Processing, Please wait...

  • Home
  • About Us
  • Search:
  • Advanced Search

Growing Science » International Journal of Industrial Engineering Computations » Considering supply risk for supplier selection using an integrated framework of data envelopment analysis and neural networks

Journals

  • IJIEC (726)
  • MSL (2637)
  • DSL (668)
  • CCL (508)
  • USCM (1092)
  • ESM (413)
  • AC (562)
  • JPM (271)
  • IJDS (912)
  • JFS (91)
  • HE (26)
  • SCI (26)

IJIEC Volumes

    • Volume 1 (17)
      • Issue 1 (9)
      • Issue 2 (8)
    • Volume 2 (68)
      • Issue 1 (12)
      • Issue 2 (20)
      • Issue 3 (20)
      • Issue 4 (16)
    • Volume 3 (76)
      • Issue 1 (9)
      • Issue 2 (15)
      • Issue 3 (20)
      • Issue 4 (12)
      • Issue 5 (20)
    • Volume 4 (50)
      • Issue 1 (14)
      • Issue 2 (10)
      • Issue 3 (12)
      • Issue 4 (14)
    • Volume 5 (47)
      • Issue 1 (13)
      • Issue 2 (12)
      • Issue 3 (12)
      • Issue 4 (10)
    • Volume 6 (39)
      • Issue 1 (7)
      • Issue 2 (12)
      • Issue 3 (10)
      • Issue 4 (10)
    • Volume 7 (47)
      • Issue 1 (10)
      • Issue 2 (14)
      • Issue 3 (10)
      • Issue 4 (13)
    • Volume 8 (30)
      • Issue 1 (9)
      • Issue 2 (7)
      • Issue 3 (8)
      • Issue 4 (6)
    • Volume 9 (32)
      • Issue 1 (9)
      • Issue 2 (6)
      • Issue 3 (7)
      • Issue 4 (10)
    • Volume 10 (34)
      • Issue 1 (8)
      • Issue 2 (10)
      • Issue 3 (8)
      • Issue 4 (8)
    • Volume 11 (36)
      • Issue 1 (9)
      • Issue 2 (8)
      • Issue 3 (9)
      • Issue 4 (10)
    • Volume 12 (29)
      • Issue 1 (9)
      • Issue 2 (6)
      • Issue 3 (8)
      • Issue 4 (6)
    • Volume 13 (41)
      • Issue 1 (10)
      • Issue 2 (8)
      • Issue 3 (10)
      • Issue 4 (13)
    • Volume 14 (50)
      • Issue 1 (11)
      • Issue 2 (15)
      • Issue 3 (9)
      • Issue 4 (15)
    • Volume 15 (55)
      • Issue 1 (19)
      • Issue 2 (15)
      • Issue 3 (12)
      • Issue 4 (9)
    • Volume 16 (75)
      • Issue 1 (12)
      • Issue 2 (15)
      • Issue 3 (19)
      • Issue 4 (29)

Keywords

Supply chain management(166)
Jordan(161)
Vietnam(149)
Customer satisfaction(120)
Performance(113)
Supply chain(108)
Service quality(98)
Competitive advantage(95)
Tehran Stock Exchange(94)
SMEs(87)
optimization(85)
Financial performance(83)
Trust(82)
TOPSIS(81)
Job satisfaction(80)
Sustainability(80)
Social media(78)
Factor analysis(78)
Knowledge Management(77)
Artificial intelligence(76)


» Show all keywords

Authors

Naser Azad(82)
Mohammad Reza Iravani(64)
Zeplin Jiwa Husada Tarigan(61)
Endri Endri(45)
Muhammad Alshurideh(42)
Hotlan Siagian(39)
Jumadil Saputra(36)
Dmaithan Almajali(36)
Muhammad Turki Alshurideh(35)
Barween Al Kurdi(32)
Ahmad Makui(32)
Basrowi Basrowi(31)
Hassan Ghodrati(31)
Mohammad Khodaei Valahzaghard(30)
Shankar Chakraborty(29)
Ni Nyoman Kerti Yasa(29)
Sulieman Ibraheem Shelash Al-Hawary(28)
Sautma Ronni Basana(28)
Prasadja Ricardianto(28)
Haitham M. Alzoubi(27)


» Show all authors

Countries

Iran(2179)
Indonesia(1285)
Jordan(786)
India(785)
Vietnam(502)
Saudi Arabia(448)
Malaysia(439)
United Arab Emirates(220)
China(184)
Thailand(151)
United States(110)
Ukraine(104)
Turkey(103)
Egypt(98)
Canada(92)
Pakistan(85)
Peru(85)
Morocco(79)
United Kingdom(79)
Nigeria(78)


» Show all countries

International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print)
Quarterly Publication
Volume 4 Issue 2 pp. 273-284 , 2013

