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Showing 3 results for Mishmast Nehi
New Solution Space for Solving the Interval Linear Programming Model
M. Allahdadi, H. Mishmast Nehi,
Volume 14, Issue 2 (7-2017)
Abstract
We consider interval linear programming (ILP) problems in the current paper. Best-worst case (BWC) is one of the methods for solving ILP models. BWC determines the values ​​of the target function, but some of the solutions obtained through BWC may result in an infeasible space. To guarantee that solution is completely feasible (i.e. avoid constraints violation), improved two-step method (ITSM) has been proposed. Many solutions are lost in this method. By using an algorithm, we introduce closed ball method (namely, CBM) as a new method for solving ILP models. In this method, feasibility test ensures that solution space is feasible. To demonstrate the effectiveness of the proposed approach, we solve two numeric examples and we compare the results obtained through BWC, ITSM, and CBM.
 

Determining the Optimal Value Bounds of the Objective Function in Interval Quadratic Programming Problem with Unrestricted Variables in Sign
M. Ghorbani Hormazdabadii, H. Mishmast Nehi, M. Allahdadi,
Volume 17, Issue 1 (3-2020)
Abstract
In the most real-world applications, the parameters of the problem are not well understood. This is caused the problem data to be uncertain and indicated with intervals. Interval mathematical models include interval linear programming and interval nonlinear programming problems.A model of interval nonlinear programming problems for decision making based on uncertainty is interval quadratic programming. These types of problems, in which the parameters are inaccurately expressed, are widely used in various sciences, including inventory management, economics, stock selection, engineering design, and molecular study. Interval parameters in these optimization problems cause the value of the objective function to be inaccurate and interval. There are several methods to compute the optimal bounds of the objective function for interval quadratic programming problems. This article examines the most difficult type of interval quadratic programming problems that includes unrestricted decision variables in sign, and provides a new method for determining the bounds of its objective function. In this method, by solving sub-models that include nonnegative variables, the optimal value bounds of the objective function are obtained.
New Approaches to Improve the Determination of Feasible Solutions of the Interval Linear Fractional Programming Problem
F. Salary Pour Sharif Abad, M. Allahdadi, H. Mishmast Nehi,
Volume 18, Issue 3 (9-2021)
Abstract
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.
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