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ارزیابی تامین‌کنندگان در شرایط عدم اطمینان با ترکیب روش های دمپستر شافر و تکنیک‌های تصمیم‌گیری چندمعیاره | ||
| پژوهشنامه مدیریت اجرایی | ||
| مقاله 4، دوره 11، شماره 22، بهمن 1398، صفحه 115-150 اصل مقاله (1.94 M) | ||
| نوع مقاله: مدیریت تولید و عملیات (برنامه‌ریزی تولید، چابکی، پایداری، ناب، سبز، برون‌سپاری، زنجیره تأمین، زنجیره ارزش، کیفیت، بهره وری، صنعت چهار و ابعاد آن، . . .) | ||
| شناسه دیجیتال (DOI): 10.22080/jem.2020.17571.3031 | ||
| نویسندگان | ||
| حمیدرضا فلاح لاجیمی* 1؛ زهرا جعفری2 | ||
| 1استادیار گروه مدیریت صنعتی، دانشکده علوم اقتصادی و اداری، دانشگاه مازندران، بابلسر، ایران | ||
| 2دانشجوی کارشناسی ارشد مدیریت صنعتی- تحقیق در عملیات، دانشکده مدیریت ، دانشگاه تهران، تهران، ایران | ||
| تاریخ دریافت: 19 آبان 1398، تاریخ بازنگری: 03 دی 1398، تاریخ پذیرش: 13 بهمن 1398 | ||
| چکیده | ||
| کی از تصمیمات کلیدی در سازمان‌ها، انتخاب تامین‌کننده می‌باشد. معمولا فهرستی از تامین‌کنندگان شناسایی شده برمبنای مجموعه‌ای از شاخص ها، ارزیابی می شوند، که این موضوع نشان از یک مسئله تصمیم‌گیری چندمعیاره است. روش‌های متعدد و شاخص‌های چندگانه‌ای برای ارزیابی تامین‌کنندگان وجود دارد. ماهیت چنین تصمیم‌گیری‌هایی در شرایط عدم اطمینان پیچیده است. هدف این پژوهش ارائه روشی برای ارزیابی تامین کنندگان در شرایط عدم اطمینان است. در این تحقیق شاخص های ارزیابی از ماتریس بالقوه تامین کنندگان استخراج و پس از تعیین اهمیت شاخص‌ها با استفاده از روش بهترین-بدترین، درجه عدم اطمینان با تحلیل رابطه‌ای خاکستری محاسبه و در نهایت برای رتبه‌بندی تامین‌کنندگان از روش دمپستر شافر استفاده شده است. داده های تحقیق از خبرگان صنعت سخت افزار، که از دانش و سابقه کافی در حوزه خرید و تدارکات برخوردار بوده، جمع آوری شده است. یک مطالعه موردی واقعی برای نشان دادن رویکرد ترکیبی پیشنهاد داده شده برای انتخاب تامین کننده ارائه شده است. در بعد توانمندی، شاخص‌های کیفیت و ظرفیت انبار و در بعد تمایل، شاخص‌های تمایل به نشر اطلاعات وتوافق متقابل به ترتیب مهمترین و کم اهمیت ترین شاخص می باشند. در نهایت رتبه بندی تامین کنندگان در شرایط عدم اطمینان انجام شد. در یافته های تحقیق شاخص های ارائه شده عمومی بوده و می تواند در صنایع و شرایط مختلف مورد استفاده قرار گیرد. همچنین از تحلیل رابطه ای خاکستری جهت محاسبات عدم اطمینان استفاده شده است. | ||
| کلیدواژه‌ها | ||
| ارزیابی تامین‌کننده؛ تحلیل رابطه‌ای خاکستری؛ دمپستر شافر؛ روش بهترین-بدترین؛ ماتریس بالقوه تامین‌کننده | ||
| عنوان مقاله [English] | ||
| Suppliers Evaluation in Uncertain Conditions by Combining Dempster Shafer and Multi-Criteria Decision Making Techniques | ||
| نویسندگان [English] | ||
| Hamidreza Fallah Lajimi1؛ Zahra Jafari Soruni2 | ||
| 1Assistant Professor, Management Department, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran | ||
| 2MSc. Student, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| One of the key decisions in organizations is the selection of suppliers. Typically, a list of identified suppliers is evaluated based on a set of criteria, which indicates a multi-criteria decision-making problem. There are multiple methods and multiple criteria for evaluating suppliers. The nature of such decisions is complex in conditions of uncertainty. The purpose of this study is to provide a method for evaluating suppliers in conditions of uncertainty. In this research, evaluation criteria are extracted from the supplier potential matrix, and after determining the importance of criteria using the Best-Worst Method, the degree of uncertainty is calculated with Gray Relational analysis and finally, Dempster-Shafer method is used to rank suppliers. Research data has been gathered from hardware industry experts, with knowledge and experience in purchasing and procurement. A real case study is presented to illustrate the proposed hybrid approach to supplier selection. In the capability dimension, quality and capacity of the warehouse and in the willingness dimension, willingness to share information and mutual interaction are respectively the best and worst indicators. Also, the ranking of suppliers was conducted in conditions of uncertainty. Research results are important from a few perspectives. First, the criteria presented are public and can be used in different industries and conditions. Second, gray relational analysis is used to calculate uncertainty, which is due to the use of less data. | ||
| کلیدواژه‌ها [English] | ||
| Supplier evaluation, Grey relational analysis (GRA), Dempster-Shafer, Best Worst Method (BWM), Supplier potential matrix (SPM) | ||
