IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Bi-Level Programming for Earning Management in Imprecise and Random Environments

Bi-Level Programming for Earning Management in Imprecise and Random Environments
View Sample PDF
Author(s): Vishnu Pratap Singh (Visvesvaraya National Institute of Technology, Nagpur, India)
Copyright: 2021
Pages: 21
Source title: Comparative Research on Earnings Management, Corporate Governance, and Economic Value
Source Author(s)/Editor(s): Elisabete S. Vieira (University of Aveiro, Portugal), Mara Madaleno (University of Aveiro, Portugal)and Graça Azevedo (University of Aveiro, Portugal)
DOI: 10.4018/978-1-7998-7596-3.ch006

Purchase

View Bi-Level Programming for Earning Management in Imprecise and Random Environments on the publisher's website for pricing and purchasing information.

Abstract

Organizations striving in today's environment of active technological and business transformations are confronted with the difficulties of “twofoldness,” that is, performing efficiently in the present while innovating effectively for the future. Administrators inside these organizations not only have to concentrate on the business benefit and profitability of each of their authorized commodities and services but must also guarantee their ability to introduce into next-generation contributions that output properties that will maintain and even enhance their renewed global competitiveness. The surprisingly fast breakdown of so many probably great companies over the last decade gives an extensive declaration to the consequence of accomplishing this dualism. In this chapter, to deal with this dualism, the authors consider a fuzzy stochastic bi-level programming problem in the mathematical models. The fuzziness and randomness concept has been taken care of by the fuzzy random variable as the parameter of the bi-level programming problem. A two-stage approach has been defined to solve the problem.

Related Content

Sonal Linda. © 2024. 24 pages.
Yasmin Yousaf Mossa, Peter Smith, Kathleen Ann Bland. © 2024. 40 pages.
Ugochukwu Okwudili Matthew, Jazuli Sanusi Kazaure, Charles Chukwuebuka Ndukwu, Godwin Nse Ebong, Andrew Chinonso Nwanakwaugwu, Ubochi Chibueze Nwamouh. © 2024. 29 pages.
Shruti Jose, Priyakrushna Mohanty. © 2024. 20 pages.
Richa Srishti. © 2024. 15 pages.
Aleksei Alipichev, Liudmila Nazarova, Yana Chistova. © 2024. 21 pages.
Mustafa Öztürk Akcaoğlu, Burcu Karabulut Coşkun. © 2024. 18 pages.
Body Bottom