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An Intelligence-Based Model for Supplier Selection Integrating Data Envelopment Analysis and Support Vector Machine | ||
Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
مقاله 1، دوره 11، شماره 2، تیر 2018، صفحه 209-241 اصل مقاله (1.97 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/ijms.2018.237965.672750 | ||
نویسندگان | ||
Alireza Fallahpour1؛ Nima Kazemi* 2؛ Mohammad Molani3؛ Sina Nayyeri3؛ Mojtaba Ehsani4 | ||
1Department of Management, Farvardin Institute of Higher Education, Qaemshahr, Mazandaran, Iran | ||
2Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia | ||
3Innovation and Management Research Center, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran | ||
4Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran | ||
چکیده | ||
The importance of supplier selection is nowadays highlighted more than ever as companies have realized that efficient supplier selection can significantly improve the performance of their supply chain. In this paper, an integrated model that applies Data Envelopment Analysis (DEA) and Support Vector Machine (SVM) is developed to select efficient suppliers based on their predicted efficiency scores. In the first step, fuzzy linguistic variables are changed to crisp data as initial dataset for DEA. Actual efficiency scores are then calculated for each Decision Making Unit (DMU) using CCR-DEA model. Afterwards, suppliers’ performance-related data are used for training SVM-DEA model. A numerical example representing an actual case is provided to indicate the applicability of the model. | ||
کلیدواژهها | ||
Supplier Selection؛ Support vector Machine؛ Data Envelopment Analysis؛ supplier efficiency؛ Artificial Intelligence | ||
عنوان مقاله [English] | ||
ارائه یک مدل هوشمند جهت انتخاب تامینکننده مناسب بر اساس ترکیب روشهای تحلیل پوششی دادهها و SVM | ||
نویسندگان [English] | ||
علیرضا فلاح پور1؛ نیما کاظمی2؛ محمد مولانی3؛ سینا نیری3؛ مجتبی احسانی4 | ||
1دانشکده مدیریت، موسسه آموزش عالی فروردین، قائم شهر، ایران | ||
2دانشکده مهندسی مکانیک، دانشگاه مالایا، کوالالامپور، مالزی | ||
3دانشکده مهندسی صنایع، دانشگاه نوشیروانی، بابل، ایران | ||
4دانشکده مدیریت، موسسه آموزش عالی فروردین، قائم شهر، ایران | ||
چکیده [English] | ||
امروزه اهمیت انتخاب تامینکننده مناسب بیش از گذشته مورد تاکید میباشد زیرا سازمانها به خوبی به این مساله واقف هستند که انتخاب تامینکنندههای کارامد موجب بهبود در عملکرد زنجیرههای تامین است. در این مقاله، به منظورانتخاب تامینکننده مناسب، مدلی ترکیبی از تحلیل پوششی دادهها و ماشین بردار پشتیبانی بر اساس امتیاز پیشبینی کارایی تامینکنندگان، توسعه داده شدهاست. در مرحله اول، متغیرهای زبانی فازی به دادههای قطعی تبدیل شده که به عنوان دادههای اولیه برای مدل تحلیل پوششی دادهها مورد استفاده قرار گرفتهاست. سپس امتیازات واقعی برای هر واحد تصمیمگیری با استفاده از مدل سی سی آر محاسبه شدهاند. در مرحله بعد، دادههای مربوط به عملکرد تامینکننده برای آموزش مدل استفاده شد. در انتها، مثالی از کاربرد مدل در مسالهای واقعی ارائه گردید. | ||
کلیدواژهها [English] | ||
انتخاب تامین کننده, ماشین بردار پشتیبان, تحلیل پوششی دادهها, کارآمدی تامین کننده, هوش مصنوعی | ||
مراجع | ||
Amin, G. R., Toloo, M., & Sohrabi, B. (2006). An improved MCDM DEA model for technology selection. International Journal of Production Research, 44(13), 2681-2686. Andre, F. J., Herrero, I., & Riesgo, L. (2010). A modified DEA model to estimate the importance of objectives with an application to agricultural economics. Omega, 38(5), 371-382. Armaghani, D. J., Mohamad, E. T., Narayanasamy, M. S., Narita, N., & Yagiz, S. (2017a). Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunnelling and Underground Space Technology, 63, 29-43. Armaghani, D. J., Raja, R. S. N. S. B., Faizi, K., & Rashid, A. S. A. (2017b). Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Computing and Applications, 28(2), 391-405. Awasthi, A., & Kannan, G. (2016). Green supplier development program selection using NGT and VIKOR under fuzzy environment. Computers & Industrial Engineering, 91, 100-108. Azadeh, A., Ghaderi, S., & Izadbakhsh, H. (2008). Integration of DEA and AHP with computer simulation for railway system improvement and optimization. Applied Mathematics and Computation, 195(2), 775-785. Azadeh, A., Zarrin, M., & Salehi, N. (2016). Supplier selection in closed loop supply chain by an integrated simulation-Taguchi-DEA approach. Journal of Enterprise Information Management, 29(3), 302-326. Azadi, M., Mirhedayatian, S. M., Saen, R. F., Hatamzad, M., & Momeni, E. (2017). Green supplier selection: a novel fuzzy double frontier data envelopment analysis model to deal with undesirable outputs and dual-role factors. International Journal of Industrial and Systems Engineering, 25(2), 160-181. Banker, R., Chen, J. Y., & Klumpes, P. (2016). A trade-level DEA model to evaluate relative performance of investment fund managers. European Journal of Operational Research, 255(3), 903-910. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092. Cao, W., & Zhang, X. (2016). Supply chain risk assessment based on support vector machine. RISTI (Revista Iberica de Sistemas e Tecnologias de Informacao), (E5), 310-323. Çelebi, D., & Bayraktar, D. (2008). An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information. Expert Systems with Applications, 35(4), 1698-1710. Chen, S. H., & Hsieh, C. H. (1999). Optimization of fuzzy simple inventory models. Proceedings from FUZZ-IEEE'99: IEEE International Conference of the Fuzzy Systems. Chou, S., & Chen, C.-W. (2017). Supply chain coordination: an inventory model for single-period utility product under fuzzy demand. The International Journal of Advanced Manufacturing Technology, 88(1-4), 585-594. da Silva, A. F., Marins, F. A. S., Tamura, P. M., & Dias, E. X. (2017). Bi-Objective Multiple criteria data envelopment analysis combined with the overall equipment effectiveness: An application in an automotive company. Journal of Cleaner Production, 157, 278-288. de Boer, L., & de Boer, L. (2017). Procedural rationality in supplier selection: Outlining three heuristics for choosing selection criteria. Management Decision, 55(1), 32-56. De, S. K., & Mahata, G. C. (2017). Decision of a fuzzy inventory with fuzzy backorder model under cloudy fuzzy demand rate. International Journal of Applied and Computational Mathematics, 3(3), 2593-2609. Demirtas, E. A., & Üstün, Ö. (2008). An integrated multiobjective decision making process for supplier selection and order allocation. Omega, 36(1), 76-90. Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S. J., Shibin, K., & Wamba, S. F. (2017). Sustainable supply chain management: Framework and further research directions. Journal of Cleaner Production, 142, 1119-1130. Ehsani, E., Kazemi, N., Olugu, E. U., Grosse, E. H., & Schwindl, K. (2016). Applying fuzzy multi-objective linear programming to a project management decision with nonlinear fuzzy membership functions. Neural Computing and Applications, 1-14. Emrouznejad, A., & Shale, E. (2009). A combined neural network and DEA for measuring efficiency of large scale datasets. Computers & Industrial Engineering, 56(1), 249-254. Emrouznejad, A., & Yang, G.-l. (2017). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences. Fallahpour, A., Amindoust, A., Antuchevičienė, J., & Yazdani, M. (2016). Nonlinear genetic-based model for supplier selection: a comparative study. Technological and Economic Development of Economy, 1-18. Fallahpour, A., Amindoust, A., Antuchevičienė, J., & Yazdani, M. (2017a). Nonlinear genetic-based model for supplier selection: a comparative study. Technological and Economic Development of Economy, 23(1), 178-195. Fallahpour, A., Olugu, E. U., & Musa, S. N. (2017b). A hybrid model for supplier selection: integration of AHP and multi expression