Decision-making information support method realizable in multi-agent system environment

System analysis, control and data processing


Аuthors

Golomazov A. V.

Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia

e-mail: milord-company@yandex.ru

Abstract

The need to improve the quality and efficiency of transportation and logistics management systems requires application of mathematical methods and models contributing to that increasing the level of intellectualization of decisions being made.

For this purpose, the proposed method employs a structure including the interacting subjects monitoring of transportation and logistics process (TLP), and selecting on its basis the significant content of factors of variables and characteristics, representing the basis of their decisions selecting. The article proposes to improve this quality by models and algorithms application, based on deterministic approach and fuzzy logic principles. Structurally, this method was regarded in the form of functional agents complexes developed based on the theory of multi-criteria selection in uncertainty conditions, game theory and principles of potential risks size and direction reduction evaluation of the interacting TLP subjects.

A possibility of periodical or continuous correction of the said content of significant variables appears herewith. This, in its turn, contributes to the decisions being taken adequacy level improvement. The intellectualization level increasing of the decisions being taken is achieved by developing complexes of functional agents being formed for the interacting TLP agents. These agents forming is performed in the method based on both deterministic approach and principles of fuzzy sets theory. Models and algorithms of functional agents allow realize information support of the TLP interacting subjects with regard for the risks and conflict situations. The method includes realized functional agents for negotiations process support, transportations planning and predicting.

The above said agents are interconnected and dynamically functioning in the multi-agent system (MAS) environment. Multi-functional agents developing by this method is base on:

- The multi-criteria theory of selection preferable solutions options in the uncertainty conditions;

- Matrix, bi-matrix, corporate and dynamic games, as well as strategies estimation with regard to Nash equilibrium;

- Ford and Fulkerson methods;

- Travelling salesman problem, knapsack problem and dynamic programming (rational route selection);

- Gantt charts plotting methods, and network scheduling and managing (transportations planning);

- Moving average method and approximation (predicting), ELECTRE method, and fuzzy logic algorithm (negotiation process support).

Keywords:

conflict, risk, situation, decision-making, method, model, criterion, restrictions, logistics, agent

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