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EVALUATION OF SUPPLY CHAIN PERFORMANCE USING AN INTEGRATED TWO-STEP CLUSTERING AND INTERVAL TYPE-2 FUZZY TOPSIS METHOD: A CASE STUDY

Yıl 2019, , 141 - 160, 24.10.2019
https://doi.org/10.30794/pausbed.457187

Öz

Supply chain
management (SCM) is an important issue for many of the researchers and
organizations that have been tackling with it for improving their performance within
different perspectives. Various metrics and decision making methodologies have
been proposed to evaluate supply chain (SC) performance in different sectors.
This paper introduces an integration of the Two-Step Clustering and the
interval type-2 (IT2) Fuzzy TOPSIS method for SC performance evaluation processes.
In the first step of the proposed integrated approach, Two-Step Clustering
analysis (CA) is employed not only for homogenous segmentation of sectors, but
also to decrease the dimension of the problem. After obtaining the results, IT2
Fuzzy TOPSIS is used for the evaluation of each company within its cluster. The
results of the integrated approach propose a macro perspective on some of the
issues such as organizational efficiency and performance. Moreover, the results
have shown valuable insight that each company has the opportunity to evaluate
itself both against rivals within clusters and inter-sectoral rivals.

Kaynakça

  • Akman, G. (2015). Evaluating suppliers to include green supplier development programs via fuzzy c-means and VIKOR methods. Computers & Industrial Engineering, 86, 69-82.
  • Bai, C. G., Dhavale, D., & Sarkis, J. (2014). Integrating Fuzzy C-Means and TOPSIS for performance evaluation: An application and comparative analysis. Expert Systems with Applications, 41(9), 4186-4196.
  • Bernardes, E. S., & Zsidisin, G. A. (2008). An examination of strategic supply management benefits and performance implications. Journal of Purchasing and Supply Management, 14(4), 209-219.
  • Boley, D., Gini, M., Gross, R., Han, E.-H. S., Hastings , K., Karypis , G., . . . Moore, J. (1999). Partitioning-Based Clustering for Web Document Categorization Decision Support Systems, 27, 329-341.
  • 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.
  • Bottani, E., & Rizzi, A. (2008). An adapted multi-criteria approach to suppliers and products selection - An application oriented to lead-time reduction. International Journal of Production Economics, 111(2), 763-781.
  • Buyukozkan, G., & Cifci, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000-3011.
  • Chan, F. T. S. (2003). Performance Measurement in a Supply Chain. The International Journal of Advanced Manufacturing Technology, 21(7), 534-548.
  • Che, Z. H., & Wang, H. S. (2010). A hybrid approach for supplier cluster analysis. Computers & Mathematics with Applications, 59(2), 745-763.
  • Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9.
  • 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.
  • Chen, S.-M., & Lee, L.-W. (2010). Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Systems with Applications, 37(4), 2790-2798.
  • Chiu, T., Fang, D., Chen, J., Wang, Y., & Jeris, C. (2001). A robust and scalable clustering algorithm for mixed type attributes in large database environment. Paper presented at the Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining San Francisco.
  • Ding, H. W., Benyoucef, L., & Xie, X. L. (2005). A simulation optimization methodology for supplier selection problem. International Journal of Computer Integrated Manufacturing, 18(2-3), 210-224.
  • Estampe, D., Lamouri, S., Paris, J.-L., & Brahim-Djelloul, S. (2013). A framework for analysing supply chain performance evaluation models. International Journal of Production Economics, 142(2), 247-258.
  • Faez, F., Ghodsypour, S. H., & O'Brien, C. (2009). Vendor selection and order allocation using an integrated fuzzy case-based reasoning and mathematical programming model. International Journal of Production Economics, 121(2), 395-408.
