Toward Thermodynamics of Real-time Scheduling

Toward Thermodynamics of Real-time Scheduling

I. Mayorov | P. Skobelev

Samara State Technical University, Russia

Smart Solutions, Ltd, Russia

Samara State Aerospace University, Russia

Page: 
213-223
|
DOI: 
https://doi.org/10.2495/DNE-V10-N3-213-223
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

The modern problem of real-time resource management to increase enterprise efficiency is considered.

A new look at the dynamic self-organizing processes based on multi-agent technologies in building and revising schedules by events in real time is suggested. Schedule is considered as a flexible network of operations of demand and resource agents. This schedule is formed during the interactions of basic agent classes that set and break the dynamic links between each other, depending on the events and changing situation in the real world.

A thermodynamic model of demand–resource network (DRN) dynamics is introduced. There is a similarity to Ilya Prigogine’s non-linear thermodynamics theory which allows us to explain the phenomenon of unstable equilibrium emergence, order and chaos, catastrophes, bifurcations and other non-linear events that are significant to the self-organizing processes control in multi-agent systems (MASs).

Keywords: 

adaptability, chaos and order, complex systems, demand–resource network, multi-agent technology, network dynamics model, non-equilibrium, real-time scheduling, self-organizing

  References

[1] Skobelev, P., Vittikh, V. Models of Self-organization for Designing Demand-Resource  Networks, Automation and Remote Control. Journal of Russian Academy of Science, (1), pp. 177–185, 2003.

[2] Vittikh, V., Skobelev, P. The compensation method of agents interactions for real time  resource allocation, Avtometriya. Journal of Siberian Branch of Russian Academy of Science, (2), pp. 78–87, 2009.

[3] Prigogine, I., Stengers, I. Order out of Chaos: Man’s new dialogue with nature. Flamingo, 1984.

[4] Prigogine, I., Nicolis, G. Self-Organization in Non-Equilibrium Systems. Wiley. 1977.

[5] Leung, Y.-T. Handbook of Scheduling: Algorithms, Models and Performance Analysis, CRC Computer and Information Science Series, Chapman & Hall, London, 2004.

[6] Shirzadeh Chaleshtari, A., Shadrokh, S. A Branch and Bound Algorithm for Resource  Constrained Project Scheduling Problem subject to Cumulative Resources, World Academy of  Science, Engineering and Technology, 6, pp. 23–28, 2012.

[7] Wooldridge, M. An Introduction to Multi-Agent Systems, John Wiley & Sons, London, 2002.

[8] Shoham, Y., Leyton-Brown, K. Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, New York, 2009.

[9] Pechoucek, M., Marík., V. Industrial deployment of multi-agent technologies: review and  selected case studies. Autonomous Agents and Multi-Agent Systems, 17(3), pp. 397–431, 2008. doi: http://dx.doi.org/10.1007/s10458-008-9050-0

[10] Leitao, P., Vrba, P. Recent Developments and Future Trends of Industrial Agents, Proceedings of the 5th International Conference on Holonic and Multi-Agent Systems in Manufacturing, Springer, Berlin, pp. 15–28, 2011. doi: http://dx.doi.org/10.1007/978-3-642-23181-0_2

[11] Brussel, H.V., Wyns, J., Valckenaers, P., Bongaerts, L. Reference architecture for  holonic m anufacturing systems: PROSA. Computer in Industry, 37(3), pp. 255–274, 1998. doi: http://dx.doi.org/10.1016/S0166-3615(98)00102-X

[12] Skobelev, P. Multi-Agent Systems for Real Time Resource Allocation, Scheduling, Optimization and Controlling: Industrial Applications (Invited Talk). Proceedings of the 5th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, 6867, pp. 1–14, 2011. doi: http://dx.doi.org/10.1007/978-3-642-23181-0_1

[13] Rzevski, G., Skobelev, P. Managing complexity. WIT Press, UK-USA, 2014.

[14] Zadeh, L.A. On the definition of adaptivity. Proceedings of the IEEE, 51(3), pp. 469–470, 1963. doi: http://dx.doi.org/10.1109/PROC.1963.1852

[15] Granichin, O., Skobelev, P., Lada, A., Tsarev, A. Cargo transportation models analysis using multi-agent adaptive real-time truck scheduling system. Proceedings of the 5th  International Conference on Agents and Artificial Intelligence (ICAART’2013), February 15-18, 2013,  Barcelona, Spain. – SciTePress, Portugal, 2, pp. 244–249, 2013.

[16] Braess’s paradox. [online] Available at: https://en.wikipedia.org/wiki/ Braess’s_paradox [20 January 2015].