Skip to main content
Log in

Interaction of software agents in the problem of coordinating orders

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

This article considers one of supply chain problems—procurement and inventory management. As part of this task, the main problem is the coordination of an order between the supplier and customer, in particular, the timely coordination of the name and number of component parts, prices, and delivery time. The current manual approach has a number of disadvantages related to noncompliance with the production plan, which cause financial losses. To automate this process, we propose a multiagent software system. The choice of approach is caused by the properties of agent systems— autonomy, pro-activeness, sociability, and reactivity. Agents communicate via message exchange with reference to the common ontology for agents participating in the negotiations. This ontology forms the basis of interaction between agents and means of structuring domain knowledge allows software agents to share available knowledge and identify new knowledge. The object of study is an enterprise that produces microchips. Software agents are developed within the JADE platform.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Plinere, D. and Borisov, A., A negotiation-based multi-agent system for supply chain management, Sci. J. Riga Tech. Univ., Inf. Technol. Manage. Sci., 2011, vol. 49, pp. 128–132.

    Google Scholar 

  2. Oberholzer, J.J., How agent technologies could be used in supply chain management, Proc. SAICSIT, 2003, pp. 111–121.

    Google Scholar 

  3. Wooldridge, M. and Jennings, N., Intelligent agents: Theory and practice, Knowl. Eng. Rev., 1995, no. 10(2), pp. 115–152.

    Article  Google Scholar 

  4. Russell, S.J. and Norvig, P., Artificial Intelligence: A Modern Approach, Englewood Cliffs, NJ: Prentice Hall, 1995.

    MATH  Google Scholar 

  5. Nwana, H.S., Software agents: An overview, Knowl. Eng. Rev., 1996, vol. 11, no. 3, pp. 205–244.

    Article  Google Scholar 

  6. Chen, Y., et al., A negotiation-based multi-agent system for supply chain management, ACM Autonomous Agents Workshop on Agent-Based Decision-Support for Managing the Internet-Enabled Supply-Chain, 1999, pp. 15–20.

    Google Scholar 

  7. Govindu, R., et al., MASCF: A generic process-centered methodological framework for analysis and design of multi-agent supply chain systems, Comput. Ind. Eng., 2007, no. 53(4), pp. 584–609.

    Article  Google Scholar 

  8. Kumar, V., et al., A review of supply chain management using multi-agent system, IJCSI Int. J. Comput. Sci. Issues, 2010, vol. 7, no. 5, pp. 198–205.

    Google Scholar 

  9. Microelectronics. Operating principles of microelectronic devices. http://mikro-elektronika.ru/napravleniyamikroelektroniki

  10. Grekov, L.D., Distributed transportation system functioning simulation based on multi-agent approach, Radioelektron. Komput. Sist., 2008, no. 1(28), pp. 110–113.

    Google Scholar 

  11. Portal of Artificial Intelligence. www.aiportal.ru/articles/multiagent-systems/parameters-and-types-agentinteraction.html

  12. Caire, C., Jade Tutorial–Jade Programming for Beginners, 2003.

    Google Scholar 

  13. Serra, I. and Girardi, R., A process for extracting non-taxonomic relationships of ontologies from text, Intell. Inf. Manage., 2011, vol. 3, no. 4, pp. 119–124.

    Google Scholar 

  14. Plinere, D. and Borisov, A., Development of ontological knowledge model for raw materials management task, Sci. J. Riga Tech. Univ., Inf. Technol. Manage. Sci., 2014, vol. 17, pp. 61–65.

    Google Scholar 

  15. Lozano-Tello, A. and Gómez-Pérez, A., Ontometric: A method to choose the appropriate ontology, J. Database Manage., 2004, vol. 15, no. 2, pp. 1–18.

    Article  Google Scholar 

  16. Meadche, A. and Staab, S., Measuring similarity between ontologies, European Conference Knowledge Acquisition and Management (EKAW), 2002, pp. 251–263.

    Google Scholar 

  17. Brewster, C., Alani, H., Dasmahapatra, S., and Wilks, Y., Data driven ontology evaluation, International Conference on Language Resources and Evaluation, 2004, pp. 164–169.

    Google Scholar 

  18. Porzel, R. and Malaka, R., A task-based approach for ontology evaluation, ECAI Workshop Ontology Learning and Population, 2004, pp. 9–16.

    Google Scholar 

  19. Plinere, D. and Borisov, A., Evaluation of the ontological knowledge model, Sci. J. Riga Tech. Univ., Inf. Technol. Manage. Sci., 2014, vol. 17, pp. 81–85.

    Google Scholar 

  20. Bellifemine, F.L., Caire, G., and Greenwood, D., Developing Multi-Agent Systems with JADE, Wiley, 2007.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to D. S. Plinere.

Additional information

Original Russian Text © D.S. Plinere, A.N. Borisov, L.Ya. Aleksejeva, 2015, published in Avtomatika i Vychislitel’naya Tekhnika, 2015, No. 5, pp. 23–34.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Plinere, D.S., Borisov, A.N. & Aleksejeva, L.Y. Interaction of software agents in the problem of coordinating orders. Aut. Control Comp. Sci. 49, 268–276 (2015). https://doi.org/10.3103/S0146411615050089

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411615050089

Keywords

Navigation