  • Fu, G. (2008). A fuzzy optimization method for multicriteria decision making: An application to reservoir flood control operation. Expert Systems with Applications, 34(1), 145-149.
  • Gencer, C., & Gürpinar, D. (2007). Analytic network process in supplier selection: A case study in an electronic firm. Applied Mathematical Modelling, 31(11), 2475-2486.
  • Ghodsypour, S. H., & O'Brien, C. (2001). The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint. International Journal of Production Economics, 73(1), 15-27.
  • Goknur, A. A., & Turan, E. E. (2010). Supply chain performance measurement: a literature review. International Journal of Production Research, 48(17), 5137-5155.
  • Gunasekaran, A., & Kobu, B. (2007). Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. International Journal of Production Research, 45(12), 2819-2840.
  • Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333-347.
  • Gunasekaran, A., Williams, H. J., & McGaughey, R. E. (2005). Performance measurement and costing system in new enterprise. Technovation, 25(5), 523-533.
  • Guo, X., Yuan, Z., & Tian, B. (2009). Supplier selection based on hierarchical potential support vector machine. Expert Systems with Applications, 36(3, Part 2), 6978-6985.
  • Heidarzade, A., Mandavi, I., & Mandavi-Amiri, N. (2016). Supplier selection using a clustering method based on a new distance for interval type-2 fuzzy sets: A case study. Applied Soft Computing, 38, 213-231.
  • Ho, C.-J. (2007). Measuring system performance of an ERP-based supply chain. International Journal of Production Research, 45(6), 1255-1277.
  • Ho, W., Xu, X. W., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16-24.
  • Hong, G. H., Park, S. C., Jang, D. S., & Rho, H. M. (2005). An effective supplier selection method for constructing a competitive supply-relationship. Expert Systems with Applications, 28(4), 629-639.
  • Hwang, C.-L., & Yoon, K. (1981). Multiple attributes decision making methods and applications. Berlin: Springer-Verlag Berlin Heidelberg.
  • Kirytopoulos, K., Leopoulos, V., & Voulgaridou, D. (2008). Supplier selection in pharmaceutical industry: An analytic network process approach. Benchmarking: An International Journal, 15(4), 494-516.
  • Li-Wei, L., & Shyi-Ming, C. (2008, 12-15 July 2008). Fuzzy multiple attributes group decision-making based on the extension of TOPSIS method and interval type-2 fuzzy sets. Paper presented at the Machine Learning and Cybernetics, 2008 International Conference on.
  • Li, S., Ragu-Nathan, B., Ragu-Nathan, T. S., & Subba Rao, S. (2006). The impact of supply chain management practices on competitive advantage and organizational performance. Omega, 34(2), 107-124.
  • Lima Junior, F. R., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing, 21, 194-209.
  • Lin, C.-J., & Wu, W.-W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205-213.
  • Lin, R. H. (2009). An integrated FANP-MOLP for supplier evaluation and order allocation. Applied Mathematical Modelling, 33(6), 2730-2736.
  • Liu, J., Ding, F. Y., & Lall, V. (2000). Using data envelopment analysis to compare suppliers for supplier selection and performance improvement. Supply Chain Management: An International Journal, 5(3), 143-150.
  • Martin, P. R., & Patterson, J. W. (2009). On measuring company performance within a supply chain. International Journal of Production Research, 47(9), 2449-2460.
  • Matas, J., & Kittler , J. (1995). Spatial and Feature Space Clustering: Applications in Image Analysis. Paper presented at the CAIP Computer Analysis of Images and Patterns.
  • Mendel, J. M., John, R. I., & Feilong, L. (2006). Interval Type-2 Fuzzy Logic Systems Made Simple. Fuzzy Systems, IEEE Transactions on, 14(6), 808-821.
