The Multi-agent Method for Real Time Production Resource-Scheduling Problem
An operational scheduling method of production resources for enterprises has been analyzed and is being proposed. In order to assemble a client’s order, it is necessary to produce each detail by making the number of technological operations via an appropriate production resource. For scheduling and managing the production process, it is necessary to define the whole structure of the final assembly with a technology map. This representation is proposed by using a special ontological definition, and give the example for an enterprise producing electrical products. The process of scheduling has a high level of complexity due to the variety of types of resources used, and the dependence of production processes on many factors and conditions. Also considered real time events and each time getting information about a new fact of processing of each detail on each resource, the current production plan has to be rescheduled. Traditional methods for solving the problem are not possible using in real time scheduling, which is why it is proposed the multi-agent approach for that task. The developed system based on the proposed method is used by the real enterprise produces electrical products in Samara city, where, as a result, the number of delays in the execution of production orders was reduced by 10%.
KeywordsMulti-agent methods Production resource management Ontology of the production enterprise Real-time scheduling
The paper has been prepared based on the materials of scientific research within the subsidized state theme of the Institute for Control of Complex Systems of the Russian Academy of Sciences for research and development on the topic: «Research and development of methods and means of analytical design, computer-based knowledge representation, computational algorithms and multi-agent technology in problems of optimizing management processes in complex systems».
- 1.Kantorovich, L.V.: Mathematical Methods of Organization and Production Planning. Leningrad State University, Leningrad (1939). (in Russian)Google Scholar
- 3.Chapman, S.N.: The Fundamentals of Production Planning and Control. Prentice Hall, Upper Saddle River, 272 p. (2006)Google Scholar
- 4.Wooldridge, M.: An Introduction to Multi-Agent Systems. Wiley, Hoboken, 484 p. (2009)Google Scholar
- 5.Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York, 833 p. (2010)Google Scholar
- 6.Jennings, N.R., Wooldridge, M.J. (eds.): Agent Technology: Foundations, Applications, and Markets. Springer, Heidelberg, 325 p. (2012)Google Scholar
- 7.Vittikh, V.A., Moiseeva, T.V., Skobelev, P.O.: Making decisions on the basis of consensus using multi-agent technologies. Ontol. Des. 2(8), 20–25 (2013). (in Russian)Google Scholar
- 8.Skobelev, P.: Towards autonomous AI systems for resource management: applications in industry and lessons learned. In: Proceedings of the XVI International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2018). LNAI, vol. 10978, pp. 12–25. Springer (2018). https://doi.org/10.1007/978-3-319-94580-4_2CrossRefGoogle Scholar
- 9.Rzevski, G., Skobelev, P.: Managing Complexity. Wit Press, 216 p. (2014)Google Scholar
- 10.Amelina, N., Granichin, O., Granichina, O., Ivanskiy, Y., Jiang, Y.: Differentiated consensuses in a stochastic network with priorities. In: Proceedings of the 2014 IEEE International Symposium on Intelligent Control, 8–10 October 2014, Antibes, Nice, France, pp. 264–269 (2014)Google Scholar
- 11.Skobelev, P., et al.: Practical approach and multi-agent platform for designing real time adaptive scheduling systems. In: Proceedings of the XII International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2014). CCIS, vol. 0430, pp. 1–12. Spinger (2014)Google Scholar
- 12.Skobelev, P.: Multi-agent systems for real time adaptive resource management. In: Leitão, P., Karnouskos, S. (eds.) Industrial Agents: Emerging Applications of Software Agents in Industry, pp. 207–230. Elsevier (2015)Google Scholar
- 13.Leung, J.: Handbook of Scheduling: Algorithms, Models and Performance Analysis. Chapman & Hall, CRC Computer and Information Science Series, 1216 p. (2004)Google Scholar