• Home
  • Supplier Ranking Using Data Envelopment Analysis and New Cross Efficiency Evaluation in the Presence of Undesirable Outputs

Share To

Article Url


Manuscript ID : JNRM-1912-1811 (R1) Visit : 198 Page: 35 - 58

Article Type: Original Research

Supplier Ranking Using Data Envelopment Analysis and New Cross Efficiency Evaluation in the Presence of Undesirable Outputs

Subject Areas : Statistics

Mehdi Soltanifar 1 , Hamid SHarafi 2 , Seyyed Mohammad Zargar 3 , Mehdi Homayounfar 4

1 - Islamic Azad Univwrsity, Semnan Branch, Semnan, Iran.
2 - Graduated in Applied Mathematics, Islamic Azad University, Science and Research Branch, Tehran, Iran.
3 - Assistant Professor, Department of Management, Semnan Branch, Islamic Azad University, Semnan, Iran.
4 - Assistant Professor, Department of Management, Rasht Branch, Islamic Azad University, Rasht, Iran

Received: 2020-01-14 Accepted : 2020-06-08 Published : 2021-09-23

Keywords: روش کارایی متقاطع, تحلیل پوششی داده ها, خروجی های نامطلوب, روش تاپسیس, رای گیری ترجیحی,

Abstract :

Data envelopment analysis is a linear programming-based approach used to evaluate the relative performance of decision making units (DMU's) that perform the same tasks with multiple inputs and multiple outputs. Due to the optimistic view of the DEA in evaluating the performance of homogeneous decision making units, multiple units with a maximum relative efficiency score (equal to unit) are highly likely. Therefore, ranking models were presented to distinguish between efficient units. Cross efficiency evaluation is one of the most useful tools for ranking DMUs in data envelopment analysis. This model has two major flaws in implementation. First, it yields different results in the presence of optimal alternatives; and second, there is no compelling reason to use the arithmetic mean to integrate the cross-performance matrix results. In this paper, a new approach, inspired by the preferential voting process and the idea proposed in the TOPSIS method, is presented to combine cross-performance results in the presence of undesirable outputs. The results are then used to rank suppliers in the presence of undesirable outputs.

References:


  1. Gupta P, Govindan K, Mehlawat MK, Kumar S (2016) Aweighted possibilistic programming approach for sustainable vendor selection and order allocation in fuzzy environment. The International Journal of Advanced Manufacturing Technology. 86(58):1785–1804.
    2.

 

  1. Kirschstein, T., Meisel, F. (2019). A multi-period multi-commodity lot-sizing problem with supplier selection, storage selection and discounts for the process industry. European Journal of Operational Research. 279: 393-406.
    2.

 

  1. Jauhar, S. K. & Pant, M. (2017). Integrating DEA with DE and MODE for sustainable supplier selection. Journal of Computational Science. Volume 21, July 2017, Pages 299-306.
    2.

 

  1. Kannan D, de Sousa Jabbour ABL, Jabbour CJC (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.
    2.

 

  1. Dobos, I., Vörösmarty, Gyö. (2018). Inventory-related costs in green supplier selection problems with Data Envelopment Analysis (DEA), International Journal of Production Economics (2018), doi: 10.1016/ j.ijpe. 2018.03.022.
    2.

 

  1. Zarbakhshnia, N., Jamali Jaghdani, (2018). Sustainable supplier evaluation and selection with a novel two stage DEA model in the presence of uncontrollable inputs and undesirable outputs: a plastic case study. The International Journal of Advanced Manufacturing Technology. 97 (5-8): 2933-2945.
    2.

 

  1. Bai, ch., Kusi-Sarpong, S., Badri



Ahmadi H., Sarkis J. (2019): Social sustainable supplier evaluation and selection: a group decision-support approach, International Journal of Production Research, 2019.1574042.

 

  1. همایون‌فر، مهدی؛ امیر تیموری، علیرضا (1398). ارزیابی عملکرد متوازن تأمین‌کنندگان با رویکرد ترکیبی دیماتل- تحلیل پوششی داده‌ها در حضور عوامل نامطلوب. پژوهش‌های نوین در ریاضی. 5 (18): 31-48.
    2.