  • Michailidou, C., Maheras, P., Arseni-Papadimititriou, A., Kolyva-Machera, F., & Anagnostopoulou, C. (2009). A study of weather types at Athens and Thessaloniki and their relationship to circulation types for the cold-wet period, part I: two-step cluster analysis. Theoretical and Applied Climatology, 97(1-2), 163-177.
  • Mokhtarian, M. N., & Hadi-Vencheh, A. (2012). A new fuzzy TOPSIS method based on left and right scores: An application for determining an industrial zone for dairy products factory. Applied Soft Computing, 12(8), 2496-2505.
  • Peric, T., Babic, Z., & Veza, I. (2013). Vendor selection and supply quantities determination in a bakery by AHP and fuzzy multi-criteria programming. International Journal of Computer Integrated Manufacturing, 26(9), 816-829.
  • Petrakis, E. G. M., & Faloutsos, C. (1997). Similarity Searching in Medical Image Databases. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 9(3), 435-447.
  • Rezaei, J., & Ortt, R. (2012). A multi-variable approach to supplier segmentation. International Journal of Production Research, 50(16), 4593-4611.
  • Rezaei, J., & Ortt, R. (2013). Multi-criteria supplier segmentation using a fuzzy preference relations based AHP. European Journal of Operational Research, 225(1), 75-84.
  • Rezaei, J., & Ortt, R. (2013). Supplier segmentation using fuzzy logic. Industrial Marketing Management, 42(4), 507-517.
  • Saen, R. F. (2007). Suppliers selection in the presence of both cardinal and ordinal data. European Journal of Operational Research, 183(2), 741-747.
  • Satish, S. M., & Bharadhwaj, S. (2010). Information search behaviour among new car buyers: A two-step cluster analysis. IIMB Management Review, 22(1–2), 5-15.
  • Shemshadi, A., Shirazi, H., Toreihi, M., & Tarokh, M. J. (2011). A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Systems with Applications, 38(10), 12160-12167.
  • Shyi-Ming, C. (1988). A new approach to handling fuzzy decision-making problems. Systems, Man and Cybernetics, IEEE Transactions on, 18(6), 1012-1016.
  • Talluri, S., & Narasimhan, R. (2003). Vendor evaluation with performance variability: A max-min approach. European Journal of Operational Research, 146(3), 543-552.
  • Tam, M. C. Y., & Tummala, V. M. R. (2001). An application of the AHP in vendor selection of a telecommunications system. Omega-International Journal of Management Science, 29(2), 171-182.
  • Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to Data Mining Pearson.
  • Triantaphyllou, E., & Lin, C.-T. (1996). Development and evaluation of five fuzzy multiattribute decision-making methods. International Journal of Approximate Reasoning, 14(4), 281-310.
  • Tsabadze, T. (2006). A method for fuzzy aggregation based on group expert evaluations. Fuzzy Sets and Systems, 157(10), 1346-1361.
  • Wang, T.-C., & Chang, T.-H. (2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications, 33(4), 870-880.
  • Wang, Y.-M., & Parkan, C. (2005). Multiple attribute decision making based on fuzzy preference information on alternatives: Ranking and weighting. Fuzzy Sets and Systems, 153(3), 331-346.
  • Wang, Y.-M., & Parkan, C. (2006). A general multiple attribute decision-making approach for integrating subjective preferences and objective information. Fuzzy Sets and Systems, 157(10), 1333-1345.
  • Yager, R. R., & Xu, Z. (2006). The continuous ordered weighted geometric operator and its application to decision making. Fuzzy Sets and Systems, 157(10), 1393-1402.
  • Yao, K., & Liu, C. (2006). An integrated approach for measuring supply chain performance. Journal of Modern Accounting and Auditing, 2(10), 17.
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
  • Zhang, T., Ramakrishnan, R., & Livny, M. (1996). ACM SIGMOD international conference on Management of data Paper presented at the SIGMOD.
Yıl 2019, , 141 - 160, 24.10.2019
https://doi.org/10.30794/pausbed.457187