| مراجع | ||
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Aggarwal, P., Bhatt, D., Devabhaktuni, V., & Bhattacharya, P. (2013). Dempster Shafer neural network algorithm for land vehicle navigation application. Information Sciences, 253, 26-33. Amin, S. H., Razmi, J., & Zhang, G. (2011). Supplier selection and order allocation based on fuzzy SWOT analysis and fuzzy linear programming. Expert Systems with Applications, 38(1), 334–342 Azadeh, A., & Alem, S. M. (2010). A flexible deterministic, stochastic and fuzzy Data Envelopment Analysis approach for supply chain risk and vendor selection problem: Simulation analysis. Expert Systems with Applications, 37(12), 7438–7448. 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(July), 99–106. Bai, C., Rezaei, J., & Sarkis, J. (2017). Multicriteria Green Supplier Segmentation. IEEE Transactions on Engineering Management, 1–14. Baraldi, P., Compare, M., & Zio, E. (2013). Maintenance policy performance assessment in presence of imprecision based on Dempster–Shafer Theory of Evidence. Information Sciences, 245, 112-131. Büyüközkan, G., & Ifi, 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. Chai, J., Liu, J. N. K., & Ngai, E. W. T. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40(10), 3872–3885. Chen, L. Y., & Wang, T. C. (2009). Optimizing partners’ choice in IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR. International Journal of Production Economics, 120(1), 233–242. Chitsaz, N., & Azarnivand, A. (2017). Water Scarcity Management in Arid Regions Based on an Extended Multiple Criteria Technique. Water Resources Management, 31(1), 233–250. De Oliveira Silva, L. G., & de Almeida-Filho, A. T. (2016). A multicriteria approach for analysis of conflicts in evidence theory. Information Sciences, 346, 275-285. Deng, X., Hu, Y., Deng, Y., & Mahadevan, S. (2014). Supplier selection using AHP methodology extended by D numbers. Expert Systems with Applications, 41(1), 156–167. Fallah Lajimi, H., Mohammadi Kani, Z., and Rasooli Khatir, Z. (2019). Applying of Piecewise Linear Value Functions in LARG Suppliers Ranking: Multi-Criteria Decision Making Mixed Approach. Industrial management vision Quarterly, 33, 115-140. (In Persian). Galankashi, M. R., Helmi, S. A., & Hashemzahi, P. (2016). Supplier selection in automobile industry: A mixed balanced scorecard-fuzzy AHP approach. Alexandria Engineering Journal, 55(1), 93–100. Guo, C., Liu, X., Jin, M., & Lv, Z. (2016). The research on optimization of auto supply chain network robust model under macroeconomic fluctuations. Chaos, Solitons & Fractals, 89, 105–114. 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. Haleh, H., & Hamidi, A. (2011). A fuzzy MCDM model for allocating orders to suppliers in a supply chain under uncertainty over a multi-period time horizon. Expert Systems with Applications, 38(8), 9076–9083. Julong, D. (1989). Introduction to grey system theory. The Journal of Grey System. Kannan, D., Jabbour, A. B. L. de S., & 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. Kannan, V. R., & Tan, K. C. (2002). Supplier selection and assessment: Their impact on business performance. Journal of supply chain management, 38(3), 11-21. Kar, A. K. (2015). A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network. Journal of Computational Science, 6, 23–33. Kellner, F., Lienland, B., & Utz, S. (2019). An a posteriori decision support methodology for solving the multi-criteria supplier selection problem. European Journal of Operational Research, 272(2), 505-522. Kuo, R. J., & Lin, Y. J. (2012). Supplier selection using analytic network process and data envelopment analysis. International Journal of Production Research, 50(11), 2852–2863. Lee, C. C., & Ou-Yang, C. (2009). A neural networks approach for forecasting the supplier’s bid prices in supplier selection negotiation process. Expert Systems with Applications, 36(2 PART 2), 2961–2970. Li, G.D., Yamaguchi, D. and Nagai, M., 2007. A grey-based decision-making approach to the supplier selection problem. Mathematical and computer modelling, 46(3), pp.573-581. 