Commenced in January 2007
Paper Count: 31106
A Context-Aware Supplier Selection Model
Abstract:Selection of the best possible set of suppliers has a significant impact on the overall profitability and success of any business. For this reason, it is usually necessary to optimize all business processes and to make use of cost-effective alternatives for additional savings. This paper proposes a new efficient context-aware supplier selection model that takes into account possible changes of the environment while significantly reducing selection costs. The proposed model is based on data clustering techniques while inspiring certain principles of online algorithms for an optimally selection of suppliers. Unlike common selection models which re-run the selection algorithm from the scratch-line for any decision-making sub-period on the whole environment, our model considers the changes only and superimposes it to the previously defined best set of suppliers to obtain a new best set of suppliers. Therefore, any recomputation of unchanged elements of the environment is avoided and selection costs are consequently reduced significantly. A numerical evaluation confirms applicability of this model and proves that it is a more optimal solution compared with common static selection models in this field.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328666Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1481
 N. Aissaoui, M. Haouari, and E. Hassini., "Supplier Selection and Order Lot Sizing Modeling: A Review", Computers & Operations Research, vol. 34, pp. 3516-3540, 2007.
 M. Weiser, and J.S. Brown, "The Coming Age of Calm Technology", Xeroc PARC, 1996.
 M. Baldauf, S. Dustdar, and F. Rosenberg, "A Survey on Context-Aware Systems", International Journal of Ad Hoc Ubiquitous Computing, vol. 2, no. 4, pp. 263-277, 2007.
 A. Kalai, and S. Vempala, "Efficient Algorithms for Online Decision Problems", Journal of Computer and System Sciences, vol. 71, pp. 291- 307, 2005.
 T. Levina, Y. Levin, J. McGill, and M. Nediak, "Linear Programming with Online Learning", Operation Research Letters, vol. 35, pp. 612- 618, 2007.
 V. Zinkevich, "Online Convex Programming and Generalized Infinitesimal Gradient Ascent", in Proc. 20th International Conference on Machine Learning, 2003, pp. 928-936.
 R. Xu, and D. Wunsch, "Survey of Clustering Algorithms", IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 645-678, 2005.
 A.K. Jain, M.N. Murty, and P.J. Flynn, "Data Clustering: A Review", ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999.
 M. Dittenbach, D. Merkl, and A. Rauber, "The Growing Hierarchical Self-Organizing Map", in Proc. International Joint Conference on Neural Networks, Como, Italy, 2000, pp. 15-19.
 J. Beringer, and E. H├╝llermeier, "Online Clustering of Parallel Data Streams", Data & Knowledge Engineering, vol. 58, pp. 180-204, 2006.
 D. Chakrabarti, R. Kumar, and A. Tomkins, "Evolutionary Clustering", in Proc. 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Philadelphia, PA, USA, 2006, pp. 554 - 560.
 Y. Huang, S. Liu, and Y. Wang, "Online Detecting and Tracking of the Evolution of User Communities", in Proc. of the Third International Conference on Natural Computation, 2007, pp. 681-685.
 Y. Wang, S. Liu, J. Feng, "Mining Naturally Smooth Evolution of Clusters from Dynamic Data", in Proc. of SIAM Conf. on Data Mining, Minneapolis, Minnesota, 2007, pp. 125-134.
 G.H. Hong, S.C. Park, D.S. Jang, and H.M. Rho, "An Effective Supplier Selection Method for Constructing a Competitive Supply-Relationship", Expert Systems with Applications, vol. 28, pp. 629-639, 2005.
 F. Fayazbakhsh, and M. Razzazi, "Coordination of a Multi-Commodity Supply Chain with Multiple Members using Flow Networks", in Proc. Second International Conference on Digital Society, Sainte Luce, Martinique, 2008, pp. 25-30.