Öz

Kaynakça

  • Akman, G. (2015). Evaluating suppliers to include green supplier development programs via fuzzy c-means and VIKOR methods. Computers & Industrial Engineering, 86, 69-82.
  • Bai, C. G., Dhavale, D., & Sarkis, J. (2014). Integrating Fuzzy C-Means and TOPSIS for performance evaluation: An application and comparative analysis. Expert Systems with Applications, 41(9), 4186-4196.
  • Bernardes, E. S., & Zsidisin, G. A. (2008). An examination of strategic supply management benefits and performance implications. Journal of Purchasing and Supply Management, 14(4), 209-219.
  • Boley, D., Gini, M., Gross, R., Han, E.-H. S., Hastings , K., Karypis , G., . . . Moore, J. (1999). Partitioning-Based Clustering for Web Document Categorization Decision Support Systems, 27, 329-341.
  • 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.
  • Bottani, E., & Rizzi, A. (2008). An adapted multi-criteria approach to suppliers and products selection - An application oriented to lead-time reduction. International Journal of Production Economics, 111(2), 763-781.
  • Buyukozkan, G., & Cifci, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000-3011.
  • Chan, F. T. S. (2003). Performance Measurement in a Supply Chain. The International Journal of Advanced Manufacturing Technology, 21(7), 534-548.
  • Che, Z. H., & Wang, H. S. (2010). A hybrid approach for supplier cluster analysis. Computers & Mathematics with Applications, 59(2), 745-763.
  • Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9.
  • 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.
  • Chen, S.-M., & Lee, L.-W. (2010). Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Systems with Applications, 37(4), 2790-2798.
  • Chiu, T., Fang, D., Chen, J., Wang, Y., & Jeris, C. (2001). A robust and scalable clustering algorithm for mixed type attributes in large database environment. Paper presented at the Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining San Francisco.
  • Ding, H. W., Benyoucef, L., & Xie, X. L. (2005). A simulation optimization methodology for supplier selection problem. International Journal of Computer Integrated Manufacturing, 18(2-3), 210-224.
  • Estampe, D., Lamouri, S., Paris, J.-L., & Brahim-Djelloul, S. (2013). A framework for analysing supply chain performance evaluation models. International Journal of Production Economics, 142(2), 247-258.
  • Faez, F., Ghodsypour, S. H., & O'Brien, C. (2009). Vendor selection and order allocation using an integrated fuzzy case-based reasoning and mathematical programming model. International Journal of Production Economics, 121(2), 395-408.
  • Fu, G. (2008). A fuzzy optimization method for multicriteria decision making: An application to reservoir flood control operation. Expert Systems with Applications, 34(1), 145-149.
  • Gencer, C., & Gürpinar, D. (2007). Analytic network process in supplier selection: A case study in an electronic firm. Applied Mathematical Modelling, 31(11), 2475-2486.
  • Ghodsypour, S. H., & O'Brien, C. (2001). The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint. International Journal of Production Economics, 73(1), 15-27.
  • Goknur, A. A., & Turan, E. E. (2010). Supply chain performance measurement: a literature review. International Journal of Production Research, 48(17), 5137-5155.
  • Gunasekaran, A., & Kobu, B. (2007). Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. International Journal of Production Research, 45(12), 2819-2840.
  • Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333-347.
  • Gunasekaran, A., Williams, H. J., & McGaughey, R. E. (2005). Performance measurement and costing system in new enterprise. Technovation, 25(5), 523-533.
  • Guo, X., Yuan, Z., & Tian, B. (2009). Supplier selection based on hierarchical potential support vector machine. Expert Systems with Applications, 36(3, Part 2), 6978-6985.
  • Heidarzade, A., Mandavi, I., & Mandavi-Amiri, N. (2016). Supplier selection using a clustering method based on a new distance for interval type-2 fuzzy sets: A case study. Applied Soft Computing, 38, 213-231.
  • Ho, C.-J. (2007). Measuring system performance of an ERP-based supply chain. International Journal of Production Research, 45(6), 1255-1277.
  • Ho, W., Xu, X. W., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16-24.
  • Hong, G. H., Park, S. C., Jang, D. S., & Rho, H. M. (2005). An effective supplier selection method for constructing a competitive supply-relationship. Expert Systems with Applications, 28(4), 629-639.
  • Hwang, C.-L., & Yoon, K. (1981). Multiple attributes decision making methods and applications. Berlin: Springer-Verlag Berlin Heidelberg.
  • Kirytopoulos, K., Leopoulos, V., & Voulgaridou, D. (2008). Supplier selection in pharmaceutical industry: An analytic network process approach. Benchmarking: An International Journal, 15(4), 494-516.
  • Li-Wei, L., & Shyi-Ming, C. (2008, 12-15 July 2008). Fuzzy multiple attributes group decision-making based on the extension of TOPSIS method and interval type-2 fuzzy sets. Paper presented at the Machine Learning and Cybernetics, 2008 International Conference on.