programming (MEP). Neural Computing and Applications, 28(3), 499-504. Fallahpour, A., Olugu, E. U., Musa, S. N., Khezrimotlagh, D., & Wong, K. Y. (2015). An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach. Neural Computing and Applications, 1-19. Fallahpour, A., Olugu, E. U., Musa, S. N., Khezrimotlagh, D., & Wong, K. Y. (2016). An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach. Neural Computing and Applications, 27(3), 707-725. Fallahpour, A., Olugu, E. U., Musa, S. N., Wong, K. Y., & Noori, S. (2017). A decision support model for sustainable supplier selection in sustainable supply chain management. Computers & Industrial Engineering, 105, 391-410. Geng, Y., Chen, J., Fu, R., Bao, G., & Pahlavan, K. (2016). Enlighten wearable physiological monitoring systems: On-body rf characteristics based human motion classification using a support vector machine. IEEE Transactions on Mobile Computing, 15(3), 656-671. Ghasemi, M.-R., Ignatius, J., Lozano, S., Emrouznejad, A., & Hatami-Marbini, A. (2015). A fuzzy expected value approach under generalized data envelopment analysis. Knowledge-Based Systems, 89, 148-159. Ghosh, A., & Chatterjee, P. (2010). Prediction of cotton yarn properties using support vector machine. Fibers and Polymers, 11(1), 84-88. GüNeri, A. F., Ertay, T., & YüCel, A. (2011). An approach based on ANFIS input selection and modeling for supplier selection problem. Expert Systems with Applications, 38(12), 14907-14917. Gupta, H., & Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 152, 242-258. Hanafizadeh, P., Khedmatgozar, H. R., Emrouznejad, A., & Derakhshan, M. (2014). Neural network DEA for measuring the efficiency of mutual funds. International Journal of Applied Decision Sciences, 7(3), 255-269. He, J., Ma, C., & Pan, K. (2017). Capacity investment in supply chain with risk averse supplier under risk diversification contract. Transportation Research Part E: Logistics and Transportation Review, 106, 255-275. Hosseinzadeh-Bandbafha, H., Nabavi-Pelesaraei, A., Khanali, M., Ghahderijani, M., & Chau, K.-W. (2017). Application of data envelopment analysis approach for optimization of energy use and reduction of greenhouse gas emission in peanut production of Iran. Journal of Cleaner Production. Ignatius, J., Ghasemi, M.-R., Zhang, F., Emrouznejad, A., & Hatami-Marbini, A. (2016). Carbon efficiency evaluation: An analytical framework using fuzzy DEA. European Journal of Operational Research. Jauhar, S. K., & Pant, M. (2017). Integrating DEA with DE and MODE for sustainable supplier selection. Journal of Computational Science. Johnsen, T. E., Miemczyk, J., & Howard, M. (2017). A systematic literature review of sustainable purchasing and supply research: Theoretical perspectives and opportunities for IMP-based research. Industrial Marketing Management, 61, 130-143. Kaboli, S. H. A., Fallahpour, A., Kazemi, N., Selvaraj, J., & Rahim, N. (2016). An expression-driven approach for long-term electric power consumption forecasting. Kanal, L. N., & Lemmer, J. F. (2014). Uncertainty in artificial intelligence (Vol. 4). Elsevier. Karkevandi-Talkhooncheh, A., Hajirezaie, S., Hemmati-Sarapardeh, A., Husein, M. M., Karan, K., & Sharifi, M. (2017). Application of adaptive neuro fuzzy interface system optimized with evolutionary algorithms for modeling CO 2-crude oil minimum miscibility pressure. Fuel, 205, 34-45. Karsak, E. E., & Dursun, M. (2016). Taxonomy and review of non-deterministic analytical methods for supplier selection. International Journal of Computer Integrated Manufacturing, 29(3), 263-286. Kazemi, N., Abdul-Rashid, S. H., Shekarian, E., Bottani, E., & Montanari, R. (2016a). A fuzzy lot-sizing problem with two-stage composite human learning. International Journal of Production Research, 54(16), 5010-5025. Kazemi, N., Ehsani, E., & Glock, C. H. (2014). Multi-objective supplier