Winner Determination in Multi-attribute Combinatorial Reverse Auctions

  • Shubhashis Kumar Shil
  • Malek MouhoubEmail author
  • Samira Sadaoui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9491)


Winner(s) determination in online reverse auctions is a very appealing e-commerce application. This is a combinatorial optimization problem where the goal is to find an optimal solution meeting a set of requirements and minimizing a given procurement cost. This problem is hard to tackle especially when multiple attributes of instances of items are considered together with additional constraints, such as seller’s stocks and discount rate. The challenge here is to determine the optimal solution in a reasonable computation time. Solving this problem with a systematic method will guarantee the optimality of the returned solution but comes with an exponential time cost. On the other hand, approximation techniques such as evolutionary algorithms are faster but trade the quality of the solution returned for the running time. In this paper, we conduct a comparative study of several exact and evolutionary techniques that have been proposed to solve various instances of the combinatorial reverse auction problem. In particular, we show that a recent method based on genetic algorithms outperforms some other methods in terms of time efficiency while returning a near to optimal solution in most of the cases.


Combinatorial reverse auctions Genetic algorithms Winner determination E-commerce 


  1. 1.
    Abbasian, R., Mouhoub, M.: An efficient hierarchical parallel genetic algorithm for graph coloring problem. In: 13th Annual GECCO, pp. 521–528 (2011)Google Scholar
  2. 2.
    Abbasian, R., Mouhoub, M.: A hierarchical parallel genetic approach for the graph coloring problem. Appl. Intell. 39(3), 510–528 (2013). SpringerCrossRefGoogle Scholar
  3. 3.
    Avasarala, V., Mullen, T., Hall, D.L., Garga, A.: MASM: market architecture or sensor management in distributed sensor networks. In: SPIE Defense and Security Symposium, pp. 5813–5830 (2005)Google Scholar
  4. 4.
    Avasarala, V., Polavarapu, H., Mullen, T.: An approximate algorithm for resource allocation using combinatorial auctions. In: International Conference on Intelligent Agent Technology, pp. 571–578 (2006)Google Scholar
  5. 5.
    Das, A., Grosu, D.: A combinatorial auction-based protocols for resource allocation in grids. In: 19th IEEE International Parallel and Distributed Processing Symposium (2005)Google Scholar
  6. 6.
    Ebrahim, R.M., Razmi, J., Haleh, H.: Scatter search algorithm for supplier selection and order lot sizing under multiple price discount environment. Adv. Eng. Softw. 40(9), 766–776 (2009)CrossRefzbMATHGoogle Scholar
  7. 7.
    Gonen, R., Lehmann, D.: Optimal solutions for multi-unit combinatorial auctions: branch and bound heuristics. In: 2nd ACM Conference on Electronic Commerce, pp. 13–20 (2000)Google Scholar
  8. 8.
    Gong, J., Qi, J., Xiong, G., Chen, H., Huang, W.: A GA based combinatorial auction algorithm for multi-robot cooperative hunting. In: International Conference on Computational Intelligence and Security, pp. 137–141 (2007)Google Scholar
  9. 9.
    Gupta, D., Ghafir, S.: An overview of methods maintaining diversity in genetic algorithms. Int. J. Emerg. Technol. Adv. Eng. 2(5), 56–60 (2012)Google Scholar
  10. 10.
    Muhlenbein, H.: Evolution in time and space-the parallel genetic algorithm. In: Foundations of Genetic Algorithms, pp. 316–337 (1991)Google Scholar
  11. 11.
    Narahari, Y., Dayama, P.: Combinatorial auctions for electronic business. Sadhana 30(Pt. 2 & 3), 179–211 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Nowostawski, M., Poli, R.: Parallel genetic algorithm taxonomy. In: 3rd International Conference on Knowledge-Based Intelligent Information Engineering Systems, pp. 88–92 (1999)Google Scholar
  13. 13.
    Ostler, J., Wilke, P.: Improvement by combination how to increase the performance of optimisation algorithms by combining them. In: 10th International Conference of the Practice and Theory of Automated Timetabling, pp. 359–365 (2014)Google Scholar
  14. 14.
    Patodi, P., Ray, A.K., Jenamani, M.: GA based winner determination in combinatorial reverse auction. In: 2nd International Conference on Emerging Applications of Information Technology (EAIT), pp. 361–364 (2011)Google Scholar
  15. 15.
    Qian, X., Huang, M., Gao, T., Wang, X.: An improved ant colony algorithm for winner determination in multi-attribute combinatorial reverse auction. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1917–1921 (2014)Google Scholar
  16. 16.
    Rassenti, S.J., Smith, V.L., Bulfin, R.L.: A combinatorial auction mechanism for airport time slot allocation. Bell J. Econ. 13, 402–417 (1982)CrossRefGoogle Scholar
  17. 17.
    Shil, S.K., Mouhoub, M.: Considering multiple instances of items in combinatorial reverse auctions. In: Ali, M., Pan, J.-S., Chen, S.-M., Horng, M.-F. (eds.) IEA/AIE 2014, Part II. LNCS, vol. 8482, pp. 487–496. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  18. 18.
    Shil, S.K., Mouhoub, M., Sadaoui, S.: An approach to solve winner determination in combinatorial reverse auctions using genetic algorithms. In: 15th Annual GECCO, pp. 75–76 (2013)Google Scholar
  19. 19.
    Shil, S.K., Mouhoub, M., Sadaoui, S.: Evolutionary technique for combinatorial reverse auctions. In: 28th FLAIRS, pp. 79–84 (2015)Google Scholar
  20. 20.
    Shil, S.K., Mouhoub, M., Sadaoui, S.: Winner determination in combinatorial reverse auctions. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds.) Contemporary Challenges and Solutions in Applied AI. SCI, vol. 489, pp. 35–40. Springer, Heidelberg (2013)Google Scholar
  21. 21.
    Sadaoui, S., Shil, S.K.: Constraint and qualitative preference specification in multi-attribute reverse auctions. In: Ali, M., Pan, J.-S., Chen, S.-M., Horng, M.-F. (eds.) IEA/AIE 2014, Part II, LNCS(LNAI), vol. 8482, pp. 497–506. Springer, Heidelberg (2014)Google Scholar
  22. 22.
    Xia, W., Wu, Z.: Supplier selection with multiple criteria in volume discount environments. Omega 35(5), 494–504 (2007)CrossRefGoogle Scholar
  23. 23.
    Zhang, L.: The winner determination approach of combinatorial auctions based on double layer orthogonal multi-agent genetic algorithm. In: 2nd IEEE Conference on Industrial Electronics and Applications, pp. 2382–2386 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shubhashis Kumar Shil
    • 1
  • Malek Mouhoub
    • 1
    Email author
  • Samira Sadaoui
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

Personalised recommendations