Issue |
RAIRO-Oper. Res.
Volume 58, Number 5, September-October 2024
|
|
---|---|---|
Page(s) | 3697 - 3714 | |
DOI | https://doi.org/10.1051/ro/2024137 | |
Published online | 10 September 2024 |
Some new results on rough interval linear programming problems and their application to scheduling and fixed-charge transportation problems
1
Mathematics Faculty, University of Sistan and Baluchestan, Zahedan, Iran
2
Department of Mathematics, Faculty of Basic Science, Babol Noshirvani University of Technology, Babol, Iran
* Corresponding author: m_allahdadi@math.usb.ac.ir
Received:
31
October
2023
Accepted:
27
June
2024
This paper focuses on linear programming problems in a rough interval environment. By introducing four linear programming problems, an attempt is being made to propose some results on optimal value of a linear programming problem with rough interval parameters. To obtain optimal solutions of a linear programming problem with rough interval data, constraints of the four proposed linear problems are applied. In this regard, firstly, the largest and the smallest feasible spaces for a linear constraint set with rough interval coefficients and parameters are introduced. Then, a rough interval for optimal value of such problems is obtained. Further, an upper approximation interval and a lower approximation interval as the optimal solutions of linear programming problems with rough interval parameters are achieved. Moreover, two solution concepts, surely and possibly solutions, are defined. Some numerical examples demonstrate the validity of the results. In particular, a scheduling problem and a fixed-charge transportation problem (FCTP) under rough interval uncertainty are investigated.
Mathematics Subject Classification: 90C05 / 65G40 / 60L99
Key words: Interval linear programming / rough interval / optimal solution / optimal value
© The authors. Published by EDP Sciences, ROADEF, SMAI 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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