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 objective decision making
Zahra Shoaei Naeini; Parastoo Mohammadi; Ali Husseinzadeh Kashan
Abstract
The oil consortium in Iran is one of the most important approaches to implementing the huge projects of the petrochemical industry. But the selection of suitable and expert partners is one of the most common bottlenecks in such cooperation networks. The purpose of this paper is to provide a practical, ...
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The oil consortium in Iran is one of the most important approaches to implementing the huge projects of the petrochemical industry. But the selection of suitable and expert partners is one of the most common bottlenecks in such cooperation networks. The purpose of this paper is to provide a practical, yet simple, solution for the decision makers to be able to choose the right candidate from the selected candidates. For this purpose, a three-stage model has been designed. In the first stage, the criteria affecting the choice of partner from the perspective of experts and reviewing the literature were first collected and weighed with the help of SWARA method. In the next stage, the ranking of partners (6 domestic and 4 foreign companies) was based on a set of decision-making methods such as COPRAS, VIKORA, SWA, TOPSIS, ARAS, MOORA, and multi-MOORA. The final stage integrated the ranking results based on the Copeland. In the end, financial capability and debt ratio and repayment potential were introduced as the most important criteria and sub-criteria. Also, partner 3 was selected as the best candidate by Copeland. Finally, in order to measure the performance of the integration of results, the Spearman correlation coefficient was used and the results of high test affinity and integration of results were obtained. Therefore, it can be said that the approach used has performed well.
meta-heuristic algorithms
Ehsan Aghdaee; Ali Husseinzadeh Kashan
Abstract
In managing a project, reliable prediction is an essential element for success. Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits. For a long time, the earned value management (EVM) for pursuing time performance and the cost of ...
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In managing a project, reliable prediction is an essential element for success. Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits. For a long time, the earned value management (EVM) for pursuing time performance and the cost of the project has been used. However, using this method to valuate project time performance by utilizing the time performance index (SPI) by researchers and practitioners has been faced with serious criticism. Therefore, the present study proposes a framework for assessment and prediction of the temporal performance of each of the thread activities in project management. In this framework, using the multi objective league championship algorithm (MOLCA), the initial plan of the projects is optimized and then via using the Kalman Filter prediction method, project execution planning is done such that the projects in conditions of uncertainty could be forecasted and ahead horizon being demonstrated accurately with the least error for project managers. In this paper, in order to ensure the quality of the solutions, the output of the algorithm is compared with genetic algorithms (NSGII) and particle swarm optimization (MOPSO), where results demonstrate the superiority of the proposed algorithm.