Open AccessMethods for the determination of effective management decisions in insufficient information conditions
Author(s) -
В.В. Моисеев,
M. Yu. Karelina,
T. Yu. Cherepnina,
Е. А. Карелина
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1353/1/012119
Subject(s) - computer science , a priori and a posteriori , ranking (information retrieval) , set (abstract data type) , management science , basis (linear algebra) , completeness (order theory) , mathematical optimization , operations research , mathematics , machine learning , mathematical analysis , philosophy , geometry , epistemology , economics , programming language
The article analyzes the methods of removing uncertainty in conditions of insufficient information for making effective management decisions in complex organizational systems. In such tasks, the choice of an effective solution depends on the state of the external environment of the study and implies the use of a number of methods or mathematical models for solving multi-criteria problems. The main methods used in cases of occurrence of a situation of uncertainty due to insufficient information are the a priori ranking methods and multiple regression analysis. However, methods based on expert judgment have a significant degree of subjectivism, and methods formed on the experimental basis and mathematical planning of research are laborious and generate uncertainty with a large number of objects or elements involved in the situation. An alternative to the above is the method of zoning, which implies dividing the set of possible states of the research environment (nature) into subsets of the dominance of individual actions. The zoning operation is an inverse parametric linear programming problem. Consequently, for solving problems in conditions of insufficient information, one can use vector optimization methods.