selection and order allocation under quantity discounts with fuzzy goals and fuzzy constraints. International Journal of Applied Decision Sciences, 7(1), 66-96. Kazemi, N., Ehsani, E., Glock, C. H., & Schwindl, K. (2015a). A mathematical programming model for a multi-objective supplier selection and order allocation problem with fuzzy objectives. International Journal of Services and Operations Management, 21(4), 435-465. Kazemi, N., Ehsani, E., & Jaber, M. (2010). An inventory model with backorders with fuzzy parameters and decision variables. International Journal of Approximate Reasoning, 51(8), 964-972. Kazemi, N., Olugu, E. U., Abdul-Rashid, S. H., & Ghazilla, R. A. B. R. (2015b). Development of a fuzzy economic order quantity model for imperfect quality items using the learning effect on fuzzy parameters. Journal of Intelligent & Fuzzy Systems, 28(5), 2377-2389. Kazemi, N., Olugu, E. U., Abdul-Rashid, S. H., & Ghazilla, R. A. R. (2016b). A fuzzy EOQ model with backorders and forgetting effect on fuzzy parameters: An empirical study. Computers & Industrial Engineering, 96, 140-148. 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. Kumar, D., Rahman, Z., & Chan, F. T. (2017). A fuzzy AHP and fuzzy multi-objective linear programming model for order allocation in a sustainable supply chain: A case study. International Journal of Computer Integrated Manufacturing, 30(6), 535-551. Kuo, R., Wang, Y., & Tien, F. (2010). Integration of artificial neural network and MADA methods for green supplier selection. Journal of Cleaner Production, 18(12), 1161-1170. Kwon, H.-B. (2017). Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling. International Journal of Production Economics, 183, 159-170. Lee, M.-C., & To, C. (2010). Comparison of support vector machine and back propagation neural network in evaluating the enterprise financial distress. International Journal of Artificial Intelligence & Applications (IJAIA), 1(3), 1007-5133. Liao, C.-N., & Kao, H.-P. (2011). An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Systems with Applications, 38(9), 10803-10811. Lima, F. R., Osiro, L., & Carpinetti, L. C. R. (2013). A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules. Applied Soft Computing, 13(10), 4133-4147. Liu, Q., & Lim, S. H. (2017). Toxic air pollution and container port efficiency in the USA. Maritime Economics & Logistics, 19(1), 94-105. Liu, T., Deng, Y., & Chan, F. (2017). Evidential supplier selection based on DEMATEL and game theory. International Journal of Fuzzy Systems, 1-13. Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686-1698. Malviya, R. K., & Kant, R. (2015). Green supply chain management (GSCM): A structured literature review and research implications. Benchmarking: An International Journal, 22(7), 1360-1394. Misiunas, N., Oztekin, A., Chen, Y., & Chandra, K. (2016). DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status. Omega, 58, 46-54. Modak, N. M., Panda, S., & Sana, S. S. (2016a). Pricing policy and coordination for a two-layer supply chain of duopolistic retailers and socially responsible manufacturer. International Journal of Logistics Research and Applications, 19(6), 487-508. Modak, N. M., Panda, S., & Sana, S. S. (2016b). Two-echelon supply chain coordination among manufacturer and duopolies retailers with recycling facility. The International Journal of Advanced Manufacturing Technology, 87(5-8), 1531-1546. Modhej, D., Sanei, M., Shoja, N., & Hosseinzadeh Lotfi, F. (2017). Integrating inverse data envelopment analysis and neural network to preserve relative efficiency values. Journal of Intelligent & Fuzzy Systems(Preprint), 1-12. Mohammady Garfamy, R. (2006). A data envelopment analysis approach based on total cost of ownership for supplier selection. Journal of Enterprise Information Management, 19(6), 662-678. Mokhtari, M., Javanshir, H., Kamali, D., Tashakori, L., & Maadanchi, F. (2013). Supplier selection in texture industry by using fuzzy MIDM. Research Journal of Applied Sciences, Engineering and Technology, 6(3), 400-411. Montanari, R., Bottani, E., Shekarian, E., & Kazemi, N. (2017). A model for the analysis of procurement strategies in the economic order interval environment. Mathematics and Computers in Simulation, 134, 79-98. Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2017). A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design, 121, 237-253. Mousavi, S. M., Mostafavi, E. S., & Hosseinpour, F. (2014). Gene expression programming as a basis for new generation of electricity demand prediction models. Computers & Industrial Engineering, 74, 120-128. Mukherjee, K. (2016). Supplier selection criteria and methods: past, present and future. International Journal of Operational Research, 27(1-2), 356-373. Nurwaha, D., & Wang, X. (2011). Prediction of rotor spun yarn strength using support vector machines method. Fibers and Polymers, 12(4), 546-549. Önüt, S., Kara, S. S., & Işik, E. (2009). Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company. Expert Systems with Applications, 36(2), 3887-3895. Opricovic, S. (2016). A comparative analysis of the DEA-CCR model and the VIKOR method. Yugoslav Journal of Operations Research, 18(2). Oum, T. H., Pathomsiri, S., & Yoshida, Y. (2013). Limitations of DEA-based approach and alternative methods in the measurement and comparison of social efficiency across firms in different transport modes: An empirical study in Japan. Transportation Research Part E: Logistics and Transportation Review, 57, 16-26. Ozcan, Y. A. (2008). Health care benchmarking and performance evaluation: an assessment using Data Envelopment Analysis (DEA). Berlin: Springer. Pagell, M., & Wu, Z. (2017). Business implications of sustainability practices in supply chains Sustainable Supply Chains. Springer, 339-353. Pant, Y., Xiao, Z., Wang, X., & Yang, D. (2017). A multiple support vector machine approach to stock index forecasting with mixed frequency sampling. Knowledge-Based Systems, 122, 90-102. Panda, S., Modak, N. M., & Cárdenas-Barrón, L. E. (2017). Coordinating a socially responsible closed-loop supply chain with product recycling. International Journal of Production Economics, 188, 11-21. Paradi, J. C., Sherman, H. D., & Tam, F. K. (2018). Bank branch productivity applications: Strategic branch management issues addressed with DEA data envelopment analysis in the financial services industry. Springer, 129-143. PrasannaVenkatesan, S., & Goh, M. (2016). Multi-objective supplier selection and order allocation under disruption risk. Transportation Research Part E: Logistics and Transportation Review, 95, 124-142. Raut, R. D., Kamble, S. S., Kharat, M. G., Joshi, H., Singhal, C., & Kamble, S. J. (2017). A hybrid approach using data envelopment analysis and artificial neural network for optimising 3PL supplier selection. International Journal of Logistics Systems and Management, 26(2), 203-223. Rejani, Y., & Selvi, S. T. (2009). Early detection of breast cancer using SVM classifier technique. arXiv preprint arXiv, 0912-2314. Saberi, M., Rostamia, M. R., Hamidianb, M., & Aghamic, N. (2016). Forecasting the profitability in the firms listed in Tehran Stock Exchange using data envelopment analysis and artificial neural network. Santin, D. (2008). On the approximation of production functions: A comparison of artificial neural networks frontiers and efficiency techniques. Applied Economics Letters, 15(8), 597-600. Sarkar, S., & Sarkar, S. (2017). A modified multiplier model of BCC DEA to determine cost-based efficiency. Benchmarking: An International Journal, 24(6), 1508-1522. Sgurev, V., Yager, R. R., Kacprzyk, J., & Atanassov, K. T. (2017). Recent contributions in intelligent systems. Springer. Shekarian, E., Jaber, M. Y., Kazemi, N., & Ehsani, E. (2014). A