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Integrated Ranking Algorithm for Efficient Decision Making
International Journal of Information Technology & Decision Making ( IF 2.5 ) Pub Date : 2021-02-26 , DOI: 10.1142/s0219622021500152
N. Deepa, B. Prabadevi, Gautam Srivastava

Decision making remains a prominent issue in all the problem domains. To make better decisions, multiple factors of the given problem need to be considered and evaluated. Multi-criteria decision-making methods have been used popularly for solving decision-making problems characterized by multiple factors. When multiple factors are considered, it is recommended to categorize the factors into the main criteria and sub-criteria. In this paper, GRAP-an integrated ranking algorithm has been developed by combining Grey Relational Analysis, Rank Sum, and Preference Ranking Organization Method Enrichment Evaluation methods (PROMETHEE) to solve decision-making problems. The weights of the sub-criteria are calculated using the Rank Sum method. Grey Relational Analysis method is used to convert the sub-criteria values into main criteria values in the form of evaluation scores of alternatives. The final ranking scores of the alternatives are obtained using the PROMETHEE method. A decision model is developed using the proposed GRAP algorithm and applied to the Job Profile selection case study. The developed decision model showed much better results compared to other MCDM approaches namely the Simple Additive Weight method, TOPSIS, VIKOR, and Complex Proportional Assessment (COPRAS). Further, a sanity check has been carried out by comparing the results of the decision model with experts’ opinions.

中文翻译:

高效决策的综合排名算法

决策仍然是所有问题领域中的一个突出问题。为了做出更好的决策,需要考虑和评估给定问题的多个因素。多准则决策方法已广泛用于解决以多因素为特征的决策问题。当考虑多个因素时,建议将因素分类为主要标准和子标准。在本文中,结合灰色关系分析、秩和和偏好排序组织方法富集评估方法(PROMETHEE)开发了一种集成排序算法 GRAP 来解决决策问题。子标准的权重使用秩和法计算。采用灰色关联分析法,以备选方案评价分数的形式,将子标准值转换为主要标准值。备选方案的最终排名分数是使用 PROMETHEE 方法获得的。使用提出的 GRAP 算法开发了一个决策模型,并将其应用于 Job Profile 选择案例研究。与其他 MCDM 方法(即简单加性加权方法、TOPSIS、VIKOR 和复杂比例评估 (COPRAS))相比,开发的决策模型显示出更好的结果。此外,通过将决策模型的结果与专家的意见进行比较,进行了健全性检查。使用提出的 GRAP 算法开发了一个决策模型,并将其应用于 Job Profile 选择案例研究。与其他 MCDM 方法(即简单加性加权方法、TOPSIS、VIKOR 和复杂比例评估 (COPRAS))相比,开发的决策模型显示出更好的结果。此外,通过将决策模型的结果与专家的意见进行比较,进行了健全性检查。使用提出的 GRAP 算法开发了一个决策模型,并将其应用于 Job Profile 选择案例研究。与其他 MCDM 方法(即简单加性加权方法、TOPSIS、VIKOR 和复杂比例评估 (COPRAS))相比,开发的决策模型显示出更好的结果。此外,通过将决策模型的结果与专家的意见进行比较,进行了健全性检查。
更新日期:2021-02-26
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