 

  1. Badorf, F., Wagner, S. M., Hoberg, K.,Papier, F. (2019), How Supplier Economies of Scale Drive Supplier Selection Decisions, Journal of Supply Chain Management, Vol. 55, Issue3, July 2019, Pages 45-67.
    2.

 

  1. Anderson, T. R., Hollingsworth, K. B., Inman, L. B., (2002). The fixed weighting nature of a cross evaluation model. Journal of Productivity Analysis, 18(1), 249–255.
    2.

 

  1. Rashidi, K., Cullinane, K., (2018). A Comparison of Fuzzy DEA and Fuzzy TOPSIS in Sustainable Supplier Selection: Implications for Sourcing Strategy, Expert Systems With Applications, doi: https:// doi.org/ 10.1016/ j.eswa.2018.12.025.
    2.

 

  1. Van Weele, A. J. (2014). Purchasing and supply chain management: Analysis, strategy, planning and practice. (6th ed.) Cengage Learning EMEA.
    2.

 

  1. Chai, J., Ngai, E. W.T. (2019). Decision-Making Techniques in Supplier Selection: Recent Accomplishments and What Lies Ahead, Expert Systems With Applications (2019), doi: https://doi.org/ 10.1016/ j.eswa.2019.112903.
    2.

 

  1. Alikhani, R. Torabi, S.A. Altay N, Strategic supplier selection under sustainability and risk criteria, International Journal of Production Economics (2018), doi: 10.1016/ j.ijpe. 2018.11.018.
    2.

 

  1. Wu, M.Y., Weng, Y.C. 2010. A study of supplier selection factors for high-tech industries in the supply chain. Total Quality Management, 21(4), 391-413.
    2.

 

  1. Ho, W., Xu, X., 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.
    2.

 

  1. Ellram, L.M. 1995. Total cost of ownership: an analysis approach for purchasing. International Journal of Physical Distribution & Logistics Management, 25(8), 4-23.
    2.

 

  1. 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.
    2.

 

  1. Lehman, D., & O'Shaughnessy, J. (1982). Decision criteria used in buying different categories of products. Journal of purchasing and materials management, 18(1), pp.9-14.
    2.

 

  1. Kiser, G. E., Rao, C. P., & Rao, S. R. (1975). Vendor Attribute Evaluations of Buying Center Members Other Than Purchasing Executives. Industrial Marketing Management, 4: pp_45-54.
    2.

 

  1. Dempsy, W. A. (1978). Vendor selection and the buying process. Industrial mraketing management, 7(4), pp. 257-267.
    2.

 

  1. Bilisik, M. E., Caglar, N., & Bilisik, O. N. (2012). A comparative performance analyze model and supplier positioning in performance maps for supplier selection and evaluation. Procedia - Social and



Behavioral Sciences, 58: 1434 – 1442.

 

  1. باورصاد، بلقیس، گنجعلی، سمیه، رحیمی، فرج الله، مهرابی، علی (1395). الگوی فرآیندی ارتقای عملکرد مالی شرکت بر اساس تولید به هنگام، چابکی و مدیریت کیفیت جامع. مطالعات مدیریت راهبردی. 27 : 107-123.
    2.

 

  1. 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.
    2.

 

  1. Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision making techniques and applications–Two decades review from 1994 to 2014. Expert Systems with Applications, 42, 4126-4148.
    2.

 

  1. Ghaemi Nasab, F., Rostamy-Malkhalifeh, M., (2010) Extension of TOPSIS for Group Decision-Making Based on the Type-2 Fuzzy Positive and Negative Ideal Solutions, Int. J. Industrial Mathematics Vol. 2, No. 3 (2010) 199-213.
    2.

 

  1. سیدبویر، سهیلا؛ مقبولی، مهناز، مطرود، فاطمه (1398). خروجی‌های با تأخیر زمانی: مدلی مبتنی بر تحلیل پوششی داده‌ها. پژوهش‌های نوین در ریاضی. 5 (20): 71- 80.
    2.

 

  1. محمدنژاد چاری، فاطمه؛ صفایی قادیکلایی، عبدالحمید (1395). شناسایی و رتبه بندی معیارهای انتخاب تامین کنندگان در زنجیره تامین لارج (مطالعه موردی: صنایع غذایی و لبنی کاله). مجله تحقیق در عملیات در کاربردهای آن. 13 (4): 102- 120.
    2.

