Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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 06 004
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)
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)
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)
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)
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)
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)
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)
Ilonen, J., Kamarainen, J., Lampine, J.: Differential evolution training algorithm for feed-forward neural networks. Neural Process. Lett. 17(1), 93–105 (2003)
Ali, M., Siarry, P., Pant, M.: An efficient differential evolution based algorithm for solving multi-objective optimization. European Journal of Operational Research (2011)
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)
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)
http://www.london2012.com/documents/locog-publications/locog-guidelines-on-carbon-emissions-of-products-and-services.pdf (accessed on May 12, 2013)
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)
Ramanathan, R.: An Introduction to Data Envelopment Analysis: A Tool for Performance Measurement. Sage Publication Ltd., New Delhi (2003)
Srinivas, T.: Data envelopment analysis: models and extensions. Production/Operation Management Decision Line, 8–11 (2000)
Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. European Journal of Operational Research 2(6), 429–444 (1978)
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)
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)
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)
Jouni, L.: A constraint handling approach for differential evolution algorithm. In: Proceeding of IEEE Congress on Evolutionary Computation (CEC 2002), pp. 1468–1473 (2002)
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
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Jauhar, S.K., Pant, M., Deep, A. (2013). An Approach to Solve Multi-criteria Supplier Selection While Considering Environmental Aspects Using Differential Evolution. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-03753-0_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03752-3
Online ISBN: 978-3-319-03753-0
eBook Packages: Computer ScienceComputer Science (R0)