  • Li, S., Ragu-Nathan, B., Ragu-Nathan, T. S., & Subba Rao, S. (2006). The impact of supply chain management practices on competitive advantage and organizational performance. Omega, 34(2), 107-124.
  • Lima Junior, F. R., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing, 21, 194-209.
  • Lin, C.-J., & Wu, W.-W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205-213.
  • Lin, R. H. (2009). An integrated FANP-MOLP for supplier evaluation and order allocation. Applied Mathematical Modelling, 33(6), 2730-2736.
  • Liu, J., Ding, F. Y., & Lall, V. (2000). Using data envelopment analysis to compare suppliers for supplier selection and performance improvement. Supply Chain Management: An International Journal, 5(3), 143-150.
  • Martin, P. R., & Patterson, J. W. (2009). On measuring company performance within a supply chain. International Journal of Production Research, 47(9), 2449-2460.
  • Matas, J., & Kittler , J. (1995). Spatial and Feature Space Clustering: Applications in Image Analysis. Paper presented at the CAIP Computer Analysis of Images and Patterns.
  • Mendel, J. M., John, R. I., & Feilong, L. (2006). Interval Type-2 Fuzzy Logic Systems Made Simple. Fuzzy Systems, IEEE Transactions on, 14(6), 808-821.
  • Michailidou, C., Maheras, P., Arseni-Papadimititriou, A., Kolyva-Machera, F., & Anagnostopoulou, C. (2009). A study of weather types at Athens and Thessaloniki and their relationship to circulation types for the cold-wet period, part I: two-step cluster analysis. Theoretical and Applied Climatology, 97(1-2), 163-177.
  • Mokhtarian, M. N., & Hadi-Vencheh, A. (2012). A new fuzzy TOPSIS method based on left and right scores: An application for determining an industrial zone for dairy products factory. Applied Soft Computing, 12(8), 2496-2505.
  • Peric, T., Babic, Z., & Veza, I. (2013). Vendor selection and supply quantities determination in a bakery by AHP and fuzzy multi-criteria programming. International Journal of Computer Integrated Manufacturing, 26(9), 816-829.
  • Petrakis, E. G. M., & Faloutsos, C. (1997). Similarity Searching in Medical Image Databases. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 9(3), 435-447.
  • Rezaei, J., & Ortt, R. (2012). A multi-variable approach to supplier segmentation. International Journal of Production Research, 50(16), 4593-4611.
  • Rezaei, J., & Ortt, R. (2013). Multi-criteria supplier segmentation using a fuzzy preference relations based AHP. European Journal of Operational Research, 225(1), 75-84.
  • Rezaei, J., & Ortt, R. (2013). Supplier segmentation using fuzzy logic. Industrial Marketing Management, 42(4), 507-517.
  • Saen, R. F. (2007). Suppliers selection in the presence of both cardinal and ordinal data. European Journal of Operational Research, 183(2), 741-747.
  • Satish, S. M., & Bharadhwaj, S. (2010). Information search behaviour among new car buyers: A two-step cluster analysis. IIMB Management Review, 22(1–2), 5-15.
  • Shemshadi, A., Shirazi, H., Toreihi, M., & Tarokh, M. J. (2011). A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Systems with Applications, 38(10), 12160-12167.
  • Shyi-Ming, C. (1988). A new approach to handling fuzzy decision-making problems. Systems, Man and Cybernetics, IEEE Transactions on, 18(6), 1012-1016.
  • Talluri, S., & Narasimhan, R. (2003). Vendor evaluation with performance variability: A max-min approach. European Journal of Operational Research, 146(3), 543-552.
  • Tam, M. C. Y., & Tummala, V. M. R. (2001). An application of the AHP in vendor selection of a telecommunications system. Omega-International Journal of Management Science, 29(2), 171-182.
  • Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to Data Mining Pearson.
  • Triantaphyllou, E., & Lin, C.-T. (1996). Development and evaluation of five fuzzy multiattribute decision-making methods. International Journal of Approximate Reasoning, 14(4), 281-310.
  • Tsabadze, T. (2006). A method for fuzzy aggregation based on group expert evaluations. Fuzzy Sets and Systems, 157(10), 1346-1361.
  • Wang, T.-C., & Chang, T.-H. (2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications, 33(4), 870-880.
  • Wang, Y.-M., & Parkan, C. (2005). Multiple attribute decision making based on fuzzy preference information on alternatives: Ranking and weighting. Fuzzy Sets and Systems, 153(3), 331-346.
  • Wang, Y.-M., & Parkan, C. (2006). A general multiple attribute decision-making approach for integrating subjective preferences and objective information. Fuzzy Sets and Systems, 157(10), 1333-1345.
  • Yager, R. R., & Xu, Z. (2006). The continuous ordered weighted geometric operator and its application to decision making. Fuzzy Sets and Systems, 157(10), 1393-1402.
  • Yao, K., & Liu, C. (2006). An integrated approach for measuring supply chain performance. Journal of Modern Accounting and Auditing, 2(10), 17.
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
  • Zhang, T., Ramakrishnan, R., & Livny, M. (1996). ACM SIGMOD international conference on Management of data Paper presented at the SIGMOD.
Toplam 62 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Makaleler
Yazarlar

