Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Bilevel Models for Investment Policy in Resource-Rich Regions. / Lavlinskii, Sergey; Panin, Artem; Plyasunov, Alexander.
Mathematical Optimization Theory and Operations Research - 19th International Conference, MOTOR 2020, Revised Selected Papers. ред. / Yury Kochetov; Igor Bykadorov; Tatiana Gruzdeva. Springer Science and Business Media Deutschland GmbH, 2020. стр. 36-50 (Communications in Computer and Information Science; Том 1275 CCIS).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Bilevel Models for Investment Policy in Resource-Rich Regions
AU - Lavlinskii, Sergey
AU - Panin, Artem
AU - Plyasunov, Alexander
N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7
Y1 - 2020/7
N2 - This article continues the research of the authors into cooperation between public and private investors in the natural resource sector. This work aims to analyze the partnership mechanisms in terms of efficiency, using the game-theoretical Stackelberg model. Such mechanisms determine the investment policy of the state and play an important role in addressing a whole range of issues related to the strategic management of the natural resource sector in Russia. For bilevel mathematical programming problems, the computational complexity will be evaluated and effective solution algorithms based on metaheuristics and allowing solving large-dimensional problems will be developed. This opens up the possibility of a practical study on the real data of the properties of Stackelberg equilibrium, which determines the design of the mechanism for forming investment policies. The simulation results will allow not only to assess the impact of various factors on the effectiveness of the generated subsoil development program but also to formulate the basic principles that should guide the state in the management process.
AB - This article continues the research of the authors into cooperation between public and private investors in the natural resource sector. This work aims to analyze the partnership mechanisms in terms of efficiency, using the game-theoretical Stackelberg model. Such mechanisms determine the investment policy of the state and play an important role in addressing a whole range of issues related to the strategic management of the natural resource sector in Russia. For bilevel mathematical programming problems, the computational complexity will be evaluated and effective solution algorithms based on metaheuristics and allowing solving large-dimensional problems will be developed. This opens up the possibility of a practical study on the real data of the properties of Stackelberg equilibrium, which determines the design of the mechanism for forming investment policies. The simulation results will allow not only to assess the impact of various factors on the effectiveness of the generated subsoil development program but also to formulate the basic principles that should guide the state in the management process.
KW - Bilevel mathematical programming problems
KW - Probabilistic local search algorithm
KW - Stackelberg game
KW - Subsoil development program
UR - http://www.scopus.com/inward/record.url?scp=85092075092&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-58657-7_5
DO - 10.1007/978-3-030-58657-7_5
M3 - Conference contribution
AN - SCOPUS:85092075092
SN - 9783030586560
T3 - Communications in Computer and Information Science
SP - 36
EP - 50
BT - Mathematical Optimization Theory and Operations Research - 19th International Conference, MOTOR 2020, Revised Selected Papers
A2 - Kochetov, Yury
A2 - Bykadorov, Igor
A2 - Gruzdeva, Tatiana
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Conference on Mathematical Optimization Theory and Operations Research,MOTOR 2020
Y2 - 6 July 2020 through 10 July 2020
ER -
ID: 25674955