 

  1. Rahmani, A., Hosseinzadeh Lotfi, F., Rostamy-Malkhalifeh, M., Allahviranloo, T., (2016). A New Method for Defuzzification and Ranking of Fuzzy Numbers Based on the Statistical Beta Distribution, Advances in Fuzzy Systems
    2.

Volume 2016, Article ID 6945184, 8 pages.

 

  1. عزیزی، حسین؛ امیرتیموری، علیرضا؛ فرضی پورصائن، رضا (1396). انتخاب تأمین‌کننذه بزاساس دیدگاه­های خوشبینانه و بدبینانه. مجله مدیریت توسعه و تحول. 31: 11-20.
    2.

 

  1. دودکانلوی میلان، مهران، جعفرزاده قوشچی، سعید (1396). ارایه مدلی یکپارچه برای ارزیابی و انتخاب تامین کنندگان بر مبنای زیان معیارها و ساختار ترجیحی تصمیم گیرنده. تحقیق در عملیات در کاربردهای آن. 14 (4): 45-65.
    2.

 

  1. فاضلی فارسانی, مهین, ذیگلری, فاطمه, اسدی, شهرام. (1394). بررسی عملکرد تامین کنندگان کالا و پیمانکاران زنجیره تامین شرکت گاز استان چهارمحال و بختیاری با استفاده از روش تحلیل پوششی داده ها. فصلنامه علمی پژوهشی پژوهش­های مدیریت راهبردی، 21(58): 101-116.
    2.

 

  1. کرباسیان, مهدی, جوانمردی, محمد, خبوشانی, اعظم, زنجیرچی, محمود. (1390). کاربرد مدل (ISM) جهت سطح‌بندی شاخص‌های انتخاب تامین‌کنندگان چابک و رتبه‌بندی تامین‌کنندگان با استفاده از روشTOPSIS-AHP فازی. مدیریت تولید و عملیات. 2 (1): 107-134.
    2.

 

  1. Fei, L., Deng, Y., Hu, Y. (2018). DS-VIKOR: A New Multi-criteria Decision-Making Method for Supplier Selection. International Journal of Fuzzy Systems, 21 (1): 157-175.
    2.

 

  1. Jauhar SK, Pant M, Abraham A (2014) A novel approach for sustainable supplier selection using differential evolution: a case on pulp and paper industry. In Intelligent Data analysis and its Applications, Volume II (pp. 105-117). Springer, Cham
    2.

 

  1. Raut RD, Kamble SS, Kharat MG, Joshi H, Singhal C, Kamble SJ (2017) A hybrid approach using data envelopment analysis and artificial neural network for optimising 3PL supplier selection. Int J Logist Syst Manage 26(2):203–223
    2.

 

  1. Izadikhah M, Saen RF, Ahmadi K (2017) How to assess sustainability of suppliers in volume discount context? A new data envelopment analysis approach. Transp Res Part D: Transp Environ 51: 102–121.
    2.

 

  1. Hatami-Marbini A, Agrell PJ, Tavana M, Khoshnevis P (2017) A flexible cross-efficiency fuzzy data envelopment analysis model for sustainable sourcing. J Clean Prod 142:2761–2779.
    2.

 

  1. Roostaee, R., Izadikhah, M., Hosseinzadeh Lotf, F., Rostamy-Malkhalifeh, M., (2012) A Multi-Criteria Intuitionistic Fuzzy Group Decision Making Method for Supplier Selection with VIKOR Method, International Journal of Fuzzy System Applications, 2(1), 1-17.
    2.

 

  1. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 2:429-44.
    2.

 

  1. Nicole A, Lea F, Zilla SS. Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research 2002; 140:249-65.


 

  1. Andersen P, Petersen NC. A procedure for ranking efficient units in data envelopment analysis. Management Science 1993;39(10):1261-94.
    2.

 

  1. Mehrabian S, Alirezaee MR,



Jahanshahloo GR. A complete efficiency ranking of decision making units in data envelopment analysis. Computational Optimization and Applications 1999; 14:261-6.

 

  1. Torgersen AM, Forsund FR, Kittelsen SAC. Slack-adjusted efficiency measures and ranking of efficient units. The Journal of Productivity Analysis 1996; 7: 379-98.
    2.