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. Lima-Junior, F. R., & Carpinetti, L. C. R. (2016). Combining SCOR model and fuzzy TOPSIS for supplier evaluation and management. International Journal of Production Economics, 174, 128–141. Lin, T. C. (2010). Switching-based filter based on Dempster’s combination rule for image processing. Information Sciences, 180(24), 4892-4908. Mızrak Özfirat, P., Tuna Taşoglu, G., & Tunçel Memiş, G. (2014). A fuzzy analytic hierarchy process methodology for the supplier selection problem. Journal of Enterprise Information Management, 27(3), 292–301. Mukherjee, S., & Kar, S. (2013). A three phase supplier selection method based on fuzzy preference degree. Journal of King Saud University. Computer and Information Sciences, 25(2), 173–185. Nazari-Shirkouhi, S., Shakouri, H., Javadi, B., & Keramati, A. (2013). Supplier selection and order allocation problem using a two-phase fuzzy multi-objective linear programming. Applied Mathematical Modelling, 37(22), 9308–9323. Qin, J., Liu, X., & Pedrycz, W. (2017). 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. Rezaeenour, J., Gazanfarinasrabad, M., Doroudi, A. (2017). Bulls-eye -Bulls-eye, developed method for MCDM with gray numbers for selecting the equipment supplier (case study selection and purchase of hospital equipment. Industrial Management Journal, 8(4), 601-624 (in Persian). Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130. Rezaei, J., & Ortt, J. R. (2011, September). Two multi-criteria approaches to supplier segmentation. In IFIP International Conference on Advances in Production Management Systems (pp. 317-325). Springer, Berlin, Heidelberg. 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. Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 135, 577–588. Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications. 42(23), 9152-9164. Safaei Ghadikolaei, A., Madhoshi, M., Jamalian, A. (2015). Presenting a Conceptual Model for Sustainable Supplier Selection (A case study in SAIPA supply chain). Industrial Management Journal, 7(4), 767-784 (in Persian). Salimi, N. (2017). Quality assessment of scientific outputs using the BWM. Scientometrics, 112(1), 195–213. Salimi, N., & Rezaei, J. (2018). Evaluating firms’ R&D performance using best worst method. Evaluation and Program Planning, 66, 147–155. Scott, J., Ho, W., Dey, P. K., & Talluri, S. (2015). A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments. International Journal of Production Economics, 166, 226–237. Sevastjanov, P., & Dymova, L. (2015). Generalised operations on hesitant fuzzy values in the framework of Dempster–Shafer theory. Information Sciences, 311, 39-58. Simić, D., Kovačević, I., Svirčević, V., & Simić, S. (2016). 50 years of fuzzy set theory and models for supplier assessment and selection: A literature review. Journal of Applied Logic. 24, 85-96. Tang, H. (2015). A novel fuzzy soft set approach in decision making based on grey relational analysis and Dempster–Shafer theory of evidence. Applied Soft Computing, 31, 317-325. Torabi, S. A., Giahi, R., & Sahebjamnia, N. (2016). An enhanced risk assessment framework for business continuity management systems. Safety Science, 89, 201–218. Valipour Parkouhi, S., & Safaei Ghadikolaei, A. (2017). A resilience approach for supplier selection: Using Fuzzy Analytic Network Process and grey VIKOR techniques. Journal of Cleaner Production, 161, 431–451. Wang, J. W., Cheng, C. H., & Huang, K. C. (2009). Fuzzy hierarchical TOPSIS for supplier selection. Applied Soft Computing Journal, 9(1), 377–386. Wood, D. A. (2016). Supplier selection for development of petroleum industry facilities, applying multi-criteria decision making techniques including fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting. Journal of Natural Gas Science and Engineering, 28, 594–612. Wu, Y., Chen, K., Zeng, B., Xu, H., & Yang, Y. (2016). Supplier selection in nuclear power industry with extended VIKOR method under linguistic information. Applied Soft Computing, 48, 444–457. Zakeri, S., & Keramati, M. A. (2015). Systematic combination of fuzzy and grey numbers for supplier selection problem. Grey Systems: Theory and Application, 5(3), 313–343. Zhang, J., & Tu, G. (2004). A new method to deal with the conflicts in the DS evidence theory. Statistics and Decision, 7, 21-22. Zhang, X., Jin, F., & Liu, P. (2013). A grey relational projection method for multi-attribute decision making based on intuitionistic trapezoidal fuzzy number. Applied Mathematical Modelling, 37(5), 3467–3477. | ||
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آمار تعداد مشاهده مقاله: 774 تعداد دریافت فایل اصل مقاله: 808 |
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