fuzzified version of the economic production quantity (EPQ) model with backorders and rework for a single-stage system. European Journal of Industrial Engineering, 8(3), 291-324. Shekarian, E., Kazemi, N., Rashid, S. H. A., & Olugu, E. U. (2017). Fuzzy inventory models: A comprehensive review. Applied Soft Computing. Shirazi, A. Z., & Mohammadi, Z. (2017). A hybrid intelligent model combining ANN and imperialist competitive algorithm for prediction of corrosion rate in 3C steel under seawater environment. Neural Computing and Applications, 28(11), 3455-3464. Tavana, M., Fallahpour, A., Di Caprio, D., & Santos-Arteaga, F. J. (2016a). A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection. Expert Systems with Applications, 61, 129-144. Tavana, M., Li, Z., Mobin, M., Komaki, M., & Teymourian, E. (2016b). Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS. Expert Systems with Applications, 50, 17-39. Vahdani, B., Iranmanesh, S., Mousavi, S. M., & Abdollahzade, M. (2012). A locally linear neuro-fuzzy model for supplier selection in cosmetics industry. Applied Mathematical Modelling, 36(10), 4714-4727. Vahdani, B., Razavi, F., & Mousavi, S. M. (2016). A high performing meta-heuristic for training support vector regression in performance forecasting of supply chain. Neural Computing and Applications, 27(8), 2441-2451. Verma, M., & Puri, J. G. (2017). DEA-MCDM approach for ranking decision making units using OWA aggregation operators. Vlahogianni, E. I., Kepaptsoglou, K., & Karlaftis, M. G. (2016). Modelling the performance of the Athens Bus Network using data envelopment analysis and neural network regression. Journal of Transport Economics and Policy (JTEP), 50(4), 369-383. Wan, X.-l., Zhang, Z., Rong, X.-x., & Meng, Q.-c. (2016). Exploring an interactive value-adding data-driven model of consumer electronics supply chain based on least squares support vector machine. Scientific Programming, 2016, 4. Wetzstein, A., Hartmann, E., Benton Jr, W., & Hohenstein, N.-O. (2016). A systematic assessment of supplier selection literature–state-of-the-art and future scope. International Journal of Production Economics, 182, 304-323. Wu, D. (2009). Supplier selection: A hybrid model using DEA, decision tree and neural network. Expert Systems with Applications, 36(5), 9105-9112. Xu, Y., Zhang, X., & Zhang, H. (2016). Research on the e-commerce platform performance and green supply chain based on data mining and SVM. International Journal of Database Theory and Application, 9(12), 141-150. Yayla, A. Y., Yildiz, A., & Ozbek, A. (2012). Fuzzy TOPSIS method in supplier selection and application in the garment industry. Fibres & Textiles in Eastern Europe. Yoon, J., Talluri, S., Yildiz, H., & Ho, W. (2017). Models for supplier selection and risk mitigation: a holistic approach. International Journal of Production Research, 1-26. Yousefi, A., & Hadi-Vencheh, A. (2016). Selecting Six Sigma projects: MCDM or DEA? Journal of Modelling in Management, 11(1), 309-325. Yu, C., Wong, T., & Li, Z. (2017). A hybrid multi-agent negotiation protocol supporting supplier selection for multiple products with synergy effect. International Journal of Production Research, 55(1), 18-37. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. Zhou, J., & Yao, X. (2017). A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition. The International Journal of Advanced Manufacturing Technology, 88(9-12), 3371-3387. Zimmer, K., Fröhling, M., & Schultmann, F. (2016). Sustainable supplier management–a review of models supporting sustainable supplier selection, monitoring and development. International Journal of Production Research, 54(5), 1412-1442. Zuo, K., & Guan, J. (2017). Measuring the R&D efficiency of regions by a parallel DEA game model. Scientometrics, 1-20. | ||
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