Considering supply risk for supplier selection using an integrated framework of data envelopment analysis and neural networks Pages 273-284 Right click to download the paper Download PDF

Authors: Vahid Nourbakhsh, Abbas Ahmadi, Masoud Mahootchi

DOI: 10.5267/j.ijiec.2013.01.001

Keywords: Data Envelopment Analysis, Disruption, Multi-Layer Perceptron, Supplier Selection, Supply Risk

Abstract: For many years, supplier selection as an important multi-criteria decision has attracted both the researchers and practitioners. Recently, high incidences of natural disasters, terrorism attacks, labor strikes, and other kinds of risks, also known as disruptions, indicate the vulnerability of procurement process to these unpredicted events. In this study, a new framework is introduced to select suppliers while considering the supply risks. In the proposed framework, an expert is asked to determine the reliability of each procurement element (i.e., production, transportation, and communication) based on some proposed risk factors. Then, a distinct Multi-Layer Perceptron (MLP) network is trained to play the role of the expert opinion for estimating the reliability scores of each procurement. In addition to reliabilities, the Data Envelopment Analysis (DEA) is used to take into account the conventional selection criteria: price, delivery, quality, and capacity. A set of Pareto-optimal suppliers is obtained from the combination of efficiencies and reliability scores. Finally, the decision maker is recommended to choose between the non-dominated suppliers. Obtained experiment results indicate the effectiveness of the proposed framework.

How to cite this paper
Nourbakhsh, V., Ahmadi, A & Mahootchi, M. (2013). Considering supply risk for supplier selection using an integrated framework of data envelopment analysis and neural networks.International Journal of Industrial Engineering Computations , 4(2), 273-284.

Refrences
Alizadeh, M., Gharakhani, M., Fotoohi, E., & Rada, R. (2011). Design and analysis of experiments in ANFIS modeling for stock price prediction. International Journal of Industrial Engineering Computations, 2(2), 409-418.

Azar, A., Olfat, L., Khosravani, F., & Jalali, R. (2011). A BSC method for supplier selection strategy using TOPSIS and VIKOR: A case study of part maker industry. Management Science Letters, 1(4), 559-568.

Berger, P.D., Gerstenfeld, A., & Zeng, A.Z. (2004). How many suppliers are best? A decision-analysis approach. Omega, 32(1), 9-15.

Caballer-Tarazona M., Moya-Clemente I., Vivas-Consuelo D., Barrachina-Mart?nez, I. (2010). A model to measure the efficiency of hospital performance. Mathematical and Computer Modelling, 52(7), 1095-1102.

Chan, F. T. S., & Chan, H. K. (2010). An AHP model for selection of suppliers in the fast changing fashion market. The International Journal of Advanced Manufacturing Technology, 51(9), 1195-1207.

Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429-444.

Cybenko, G. (1989). Approximation by super positions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4), 303-314.

Dickson, G.W. (1966). An analysis of vendor selection systems and decisions. Journal of Purchasing, 2(1), 5-17.

Ellram, L.M. (1990). The supplier selection decision in strategic partnerships. Journal of Purchasing and Materials Management, 26(4), 8-14.

Gheidar Kheljani, J., Ghodsypour, S., & O & apos; Brien, C. (2009). Optimizing whole supply chain benefit versus buyer & apos; s benefit through supplier selection. International Journal of Production Economics, 121(2), 482-493.

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.

Jafari Songhori, M., Tavana M., Azadeh, A., & Khakbaz, M. H. (2011). A supplier selection and order allocation model with multiple transportation alternatives. The International Journal of Advanced Manufacturing Technology, 52(1), 365-376.

Karray, F. O., & De Silva, C. W. (2004). Soft computing and intelligent systems design: theory, tools, and applications: Addison-Wesley.

Levary, R.R. (2008). Using the analytic hierarchy process to rank foreign suppliers based on supply risks. Computers and Industrial Engineering, 55(2), 535-542.

Li, L. & Zabinsky, Z.B. (2011). Incorporating uncertainty into a supplier selection problem. International Journal of Production Economics, 134(2), 344-356.

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.

Liu, J. S., Lu, L. Y. Y., Lu, W.-M., & Lin, B. J. Y. (2013). A survey of DEA applications. Omega, 41(5), 893–902.

Moqri, M. M., Moshref Javadi, M., & Yazdian, S. A. (2011). Supplier selection and order lot sizing using dynamic programming. International Journal of Industrial Engineering Computations, 2(2), 319-328.

Muralidharan, C., Anantharaman, N., & Deshmukh, S. (2002). A Multi-Criteria Group Decision making model for supplier rating. Journal of Supply Chain Management, 38(4), 22-33.

