An Approach to Solve Multi-criteria Supplier Selection While Considering Environmental Aspects Using Differential Evolution

  • Sunil Kumar Jauhar
  • Millie Pant
  • Aakash Deep
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8297)


Selection of an appropriate supplier is gaining a lot interest among researchers working in the field of supply chain management .Often many suppliers are available in the market that fulfills some preliminary criteria. However the real task is to determine the most suitable set of suppliers (or key suppliers) subject to management as well as environmental aspects. In the current study, we present an approach to solve the multiple-criteria green supplier selection problem (mathematical model formulated with Data Envelopment Analysis) with the application of differential evolution. A hypothetical case demonstrates the application of the present approach.


Green supplier selection Supply chain management Differential evolution Data Envelopment Analysis Multi criteria decision making CO2 Emissions 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agarwal, P., Sahai, M., Mishra, V., Bag, M., Singh, V.: A review of multi-criteria techniques for supplier evaluation and selection. International Journal of Industrial Engineering Computations 2 (2011), doi:10 5267/j ijiec 2011 06 004Google Scholar
  2. 2.
    Noci, G.: Designing green vendor rating systems for the assessment of a supplier’s environmental performance. European J. of Purchasing and Supply Management 3(2), 103–114 (1997)CrossRefGoogle Scholar
  3. 3.
    Humphreys, P.K., Wong, Y.K., Chan, F.T.S.: Integrating environmental criteria into the supplier selection process. Journal of Material Processing Technology 138, 349–356 (2003)CrossRefGoogle Scholar
  4. 4.
    Selos, E., Laine, T.: The perceived usefulness of decision-making methods in procurement. In: Seventeenth International Working Seminar on Production Economics, Pre-prints, vol. 1, pp. 461–472 (2012)Google Scholar
  5. 5.
    Storn, R., Price, K.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces, Berkeley, CA. Tech. Rep. TR-95-012 (1995)Google Scholar
  6. 6.
    Plagianakos, V., Tasoulis, D., Vrahatis, M.: A Review of Major Application Areas of Differential Evolution. In: Chakraborty, U.K. (ed.) Advances in Differential Evolution. SCI, vol. 143, pp. 197–238. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Wang, F., Jang, H.: Parameter estimation of a bio reaction model by hybrid differential evolution. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC 2000), pp. 410–417 (2000)Google Scholar
  8. 8.
    Joshi, R., Sanderson, A.: Minimal representation multi sensor fusion using differential evolution. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 29(1), 63–76 (1999)CrossRefGoogle Scholar
  9. 9.
    Ilonen, J., Kamarainen, J., Lampine, J.: Differential evolution training algorithm for feed-forward neural networks. Neural Process. Lett. 17(1), 93–105 (2003)CrossRefGoogle Scholar
  10. 10.
    Ali, M., Siarry, P., Pant, M.: An efficient differential evolution based algorithm for solving multi-objective optimization. European Journal of Operational Research (2011)Google Scholar
  11. 11.
    Genovese, A., Koh, S.C.L., Bruno, G., Bruno, P.: Green supplier selection: a literature review and a critical perspective. Paper Presented at IEEE 8th International Conference on Supply Chain Management and Information Systems (SCMIS), Hong Kong (2010)Google Scholar
  12. 12.
    Mazhar, M.I., Kara, S., Kaebernick, H.: Reusability assessment of components in consumer products—a statistical and condition monitoring data analysis strategy. In: Fourth Australian Life Cycle Assessment Conference—Sustainability Measures For Decision Support, Sydney, Australia (2005)Google Scholar
  13. 13.
  14. 14.
    Dimitris, K.S., Lamprini, V.S., Yiannis, G.S.: Data envelopment analysis with nonlinear virtual inputs and outputs. European Journal of Operational Research 202, 604–613 (2009)Google Scholar
  15. 15.
    Ramanathan, R.: An Introduction to Data Envelopment Analysis: A Tool for Performance Measurement. Sage Publication Ltd., New Delhi (2003)Google Scholar
  16. 16.
    Srinivas, T.: Data envelopment analysis: models and extensions. Production/Operation Management Decision Line, 8–11 (2000)Google Scholar
  17. 17.
    Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. European Journal of Operational Research 2(6), 429–444 (1978)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Banker, R.D., Charnes, A., Cooper, W.W.: Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30, 1078–1092 (1984)CrossRefzbMATHGoogle Scholar
  19. 19.
    Das, S., Abraham, A., Chakraborty, U., Konar, A.: Differential evolution using a neighborhood-based mutation operator. IEEE Transaction on Evolutionry Computing 13(3), 526–553 (2009)CrossRefGoogle Scholar
  20. 20.
    Kumar, P., Mogha, S.K., Pant, M.: Differential Evolution for Data Envelopment Analysis. In: Deep, K., Nagar, A., Pant, M., Bansal, J.C. (eds.) Proceedings of the International Conference on SocProS 2011. AISC, vol. 130, pp. 311–320. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Jouni, L.: A constraint handling approach for differential evolution algorithm. In: Proceeding of IEEE Congress on Evolutionary Computation (CEC 2002), pp. 1468–1473 (2002)Google Scholar
  22. 22.
    Coello, C.A.C.: Theoretical and numerical constraint handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering 191(11-12), 1245–1287 (2002); Differential Evolution for Data Envelopment Analysis 319 CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Ray, T., Kang, T., Chye, S.K.: An evolutionary algorithm for constraint optimization. In: Whitley, D., Goldberg, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.G. (eds.) Proceeding of the Genetic and Evolutionary Computation Conference (GECCO 2000), pp. 771–777 (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Sunil Kumar Jauhar
    • 1
  • Millie Pant
    • 1
  • Aakash Deep
    • 2
  1. 1.Indian Institute of Technology, RoorkeeIndia
  2. 2.Jaypee University of Engineering and TechnologyGunaIndia

Personalised recommendations