Volume 18, Issue 3 (9-2021)                   2021, 18(3): 31-48 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Salary Pour Sharif Abad F, Allahdadi M, Mishmast Nehi H. Presentation New Approaches to Improve the Determination of Feasible Solutions of the Interval Linear Fractional Programming Problem. Journal of Operational Research and Its Applications. 2021; 18 (3) :31-48
URL: http://jamlu.liau.ac.ir/article-1-1806-en.html
Mathematics Faculty, University of Sistan and Baluchestan, Zahedan, Iran
Abstract:   (178 Views)
In this research, the interval linear fractional programming model is considered. Since this model is an interval model, hence we are looking for methods where an optimal solution set is obtained. In this paper, we suggest two methods for the determination optimal solution set of the ILFP model so that these methods are formed from two sub-models. The obtained solutions solving these two sub-models form a region that we consider it as optimal solution set of the ILFP. If the obtained solution satisfies in largest region of interval constraints of the ILFP model, the solution is called feasible. In the first method, we gain an optimal solution set that some of its points may not satisfy some constraints of the largest region, hence we use an alternative method to improve the optimal solution set such that we will able to remove the infeasible part of the optimal solution set of the first method by an alternative method and obtain a feasible optimal solution set. In the second method, to ensure that the optimal solution set is completely feasible, we add a supplementary constraint to the second sub-model and we obtain a feasible optimal solution set.
Full-Text [PDF 922 kb]   (67 Downloads)    
Type of Study: Research | Subject: Special
Received: 2020/02/29 | Accepted: 2021/02/22

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.