In this research paper, we try to solve the problem of the expansion of the resource conversion process (RCP) of multi-agent model that is used to solve modeling and decision-making problems in the field of production and business processes, as well as organizational and technical systems. To test the coalition model, we took data from the window construction market model. The window structures department of Ural Industrial Group CJSC is engaged in the production and sale of plastic windows.
of the analysis of these models presented in [
Model of Resource Conversion Processes
The basis is the author's hybrid model, built as a result of the integration of simulation, expert, situational and multi-agent
modeling for creating a coalition model of RCP [
SPC1
С1 KBС1 PR2
U2 U6 Op4 Mech 4 MsgС1,A2 MsgA2,С1 Res1, Res2
A2
KBA2 A4 KBA4 U1
PR1
Mech 1-5 SPA2 SPA3 M.s..g1 Msgn С1 Mech 1 Mech 2 Res4 MsgС1,A3 MsgA3,С1 A3 KBA3 U7
Res 5-7 Op5 Mech 5 U4 Op1 Mech6, Mech7 Res3
Res8, Res9 Op3
U3 Mech 3
U5 Op2 Res5 Res6 Res7
SPA1
A1 KBA1 Res9, Res10
To test the coalition model, we took data from the window construction market model, previously implemented in the first version of BPsim.MAS. The window structures department of Ural Industrial Group CJSC (UIG CJSC) is engaged in the production and sale of plastic windows. The practical task of improving the window construction market model is to analyze the options for holding organization as a result of the merger of CJSC “UIG” with one of the players. The motive is to increase the profitability of the business of merged enterprises (by reducing overhead costs, increasing production and sales) for forming a coalition. A prerequisite is a developed sales network of one agent and a good production base of the second agent for the formation of a coalition.
In the example that is shown in Fig. 1, agents A2 and A3 act as participants of the coalition, while the coalition C1 itself can act as a supervisor. A multi-agent simulation model of the resource transformation process that supports the functions of forming and breaking up coalitions should be able to carry out the following structural and parametric changes at the moments of forming / breaking up a coalition: 1) linking / breaking of operations (processes) of agents; 2) enabling / disabling individual blocks of the model and agent rules; 3) separation / association of resources and funds; 4) changing in the state of resources, funds, applications; 5) a dynamic changing in the priorities of operations and the rules of agents for the consumption / usage of resources and means. These changes in the model should be supported during the simulation experiment.
The introduction of coalitions and communication into the expansion model broadens the possibilities of modeling conflicts that arise on common resources and means. Conflict resolution can be implemented on the basis of communication (exchanging messages, conducting auctions) and coalitions (conflict resolution rules can be described in the coalition agent). The processes presented in Fig. 2 function in the coalition model of RCP.
The multi-agent modeling algorithm developed for the practical implementation coalition model of the RCP is
presented in [
The algorithm is supplemented by the following two stages: 1) formation / collapse of the coalition; 2) making structural and parametric changes in the dynamic model of RCP.
The general interaction is implemented in the basis of the InteRRap architecture [9] of the subsystems of the hybrid model, the application is presented in (Fig. 3) [10] of the architecture to the model of RCP. The processes of collapse and coalition formation are implemented through an intelligent agent developed in the planning subsystem (built on the basis of a frame expert system). Decision support systems BPsim.MAS and BPsim.DSS are software modules that implement the coalition model of RCP. The appearance is shown in Fig. 4 of the multi-agent simulation model in BPsim.MAS.
2) by expanding the knowledge bases of agents or behavior patterns by rules / actions of using / managing / consuming common resources, means, applications as well as a system for resolving internal conflicts of a coalition; 3) by developing a coalition agent (using the existing capabilities of BPsim.MAS). Moreover, the agent-coalition model should take into account both the behavior models of individual agents and the general rules for the distribution of resources, funds and applications;
The implementation is as follows of communications between different types of agents of RCP models: 1) the exchange of messages between agents within the dynamic model of RCP (reactive-intelligent agents and reactive agents) is carried out by introducing applications (messages) into the model of the dynamic process, introducing commands and command syntax for the problem being solved (specific subject area) and describing message processing rules in the agent model;
2) the exchange of messages between agents of the dynamic model of RCP and intelligent agents (in the frame-object expert system) is carried out through a message clipboard containing common variables used in BPsim.MAS dynamic simulation modules and BPsim.DSS technical and economic design modules. 4
Conclusion In this research paper, the task was solved of developing a coalition multi-agent model of the resource conversion process, as well as the following tasks: - the basic concepts were defined of a coalition model of the resource transformation process; - a multi-agent modeling algorithm that takes into account the stages of coalition formation and collapse, was developed as well as the possibility of making structural and parametric changes to the dynamic model of the resource conversion process;
- the possibility was shown of implementing applied multi-agent models with coalitions in the BPsim software package. 4.1.1
Acknowledgements
This research paper is supported by 211 acts of the Government of the Russian Federation, agreement No. 02.A03.21.0006. 7. Shorikov A.F.: Solution of the Two-Level Hierarchical Minimax Program Control Problem in a Nonlinear DiscreteTime Dynamical. In: 2th IFAC Conference on Modelling, Identification and Control of Nonlinear Systems. Book of Abstracts. University of Guadalajara, Mexico, P. 33 (2018). 8. Khalyasmaa, A. I., Zinovieva, E. L., Intelligent decision support system for technical solutions efficiency assessment, Proceedings of 2017 IEEE 2nd International Conference on Control in Technical Systems, CTS, 2017 9. Muller J.P. &M.Pischel. 1993. The Agent Architecture InteRRaP: Concept and Application, German Research
Center for Artificial Intelligence (DFKI). 1993. 10. K. Aksyonov, E. Bykov, E. Smoliy, E. Sufrygina, O. Aksyonova and Wang Kai Development and Application of Decision Support System BPsim.DSS // Proceedings of the IEEE 2010 Chinese Control and Decision Conference (CCDC 2010), 26-28 May 2010, Xuzhou, China, Pages 1207-1212. WOS:000290460300245 DOI: 10.1109/CCDC.2010.5498160