 

  1. Friedman L, Sinuany-Stern Z. Scaling units via the canonical correlation analysis and the data envelopment analysis. European Journal of Operational Research 1997;100(3):629-37.
    2.

 

  1. Bardhan I, Bowlin WF, Cooper WW, Sueyoshi T. Models for efficiency dominance in data envelopment analysis. Part I: additive models and MED measures. Journal of the Operations Research Society of Japan 1996;39:322- 32.
    2.

 

  1. Golany B. An interactive MOLP procedure for the extension of data envelopment analysis to effectiveness analysis. Journal of the Operational Research Society 1988; 39(8):725-34.
    2.

 

  1. Sexton TR, Silkman RH, Hogan AJ. Data envelopment analysis: critique and extensions. In: Silkman RH, editor. Measuring efficiency: an assessment of data envelopment analysis, vol. 32. San Francisco: Jossey-Bass; 1986. p. 73-105.
    2.

 

  1. Hosseinzadeh Lotfi, F., Rostamy-Malkhalifeh, M., Aghayi, N., Ghelej Beigi, Z., Gholami, K., (2013) An improved method for ranking alternatives in multiple criteria decision analysis, Applied Mathematical Modelling, 37 (2013) 25-33.
    2.

 

  1. Hosseinzadeh Lotf, F., Navabakhs, M., Tehranian, A., Rostamy-Malkhalifeh, M., Shahverdi., (2007) Ranking Bank Branches with Interval Data The Application of DEA, International Mathematical Forum, 2,2007,no.9,429–440.
    2.

 

  1. Peykania, P., Mohammadi, E., Rostamy-Malkhalifeh, M., Hosseinzadeh Lotf, F., (2019) Fuzzy Data Envelopment Analysis Approach for Ranking of Stocks with an Application to Tehran Stock Exchange, Advances in mathematical finance & applications, 4(1), (2019), 31-43.
    2.

 

  1. Barzegarinegad, A., Jahanshahloo, G., Rostamy-Malkhalifeh, M., (2014) A Full Ranking for Decision Making Units Using Ideal and Anti-Ideal Points in DEA, Hindawi Publishing Corporation, The Scientific World Journal, Volume 2014, Article ID 282939, 8 pages.
    2.

 

  1. Hosseinzadeh Lotfi, F., Jahanshahloo, G. R., Khodabakhshi, M., Rostamy-Malkhlifeh, M., Moghaddas, Z. Vaez-Ghasemi, M., (2013) A Review of Ranking Models in Data Envelopment Analysis, Journal of Applied Mathematics, Volume 2013, Article ID 492421, 20 pages.
    2.

 

  1. Doyle, J., Green, R., (1994). Efficiency and cross efficiency in DEA: Derivations, meanings and the uses. Journal of the Operational Research Society, 45(5), 567 578.
    2.

 

  1. Doyle JR, Green RH. Cross-evaluation in DEA: improving discrimination among DMUs. INFOR 1995; 33: 205-22.
    2.

 

  1. Davtalab-Olyaie, M. (2018): A secondary goal in DEA cross-efficiency evaluation: A “one home run is much better than two doubles” criterion, Journal of the Operational Research Society, Volume 70, 2019 - Issue , Pages 807-816.
    2.

 

  1. Zerafat Angiz, M. Mustafa, A.



Kamali, M. J. Cross-ranking of Decision Making Units in Data Envelopment Analysis, Applied Mathematical Modelling 37 (2013) 398–405.

 

  1. Cook, M. Kress, A. A data envelopment model for aggregating preference rankings, Manage. Sci. 36 (1990) 1302–1310.
    2.

 

  1. Jahanshahloo, G.R., Sanei, M., Rostamy-Malkhalifeh, M., Saleh, H., (2009) A comment on “A fuzzy DEA/AR approach to the selection of flexible manufacturing systems”, Computers & Industrial Engineering, Volume 56, Issue 4, May 2009, Pages 1713-1714.
    2.

 

  1. M. Soltanifar and F. Hosseinzadeh Lotfi., (2011).The voting analytic hierarchy process method for discriminating among efficient decision making units in data envelopment analysis. Computers & Industrial Engineering 60 (4), 585-592.
    2.

