multi objective decision making
Mehdi Allahdadi; Fatemeh Salary Pour Sharif Abad; Hassan Mishmast Nehi
Abstract
Purpose: Determining efficient solutions of the Interval Multi Objective Linear Fractional Programming (IMOLFP) model is generally an NP-hard problem. For determining the efficient solutions, an effective method has not yet been proposed. So, we need to have an appropriate method to determine the efficient ...
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Purpose: Determining efficient solutions of the Interval Multi Objective Linear Fractional Programming (IMOLFP) model is generally an NP-hard problem. For determining the efficient solutions, an effective method has not yet been proposed. So, we need to have an appropriate method to determine the efficient solutions of the IMOLFP. For the first time, we want to introduce algorithms in which the strongly and weakly efficient solutions of the IMOLFP are obtained.Methodology: In this paper, we introduce two algorithms such that in one, strongly feasible of inequalities and in the other, weakly feasible of inequalities are considered (A system of inequalities is strongly feasible if and only if the smallest region is feasible, and a system of inequalities is weakly feasible if and only if the largest region is feasible). We transform the objective functions of the IMOLFP to real linear functions and then convert to a single objective linear model and then in each iteration of the algorithm, we add some new constraints to the feasible region. By selecting an arbitrary point of the feasible region as start point and using the proposed algorithms, we obtain the strongly and weakly efficient solutions of the IMOLFP.Findings: In both proposed algorithms, we obtain an efficient solution by selecting the arbitrary points, and by changing the starting point, we obtain a new point as the efficient solution.Originality/Value: In this research, for the first time, we have been able to obtain the strongly and weakly efficient solutions of the IMOLFP.
Multi-Attribute Decision Making
Abazar Keikha; Hassan Mishmast Nehi
Abstract
Purpose: Using hesitant fuzzy numbers as a combination of two common types of evaluation: self-evaluation and evaluation by judges, in order to make real and fair evaluations. Updating the Choquet integral method to apply with hesitant fuzzy numbers in the evaluation process, and use it to solve decision ...
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Purpose: Using hesitant fuzzy numbers as a combination of two common types of evaluation: self-evaluation and evaluation by judges, in order to make real and fair evaluations. Updating the Choquet integral method to apply with hesitant fuzzy numbers in the evaluation process, and use it to solve decision problems such as evaluating employees and organizations. Methodology: The method of conducting these studies is based on the pattern of library studies.</pFindings: Deficiencies such as showcasing the evaluators during the evaluation period on the one hand, and the lack of mastery of external judges on some organizational complexities and the apparent and hidden motivations of the evaluators for unrealistic evaluation in the self-evaluation process, on the other hand, are some of factors that challenge the evaluation results, and these defects in the hybrid evaluation model are eliminated using hesitant fuzzy numbers. In addition, evaluation indicators in many cases interact with each other and have so-called positive and negative effects on each other. Choquet Integral is able to take this into account and take the assessment one step closer to becoming more realistic. Therefore, its computational development with hesitant fuzzy numbers, which has been considered in this article, can helps the evaluation system and performance of employees and organizations.Originality/Value: Computational development of hesitant fuzzy numbers with the help of Choquet integral, using the Choquet integral of hesitant fuzzy numbers in solving multi-criteria decision making problems such as employee and organizational evaluation.
Linear Optimization
Mehdi Allahdadi; Hasan Mishmast Nehi
Abstract
In this paper, solution space of interval linear programming (ILP) models that is a NP-hard problem, has been considered. In all of the solving methods of the ILP, feasibility condition has been only considered. Best-worst case (BWC) is one of the methods for solving the ILP models. Some of the solutions ...
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In this paper, solution space of interval linear programming (ILP) models that is a NP-hard problem, has been considered. In all of the solving methods of the ILP, feasibility condition has been only considered. Best-worst case (BWC) is one of the methods for solving the ILP models. Some of the solutions obtained by the BWC may result in an infeasible space. To guarantee that solution is completely feasible, improved two-step method (ITSM) is proposed. By using a new approach, we introduce a space for solving ILP models in which by two tests, feasibility and optimality of the obtained space has been guaranteed.