Narasimhan, R., Talluri, S., Mahapatra, S.K. (2006). Multi-product, multi-criteria model for supplier selection with product life cycle considerations. Decision Sciences, 37(4), 577-603.

Narasimhan, R., Talluri, S., & Mendez, D. (2001). Supplier evaluation and rationalization via data envelopment analysis: an empirical examination. Journal of Supply Chain Management, 37(3), 28-37.

Ng, W.L. (2008). An efficient and simple model for multiple criteria supplier selection problem. European Journal of Operational Research, 186(3), 1059-1067.

Nydick, R. L., & Hill, R. P. (1992). Using the analytic hierarchy process to structure the vendor selection procedure. International Journal of Purchasing and Materials Management, 28(2), 31-36.

Pochard, S. (2003). Managing risks of supply-chain disruptions: dual sourcing as a real option. Citeseer.

Ramanathan, R. (2007). Supplier selection problem: integrating DEA with the approaches of total cost of ownership and AHP. Supply Chain Management: An International Journal, 12(4), 258-261.

Reuters (2005). BMW to recall 20,000 cars with faulty Bosch part, Press release on February 2, 2005.

Ruiz-Torres, A.J., & Mahmoodi, F. (2007). The optimal number of suppliers considering the costs of individual supplier failures. Omega, 35(1), 104-115.

Rumelhart, D. E., Hintont, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.

Sadjadi, S., & Omrani, H. (2008). Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies. Energy Policy, 36(11), 4247-4254.

Sadjadi, S., & Omrani, H. (2010). A bootstrapped robust data envelopment analysis model for efficiency estimating of telecommunication companies in Iran. Telecommunications Policy, 34(4), 221-232.

Sadjadi, S., Omrani, H., Abdollahzadeh, S., Alinaghian, M., & Mohammadi, H. (2011). A robust super-efficiency data envelopment analysis model for ranking of provincial gas companies in Iran. Expert Systems with Applications, 38(9), 10875-10881.

Sadjadi, S., Omrani, H., Makui, A., & Shahanaghi, K. (2011). An interactive robust data envelopment analysis model for determining alternative targets in Iranian electricity distribution companies. Expert Systems with Applications, 38(8), 9830-9839.

Samarasinghe, S. (2006). Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition: Auerbach Publications.

Schalkoff, R.J. (1997). Artificial neural networks. McGraw-Hill Companies.

Seydel, J. (2006). Data envelopment analysis for decision support. Industrial Management and Data Systems, 106(1), 81-95.

Sharma S., and Gupta S. (2010). Malmquist productivity and efficiency analysis for banking industry in India. International Journal of Business Excellence, 3(1), 65-76.

Sheffi, Y. (2005). The Resilient Enterprise: Overcoming Vulnerability for Competitive Advantage. Cambridge, MA: MIT Press.

Talluri, S., & Narasimhan, R. (2003). Vendor evaluation with performance variability: a max-min approach. European Journal of Operational Research, 146(3), 543-552.

Timmerman, E. (1987). An approach to vendor performance evaluation. Engineering Management Review, IEEE, 15(3), 14-20.

Wall Street Journal (2001). Trail by Fire: A Blaze in Albuquerque Sets off Major Crisis for Cell-phone Giants. Press release on January 29, 2001.

Werbos, P. (1974). Beyond regression: new tools for prediction and analysis in the behavioral sciences. PhD, Harvard.

Xu, N., & Nozick, L. (2009). Modeling supplier selection and the use of option contracts for global supply chain design. Computers and Operations Research, 36(10), 2786-2800.

Zande Hesami, H., Afshari, M.A., Ayazi, S.A., & Siahkali Moradi, J. (2011). A hybrid analytical network process and fuzzy goal programming for supplier selection: A case study of auto part maker. Management Science Letters, 1(4), 583-594.
  • 51
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: International Journal of Industrial Engineering Computations | Year: 2013 | Volume: 4 | Issue: 2 | Views: 3341 | Reviews: 0

Related Articles:
  • A multiple criteria decision making technique for supplier selection and in ...
  • An empirical study for ranking insurance firms using a hybrid of data envel ...
  • A new DEA-GAHP method for supplier selection problem
  • A discount ordering strategy in two-level supply chain: A case study of tex ...
  • Supplier evaluation in manufacturing environment using compromise ranking m ...

Add Reviews

Name:*
E-Mail:
Review:
Bold Italic Underline Strike | Align left Center Align right | Insert smilies Insert link URLInsert protected URL Select color | Add Hidden Text Insert Quote Convert selected text from selection to Cyrillic (Russian) alphabet Insert spoiler
winkwinkedsmileam
belayfeelfellowlaughing
lollovenorecourse
requestsadtonguewassat
cryingwhatbullyangry
Security Code: *
Include security image CAPCHA.
Refresh Code

® 2010-2025 GrowingScience.Com