 

  1. M. Soltanifar, A. Ebrahimnejad, MM. Farrokhi., (2010) Ranking of different ranking models using a voting model and its application in determining efficient candidates, International Journal of Society Systems Science 2 (4), 375-389.
    2.

 

  1. M. Soltanifar, S. Shahghobadi, Selecting a benevolent secondary goal model in data envelopment analysis cross-efficiency evaluation by a voting model, Socio-Economic Planning Sciences 47 (2013) 65-74.
    2.

 

  1. M. Soltanifar, (2011), Ranking of different common set of weights models using a voting model and its application in determining efficient DMUs, International Journal of Advanced Operations Management 3 (3-4), 290-308.
    2.

 

  1. M. Soltanifar, S. Shahghobadi.,



(2014), Classifying Inputs and Outputs in Data Envelopment Analysis Based on TOPSIS Method and a Voting Model, International Journal of Business Analytics (IJBAN) 1 (2), 48-63.,

 

  1. H. Sharafi, F. Hosseinzadeh Lotfi, Gh. Jahanshahloo, M. Rostamy-malkhalifeh, M. Soltanifar, S. Razipour-GhalehJough, Ranking of petrochemical companies using preferential voting at unequal levels of voting power through data envelopment analysis, Mathematical Sciences, volume 13, pages287–297(2019).
    2.

 

  1. M. Soltanifar, (2017), A new group voting analytical hierarchy process method using preferential voting. JOURNAL OF OPERATIONAL RESEARCH AND ITS APPLICATIONS (JOURNAL OF APPLIED MATHEMATICS), Volume 14, Issue 3540016, Pages 1-13.
    2.

 

  1. M. Izadikhah, R. Farzipoor Saen., (2019), Solving voting system by data envelopment analysis for assessing sustainability of suppliers, Group Decis Negot 28: 641.
    2.

 

  1. Pittman, R. W., (1983). Multilateral productivity comparisons with undesirable outputs. Economic Journal, 93 (372), 883-891.
    2.

 

  1. Caves, D. W., Christensen, L. R., Diewert, E., (1982). Multilateral comparisons of output, input and productivity using superlative index numbers. The Economic Journal, 92 (365), 73-86.
    2.

 

  1. Fare, R., Grosskopf, S., Lovell, C. A. K., (1989). Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. The Review of Economics and Statistics, 71, 90– 98.
    2.
  2. Seiford, L. M., Zhu, J., (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142 (1), 16-20.
    3.

 

  1. Chambers, R. G., Chung, Y., Fare, R., (1996). Benefit and distance function. Journal of Economic Theory,70(2),407-419.
    2.

 

  1. Chung, Y. H., Fare, R., Grosskopf, S., (1997). Productivity and undesirable outputs a directional distance function approach. Journal of Environmental Management, 51 (3), 229-240.
    2.

 

  1. Dong, G., (2013). A complete ranking of DMUs with undesirable outputs restrictions in DEA models. Mathematical and Computer Modelling,58(5-6),1102-1109
    2.

 

  1. Liu, W., Zhongbao, Z., Ma, Ch., Liu, D., Shen, W., (2015). Two-stage DEA models with undesirable input intermediate-outputs. Omega, 56, 74- 87.
    2.

 

  1. Liu, X., Chu, J., Yin, P., Sun, J., (2016), DEA cross-efficiency evaluation considering undesirable output and ranking priority: a case study of eco-efficiency analysis of coal-fired power plants, Journal of Cleaner Production, 142 (2), 1-9.
    2.

 

  1. Aghayi, N. Ranking Efficient DMUs in Two-stage Network DEA with Common Weights method, Journal of new researches in mathematics, Volume 3, Issue 11, July and August 2017, Page 19-30.
    2.

 

  1. A, Ebrahimnejad, M.R. Bagherzadeh, Data envelopment analysis approach for discriminating efficient candidates in voting systems by considering the priority of voters, Hacettepe Journal of Mathematics and Statistics Volume 45 (1) (2016), 165–180
    2.

Related articles

The rights to this website are owned by the Raimag Press Management System.
Copyright © 2021-2024

Home| Login| About Us| Contact Us|
[فارسی] [العربية] [fa] [ar]