Using the method of maximizing deviation to make decision for multiindices
By Yingming Wang. 2012
359 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/b4fa700d0656665b270cd02bd0e204f9e6efaa3c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- Fundamental Properties of Interval‐Valued Pythagorean Fuzzy Aggregation Operators (2016, methodology): “The maximizing deviation method was proposed by Wang 29 to determine the attribute weights for solving MADM problems with numerical information.”
- Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information (2013, methodology): “Wang [44] suggested that zero weight should be assigned to the corresponding attribute.”
- Particle swarm optimization algorithm embedded with maximum deviation theory for solving multi-objective flexible job shop scheduling problem (2015, methodology): “Therefore, maximum deviation theory proposed by Wang [13] has been adopted for ranking the solution to ease the decision-making process of choosing the best solution from a set of nondominated solutions.”
- Hesitant fuzzy entropy and cross‐entropy and their use in multiattribute decision‐making (2012, methodology): “If we use the maximizing deviation method 58 to derive the weight vector of the attributes in Step 2, and use the TOPSIS method 57 to compare the alternatives in Steps 3 and 4, then the following method can be obtained:”
- Managing Multigranular Linguistic Distribution Assessments in Large-Scale Multiattribute Group Decision Making (2017, methodology): “The basic idea of the maximum deviation approach [54], [55] consists of if an attribute makes the collective values among all the alternatives have obvious differences, then it plays an important role in choosing the best alternative.”