1. Proceedings
  2. I3M
  3. 2022
  4. MAS
  5. 10.46354/i3m.2022.mas.008

Modelling supply chain coordination using multidimensional auctions

  • a Petr Fiala  ,
  • b Renata Majovská
  • a Prague University of Economics and Business, W. Churchill Sq. 4, Prague 3, 13067, Czech Republic
  • b University of Finance and Administration, Prague, Department of Computer Science and Mathematics, Estonská 500, Prague 10, 101 00, Czech Republic
Cite as
Fiala P., and Majovská R., (2022).,Modelling supply chain coordination using multidimensional auctions. Proceedings of the 21st International Conference on Modelling and Applied Simulation MAS 2022). , 008 . DOI: https://doi.org/10.46354/i3m.2022.mas.008

Abstract

Supply chain is a decentralized system where material, financial and information flows connect economic agents. There is much inefficiency in supply chain behavior. Recently, considerable attention of researchers is drowned to provide some incentives to adjust the relationship of supply chain agents to coordinate the supply chain, i.e., the total profit of the decentralized supply chain is equal to that achieved under a centralized system. Various mechanisms are proposed to coordinate supply chains. The use of auctions has so far been little studied. Auctions are important market mechanisms for the allocation of goods and services. The main contribution of the paper is the design of a complex trading model between layers of the supply chain. The model is based on our proposed so-called multidimensional auctions which include all auction extensions (multi-item, multi-type, multi-criteria, multi-round) into one common model and its use for supply chain coordination. We also proposed the Aspiration Level Oriented Procedure (ALOP) for solving multidimensional auctions. This approach then serves as a simulation of real auctions, where extensions suitable for reality are captured.

References

  1. Alaswad, S., Salman, S., Al Hashmi, A., Al Marzooqi, H., & Al Hammadi, M. (2019). The effect of demand variability on supply chain performance. In: 8th International Conference on Modeling Simulation and Applied Optimization, 1-4.
  2. Arshinder, K., Kanda, A., & Deshmukh, S. G. (2011). A review on supply chain coordination: coordination mechanisms, managing uncertainty and research directions. Supply chain coordination under uncertainty, 39-82.
  3. Babekian, S., Hariga, M., & Bahroun, Z. (2017). Integrated economic and environmental two-stage supply chain model under vendor managed consignment inventory partnership. In: 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), 1-6.
  4. Chaabane, A. (2013). Sustainable supply chains optimization: Mathematical modelling approach. In: 5th International Conference on Modeling, Simulation and Applied Optimization, 1-6.
  5. Cogollo-Flórez, J. M., & Correa-Espinal, A. A. (2018). Rule-based Modeling of Supply Chain Quality Management. In: The international conference on modeling and applied simulation, 120-125.
  6. Cramton, P., Shoham, Y. & Steinberg, R. (Eds.) (2006). Combinatorial Auctions. Cambridge: MIT Press.
  7. de Vries, S. & Vohra, R. V. (2003). Combinatorial auctions: A survey. INFORMS Journal of Computing, 15:284-309
  8. Fiala, P. (2005). Information sharing in supply chains. Omega, 33:419 – 423.
  9. Fiala, P. (2016). Profit allocation games in supply chains. Central European Journal of Operations Research, 24:267-281.
  10. Fiala, P. & Borovička, A. (2022). Stock portfolio selection using aspiration level-oriented procedure: real case on the RM-SYSTEM Czech stock exchange. Central European Journal of Operations Research, 30:781–805.
  11. Fiala, P. & Kuncová, M. (2019). Simulation model of supply networks development. In: The 18th International Conference on Modelling and Applied Simulation. Genoa: University of Genoa, 16–21.
  12. Fruhner, D. Grimm, D., Sardesai, S. Wageniz, A., Vennemann, A. & Hegmans, T. (2020). A Tool-Independent Generalized Description for Sustainable Supply Chain Design. In: The 19th International Conference on Modelling and Applied Simulation. Cal-Tek, 99-105.
  13. Harrison, T. P., Lee, H. L. & Neale, J. J. (2003). The Practice of Supply Chain Management: Where Theory and Application Converge. Boston: Kluwer.
  14. Klemperer, P. (2004). Auctions: Theory and Practice. Princeton: Princeton University Press.
  15. Krishna, V. (2002). Auction Theory. San Diego: Academic Press.
  16. Klemperer, P. (2004). Auctions: Theory and Practice. Princeton: Princeton University Press.
  17. Majovska, R. & Fiala, P. (2019). Modelling of supply network design. In: The 18th International Conference on Modelling and Applied Simulation. Genoa: University of Genoa, 22–27.
  18. Milgrom, P. (2004). Putting Auction Theory to Work. Cambridge: Cambridge University Press.
  19. Rinaldi, M., Bottani, E., Ferretti, G., Armenzoni, M., Marchini, D., Solari, F., Vignali, G., Montanari, R. (2012). Supply chain simulation: A study on reorder policies for perishable food products. In: The 11th International Conference on Modelling and Applied Simulation, 308-315.
  20. Simchi-Levi, D., Kaminsky, P. & Simchi-Levi, E. (1999). Designing and Managing the Supply Chain: Concepts, Strategies and Case studies. Boston: Irwin/ Mc Graw-Hill.
  21. Simchi-Levi, D., Wu, S. D. & Shen, M. (eds) (2004). Handbook of Quantitative Supply Chain Analysis: Modeling in the e-Business Era. Boston: Kluwer.
  22. Snyder, L. V. & Shen Z. J. M. (2011). Fundamentals of Supply Chain Theory. Hoboken: Wiley.
  23. Snyder, L. V. & Shen Z. J. M. (2011). Fundamentals of Supply Chain Theory. Hoboken: Wiley.
  24. Tayur, S., Ganeshan, R., & Magazine, M. (Eds.) (1999). Quantitative models for supply chain management. Boston: Kluwer., & Krishnaiyer, K. (2020). Integration of Lean practices and Industry 4.0 technologies: Smart manufacturing for nextgeneration enterprises. The International Journal of
    Advanced Manufacturing Technology, 107(5), 2927– 2936. https://doi.org/10.1007/s00170-020-05124-0