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In this paper, a hybrid method based on a linear programming model for solving Multi-Attribute Decision-Making (MADM) problems by combining two new methods, the COmplex PRoportional ASsessment (COPRAS) and the Multi-Objective Optimization Ratio Analysis (MOORA) and also using the concept of discrimination intensity functions are presented. Further interaction with the Decision Maker (DM) to determine the weights of the attributes and calculate the weights by solving a linear programming problem without determining the predetermined weight are two of the advantages of the new method. In the proposed method, for each alternative, attributes are weighted with optimism for that alternative, and then alternatives are ranked through efficiency intervals. The proposed method is implemented on a real-world problem derived from the subject literature and compared with other MADM methods. The difference in the final results is evident due to the consideration of more details in determining the rankings. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to provide a hybrid and improved version of one of the Multi-Attribute Decision-Making (MADM) methods, which is a more useful tool for decision support due to the constructive interaction with the decision-maker. Methodology: For this purpose, a linear programming problem with weight restriction and discrimination intensity functions was used to present the improved WASPAS method, and thus, when the explicit weight for the indicators was not taken from the decision maker, it was possible to rank the options. Findings: The results of comparing the use of the proposed method with the WASPAS method showed that this method has a good capability for use in multi-criteria decision making problems and in this particular issue, the results of using this method showed that in the context of the Covid-19 epidemic, the supportive leadership style had the highest rank among the leadership styles, followed by the transactional leadership, participatory, transformational and was placed authoritative. Originality/Value: In this study, the improved WASPAS method was presented to determine the priority and weight of criteria in solving MADM problems and to determine the organizational leadership style in the Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a mathematical programming technique for efficiency analysis. For dealing with the data in ratio form, the DEA model for ratio data known as DEA-R exists in the literature. However, some ratio data like financial risk may be negative naturally. In this paper, we contribute to the literature in two ways. In the first place, we deal with DEA-R models in the presence of negative ratio data by proposing an inverse DEA model for merger analysis. In the second place, we develop DEA-R models for merger analysis that can deal with negative data. We apply our models in a real-world application of efficiency and merger analysis of an Iranian bank with 66 branches. The proposed models maintain data confidentiality. This motivates managers to participate in the evaluation and merger process. Our models also provide a reasonable endogenous weight restriction framework without restricting weights exogenously. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making (MADM) methods are widely used by decision makers as decision support tools. Most MADM methods have shortcomings in the solution process that combined with other decision making methods can eliminate these shortcomings or improve the performance of the method. One of the methods that can be used to improve MADM methods is preferential voting, which is actually a linear programming (LP) model with weight restrictions. The Kemeny Median Indicator Ranks Accordance (KEMIRA) is one of the most modern MADM methods; in this paper, we provide an improved version in this relative, by utilizing the concept of preferential voting. The new model, in being implemented on a real-world problem, will be compared to the previous method and ultimately some of its advantages will be rendered. [ABSTRACT FROM AUTHOR]