scholarly journals Interval linear programming under transformations: optimal solutions and optimal value range

2018 ◽  
Vol 27 (3) ◽  
pp. 601-614 ◽  
Author(s):  
Elif Garajová ◽  
Milan Hladík ◽  
Miroslav Rada
Keyword(s):  
Linear Programming ◽  
Optimal Solutions ◽  
Interval Linear Programming ◽  
Optimal Value ◽  
Value Range ◽  
Interval Linear ◽  
Optimal Value Range

Bounds on the worst optimal value in interval linear programming

2018 ◽  
Vol 23 (21) ◽  
pp. 11055-11061 ◽  
Author(s):  
Mohsen Mohammadi ◽  
Monica Gentili
Keyword(s):  
Linear Programming ◽  
Interval Linear Programming ◽  
Optimal Value ◽  
Interval Linear

scholarly journals Finding efficient solutions in the interval multi-objective linear programming models

2020 ◽  
pp. 34-34
Author(s):  
Aida Batamiz ◽  
Mehdi Allahdadi
Keyword(s):  
Monte Carlo Simulation ◽  
Linear Programming ◽  
Efficient Solutions ◽  
Expected Value ◽  
Programming Models ◽  
Optimal Solutions ◽  
Interval Linear Programming ◽  
Multi Objective ◽  
Interval Linear ◽  
Ilp Models

The aim of our paper is to obtain efficient solutions to the interval multi- objective linear programming (IMOLP) models. In this paper, we propose a new method to determine the efficient solutions in the IMOLP models by using the expected value and variance operators (EVV operators). First, we define concepts of the expected value, variance, and uncertainty distributions, and present some properties of the EVV operators. Then, we introduce the IMOLP model under these operators. An IMOLP model consist of separate ILPs, but using the EVV operators and the uncertainty distributions, it can be converted into the interval linear programming (ILP) models under the EVV operators (EVV-ILP model). We show that optimal solutions of the EEV-ILP model are the efficient solutions of IMOLP models with uncertainty variables. The proposed method, which is called EVV, is not hard to solve. Finally, Monte Carlo simulation is used to show its the performance assessment.

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