1. Proceedings
  2. I3M
  3. 2020
  4. EMSS
  5. 10.46354/i3m.2020.emss.006

Agent-based modeling for decision making support: case of transport logistics in oil company

  • a Elena Serova , 
  • b Daniil Shklyaev   
  • a,b Maikop State Technological University, Pervomayskaya st. 191, Maikop, 385000, Russia
Cite as
Serova E., Shklyaev D. (2020). Agent-based modeling for decision making support: case of transport logistics in oil company. Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020), pp. 35-40. DOI: https://doi.org/10.46354/i3m.2020.emss.006

Abstract

At present the use of modern modeling methods and tools is an essential component of management information systems for a company to succeed in rapidly changing environment. It is important that simulation is considered today as obligatory stage of decision making in oil companies, which use modern information technologies actively. The paper is focused on the description and comparative analysis of system dynamics and agent-based modeling, used for intelligent decision support systems development in transport logistics. The main goal of this research is evaluation of the multi-agent systems role for decision making processes and management information systems development and creating the model of logistics processes (the process of oil products loading and unloading). It also considers the main determinations and notions of the intellectual agent modelling methodology, gives the types of modeling categorization. The work is based on generalization of theoretical researches in this area along with the international practices and domestic experience.

References

  1. Albright, S.C., Zappe, C. J., and Winston W. L. (2011). Data Analysis, Optimization, and Simulation Modeling. Canada: Cengage Learning.
  2. Borshchev, A.; Filippov, A. (2004). AnyLogic — MultiParadigm Simulation for Business, Engineering and Research. Proceedings of The 6th IIE Annual Simulation Solutions Conference. Orlando, Florida, USA.
  3. Borshchev, A., Filippov, A. (2006). From System Dynamics and Discrete Event to Practical Agent Based Modeling. Retrieved from https://www.anylogic.com/resources/articles/
  4. Elberg, M.S., Tsygankov, N.S. (2017). Imitation modeling: tutorial. Krasnoyarsk: Siberia Federal
    University.
  5. Forrester, J. (1961). Industrial Dynamics. Cambridge, MA: MIT Press.
  6. Karpov, Y. (2005). System simulation modeling. Introduction to modeling with AnyLogic. St.
    Petersburg: BHV.
  7. Katalevsky, D.Yu. (2015). Fundamentals of simulation and system analysis in management: a tutorial (2nd ed.). Moscow: Delo.
  8. Krichevsky, M. and Serova, E. (2016). Business Analysis and Decision Making Based on Data and Models. Theory, Practice, Tools. St. Petersburg: Professional Literature.
  9. Law, A.M.; Kelton, W.D. (2000). Simulation Modelling and Analysis (3rd ed.). McGraw-Hill.
  10. Pidd, M. (2004). Computer Simulation in Management Science (5th ed.). Wiley.
  11. Pospelov, D.A. (1998). Multi-agent systems – present and future. Information Technologies and Computing Systems, No 1:14-21.
  12. Serova E. (2012a). Distributed Artificial Intelligent Systems for Decision Making Support. Proceedings of The 26th Annual Conference of the British Academy of Management BAM 2012. Cardiff: Cardiff University, Cardiff Business School, the United Kingdom.
  13. Serova, E. (2012b). Enterprise Information Systems of New Generation. The Electronic Journal Information Systems Evaluation, 15(1): 116-126. Retrieved from file:///D:/Users/User/Downloads/ejise-volume15-issue1-article823.pdf.
  14. Serova E. (2013). The Role of Agent Based Modelling in the Design of Management Decision Processes. The  Electronic Journal Information Systems Evaluation, 16(1), 74-84. Retrieved from
    file:///D:/Users/User/Downloads/ejise-volume16-issue1-article867.pdf.
  15. Sterman J. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw Hill.
  16. Wooldridge, M. (2002). Introduction to MultiAgent Systems. Wiley.
  17. AnyLogic. Official Web-Site. Retrieved from https://www.anylogic.ru.
  18. Gazprom. Informatory. Official Web-Site. Retrieved from
    http://www.gazprominfo.ru/articles/liquefiedpetroleum/.