Advertisement

An Economic Model-Based Matching Approach Between Buyers and Sellers Through a Broker in an Open E-Marketplace

  • Dien Tuan Le
  • Minjie Zhang
  • Fenghui Ren
Article

Abstract

A broker in an open e-marketplace enables buyers and sellers to do business with each other. Although a broker plays an important role in e-marketplaces, theory and guidelines for matching between buyers and sellers in multi-attribute exchanges are limited. Therefore, a challenge for a broker’s responsibility is how to maximize a buyer’s total satisfaction degree as its goals under the consideration of trade-off between a buyer’s buying quantity and price paid to a seller, and other attributes. To solve this challenge, this paper proposes an economic model-based matching approach between a buyer’s requirements and a seller’s offers. The major contributions of this paper are that (i) a broker can model a seller’s price policy as per a buyer’s buying quantity through communication between a broker and a seller; (ii) due to each buyer’s different quantity demand, a broker models a buyer’s satisfaction degree as per a buyer’s buying quantity based on communication between a broker and a buyer; and (iii) to carry out a broker’s matching processes, an objective function and a set of constraints are generated to help a broker to maximize a buyer’s total satisfaction degree. Experimental results demonstrate the good performance of the proposed approach.

Keywords

E-marketplace broker multi-attributes matching approach economic model objective function 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Alpár, F. Z. (2010). Matchmaking framework for b2b e-marketplaces. Informatica Economica, 14(4): 164–169.Google Scholar
  2. [2]
    Badia, L., LindströM, M., Zander, J. & Zorzi, M. (2004). An economic model for the radio resource management in multimedia wireless systems. Computer Communications 27(11): 1056–1064.CrossRefGoogle Scholar
  3. [3]
    Badia, L., Merlin, S., Zanella, A. & Zorzi, M. (2006). Pricing VoWLAN services through a micro-economic framework. IEEE Wireless Communications 13(1): 6–13.CrossRefGoogle Scholar
  4. [4]
    Badidi, E. (2016). A broker-based framework for integrated sla-aware saas provisioning. International Journal on Cloud Computing: Services and Architecture (IJCCSA) 6(2): 1–19.Google Scholar
  5. [5]
    Easley, D. & Kleinberg, J. (2010). Networks, Crowds, and Markets. Cambridge Univ Press.CrossRefzbMATHGoogle Scholar
  6. [6]
    Fletcher, R. (2013). Practical Methods of Optimization, John Wiley & Sons.zbMATHGoogle Scholar
  7. [7]
    Han, L. & Hong, S.H. (2013). In-house transactions in the real estate brokerage industry: matching outcome or strategic promotion? Summer Real Estate Symposium. Monterey, California.Google Scholar
  8. [8]
    Jiang, Z.Z., Fan, Z.P., Ip, W. & Chen, X. (2016). Fuzzy multi-objective modeling and optimization for one-shot multi-attribute exchanges with indivisible demand. IEEE Transactions on Fuzzy Systems, 24(3): 708–723.CrossRefGoogle Scholar
  9. [9]
    Jiang, Z.Z., Fan, Z.P., Tan, C.Q. & Yuan, Y. (2011). A matching approach for one-shot multi-attribute exchanges with incomplete weight information in e-brokerage. International Journal of Innovative Computing, Information and Control, 7(5): 2623–2636.Google Scholar
  10. [10]
    Jiang, Z.Z., Ip, W.H., Lau, H.C.W. & Fan, Z.P. (2011). Multi-objective optimization matching for one-shot multi-attribute exchanges with quantity discounts in e-brokerage. Expert Systems with Applications, 38(4): 4169–4180.CrossRefGoogle Scholar
  11. [11]
    Jung, J.J. & Jo, G.S. (2000). Brokerage between buyer and seller agents using constraint satisfaction problem models. Decision Support Systems 28(4): 293–304.CrossRefGoogle Scholar
  12. [12]
    Ketter, W., Collins, J., Reddy, P. & Weerdt, M. (2012). The 2012 power trading agent competition. ERIM Report Series Reference No. ERS-2012-010-LIS, 1-49, 2012.Google Scholar
  13. [13]
    Kuate, R. T., He, M., Chli, M. & Wang, H. (2013). An Intelligent broker agent for energy trading: An MDP approach. In: the Twenty-Third International Joint Conference on Artificial Intelligence, 234-240, 2013.Google Scholar
  14. [14]
    Le, D.T., Zhang, M. & Ren, F. (2015). A broker-based optimal matching approach of buyers and sellers for multi-attribute exchanges in open markets. In: IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, 563-568, 2015.Google Scholar
  15. [15]
    Le, D.T., Ren, F. & Zhang, M. (2016). Membership function based matching approach of buyers and sellers through a broker in open e-marketplace. In: Smart Simulation and Modelling for Complex Systems: Multi-Agent and Complex Systems, 125-137, 2016.Google Scholar
  16. [16]
    Le, D.T., Zhang, M. & Ren, F. (2016). A multi-criteria group based matching approach of buyers and sellers through a broker in open e-marketplaces. In: the Ninth International Workshop on Agent-based Complex Automated Negotiations (ACAN2016), 17-23, 2016.Google Scholar
  17. [17]
    Li, X. & Murata, T. (2009). Priority based matchmaking method of buyers and suppliers in b2b e-marketplace using multi-objective optimization. In: the International MultiConference of Engineers and Computer Scientists, Vol. 1, 2009.Google Scholar
  18. [18]
    Peters, M., Ketter, W., Saar-Tsechansky, M. & Collins, J. (2013). A reinforcement learning approach to autonomous decision-making in smart electricity markets. Machine Learning, 92(1): 5–39.MathSciNetCrossRefGoogle Scholar
  19. [19]
    Srivastava, N. K., Singh, P. & Singh, S. (2014). Optimal adaptive CSP scheduling on basis of priority of specific service using cloud broker. In: the 9th International Conference on Industrial and Information Systems, 1-6, 2014.Google Scholar
  20. [20]
    Standing, S., Standing, C. & Love, P.E. (2010). A review of research on e-marketplaces 1997–2008. Decision Support Systems, 49(1):41–51.CrossRefGoogle Scholar
  21. [21]
    Tiwari, A., Nagaraju, A. & Mahrishi, M. (2013). An optimized scheduling algorithm for cloud broker using adaptive cost model. In: the IEEE 3rd International Advance Computing Conference (IACC), 28-33, 2013.Google Scholar
  22. [22]
    Wang, X., Zhang, M., Ren, F. & Ito, T. (2015). GongBroker: a broker model for power trading in smart grid markets. In: the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 21-24, 2015.Google Scholar
  23. [23]
    Wang, X., Zhang, M., & Ren, F. (2017). A hybrid-learning based broker model for strategic power trading in smart grid markets. Knowledge-Based Systems, 119: 142–151.CrossRefGoogle Scholar
  24. [24]
    Wu, L., Garg, S.K., Buyya, R., Chen, C. & Versteeg, S. (2013). Automated SLA negotiation framework for cloud computing. In: the 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 235-244, 2013.Google Scholar

Copyright information

© Systems Engineering Society of China and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of E-commerce, University of EconomicsThe University of Da NangDa NangVietnam
  2. 2.School of Computing and Information TechnologyUniversity of WollongongWollongongAustralia

Personalised recommendations