key Indicators of Sustainable Development Performance in the Petrochemical Industry Using SMAA and SMAA-S. Industrial Management Journal, 11(2), 273-302. (in Persian)

Shibin, K. T., Gunasekaran, A., & Dubey, R. (2017). Explaining sustainable supply chain performance using a total interpretive structural modeling approach. Sustainable Production and Consumption, 12, 104–118.

Singh, V., & Singru, P. M. (2018). Graph theoretic structural modeling based new measures of complexity for analysis of lean initiatives. Journal of Manufacturing Technology Management, 29(2), 329-349.

Stylios, C. D., & Groumpos, P. P. (2004). Modeling complex systems using fuzzy cognitive maps. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 34(1)155-162.

Tsadiras, A. K. (2008). Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Information Sciences, 178(20), 3880-3894.

Tsadiras, A. K., Kouskouvelis, I., & Margaritis, K. G. (2001, November). Using fuzzy cognitive maps as a decision support system for political decisions. In Panhellenic Conference on Informatics (pp. 172-182). Springer, Berlin, Heidelberg.

Van Vliet, M., Kok, K., & Veldkamp, T. (2010). Linking stakeholders and modellers in scenario studies: The use of Fuzzy Cognitive Maps as a communication and learning tool. Futures, 42(1), 1-14.

Wan Ahmad, W. N. K., Rezaei, J., Tavasszy, L. A., & de Brito, M. P. (2016). Commitment to and preparedness for sustainable supply chain management in the oil and gas industry. Journal of Environmental Management, 180, 202–213.

Wang, L. P., Pichler, E. E., & Ross, J. (1990). Oscillations and chaos in neural networks: an exactly solvable model. Proceedings of the National Academy of Sciences, 87(23), 9467-9471.

Winter, S., & Lasch, R. (2016). Environmental and social criteria in supplier evaluation – Lessons from the fashion and apparel industry. Journal of Cleaner Production, 139, 175–190.

Zhang, M., Tse, Y. K., Doherty, B., Li, S., & Akhtar, P. (2018). Sustainable supply chain management: Confirmation of a higher-order model. Resources, Conservation and Recycling, 128, 206–221