ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Modeling and Optimizing the System Reliability Using Bounded Geometric Programming Approach

Ahmad, S and Ahmad, F and Alam, I and Sayed, AE and Abdollahian, M (2022) Modeling and Optimizing the System Reliability Using Bounded Geometric Programming Approach. In: Mathematics, 10 (14).

[img]
Preview
PDF
mathematics_10-14_2022.pdf - Published Version

Download (476kB) | Preview
Official URL: https://doi.org/10.3390/math10142435

Abstract

The geometric programming problem (GPP) is a beneficial mathematical programming problem for modeling and optimizing nonlinear optimization problems in various engineering fields. The structural configuration of the GPP is quite dynamic and flexible in modeling and fitting the reliability optimization problems efficiently. The work’s motivation is to introduce a bounded solution approach for the GPP while considering the variation among the right-hand-side parameters. The bounded solution method uses the two-level mathematical programming problems and obtains the solution of the objective function in a specified interval. The benefit of the bounded solution approach can be realized in that there is no need for sensitivity analyses of the results output. The demonstration of the proposed approach is shown by applying it to the system reliability optimization problem. The specific interval is determined for the objective values and found to be lying in the optimal range. Based on the findings, the concluding remarks are presented.

Item Type: Journal Article
Publication: Mathematics
Publisher: MDPI
Additional Information: The copyright for this article belongs to the Authors.
Keywords: bounded optimization approach; geometric programming problems; interval-based parameters; system reliability
Department/Centre: Division of Interdisciplinary Sciences > Management Studies
Date Deposited: 24 Sep 2022 06:53
Last Modified: 24 Sep 2022 06:53
URI: https://eprints.iisc.ac.in/id/eprint/76662

Actions (login required)

View Item View Item