Mehmet Erdem 0000-0003-4396-2149

Turan Erman Erkan 0000-0002-0078-711X

Yayımlanma Tarihi 24 Ekim 2019
Kabul Tarihi 19 Eylül 2019
Yayımlandığı Sayı Yıl 2019

Kaynak Göster

APA Erdem, M., & Erkan, T. E. (2019). EVALUATION OF SUPPLY CHAIN PERFORMANCE USING AN INTEGRATED TWO-STEP CLUSTERING AND INTERVAL TYPE-2 FUZZY TOPSIS METHOD: A CASE STUDY. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(37), 141-160. https://doi.org/10.30794/pausbed.457187
AMA Erdem M, Erkan TE. EVALUATION OF SUPPLY CHAIN PERFORMANCE USING AN INTEGRATED TWO-STEP CLUSTERING AND INTERVAL TYPE-2 FUZZY TOPSIS METHOD: A CASE STUDY. PAUSBED. Ekim 2019;(37):141-160. doi:10.30794/pausbed.457187
Chicago Erdem, Mehmet, ve Turan Erman Erkan. “EVALUATION OF SUPPLY CHAIN PERFORMANCE USING AN INTEGRATED TWO-STEP CLUSTERING AND INTERVAL TYPE-2 FUZZY TOPSIS METHOD: A CASE STUDY”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 37 (Ekim 2019): 141-60. https://doi.org/10.30794/pausbed.457187.
EndNote Erdem M, Erkan TE (01 Ekim 2019) EVALUATION OF SUPPLY CHAIN PERFORMANCE USING AN INTEGRATED TWO-STEP CLUSTERING AND INTERVAL TYPE-2 FUZZY TOPSIS METHOD: A CASE STUDY. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 37 141–160.
IEEE M. Erdem ve T. E. Erkan, “EVALUATION OF SUPPLY CHAIN PERFORMANCE USING AN INTEGRATED TWO-STEP CLUSTERING AND INTERVAL TYPE-2 FUZZY TOPSIS METHOD: A CASE STUDY”, PAUSBED, sy. 37, ss. 141–160, Ekim 2019, doi: 10.30794/pausbed.457187.
ISNAD Erdem, Mehmet - Erkan, Turan Erman. “EVALUATION OF SUPPLY CHAIN PERFORMANCE USING AN INTEGRATED TWO-STEP CLUSTERING AND INTERVAL TYPE-2 FUZZY TOPSIS METHOD: A CASE STUDY”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 37 (Ekim 2019), 141-160. https://doi.org/10.30794/pausbed.457187.
JAMA Erdem M, Erkan TE. EVALUATION OF SUPPLY CHAIN PERFORMANCE USING AN INTEGRATED TWO-STEP CLUSTERING AND INTERVAL TYPE-2 FUZZY TOPSIS METHOD: A CASE STUDY. PAUSBED. 2019;:141–160.
MLA Erdem, Mehmet ve Turan Erman Erkan. “EVALUATION OF SUPPLY CHAIN PERFORMANCE USING AN INTEGRATED TWO-STEP CLUSTERING AND INTERVAL TYPE-2 FUZZY TOPSIS METHOD: A CASE STUDY”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 37, 2019, ss. 141-60, doi:10.30794/pausbed.457187.
Vancouver Erdem M, Erkan TE. EVALUATION OF SUPPLY CHAIN PERFORMANCE USING AN INTEGRATED TWO-STEP CLUSTERING AND INTERVAL TYPE-2 FUZZY TOPSIS METHOD: A CASE STUDY. PAUSBED. 2019(37):141-60.