نوع مقاله : یادداشت فنی
نویسندگان
دانشکده مهندسی صنایع، دانشگاه آزاد اسلامی واحد اراک
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Classic Linear Assignment method is a multi-criteria decision making method, which takes the weight of criteria into account, and each ranking is assigned to one, and only one alternative. In order to omit the need of calculating the weight of criteria, to use the priority of decision- making criteria,, and to be able to assign each ranking to more than one alternative, a multi- objective linear programming method is suggested in this paper in which an objective function is defined for each criterion, to optimize alternatives based on that particular criterion. The objective function of each criterion consists of the total related performance point variables in the linear assignment model so that the best alternative, which is optimum in terms of all criteria, is chosen in the end. There are as many objective functions as criteria which all contribute to a Multi- Objective Linear Programming. Then, regarding the
priority of the criteria, the multi- objective linear programming model is solved using Absolute Priorities Method. In the suggested method, measuring the weight of criteria is not required; however, the degree of the importance of each criterion is taken into account. Decision- makers exert their opinion on the final ranking by prioritizing the criteria. Constraints posed by the need to assign each ranking to only one alternative are removed in this model. Global Criterion method can be used to solve the resulting MOLP problem, too. In addition, when adding new criteria to the problem, as many new objective functions are added to, hence, there will be no need to recalculate the weight of criteria and do the calculations required in the linear assignment method. Calculating Spearman's Correlation Coefficient shows that the proposed model in comparison with Classic Linear Assignment method is more consistent with the most commonly used multi- criteria decision-making methods: TOPSIS, VIKOR, and MOORA.
کلیدواژهها [English]
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