A utility theory based robot selection and evaluation for electronics assembly
Computers and Industrial Engineering
A computational evaluation of the original and revised analytic hierarchy process
Computers and Industrial Engineering
An introduction to fuzzy control (2nd ed.)
An introduction to fuzzy control (2nd ed.)
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Industrial Robotics: Technology, Programming and Application
Industrial Robotics: Technology, Programming and Application
Computers and Industrial Engineering
Fuzzy Sets and Systems
Optimal selection of robots by using distance based approach method
Robotics and Computer-Integrated Manufacturing
A fuzzy digraph method for robot evaluation and selection
Expert Systems with Applications: An International Journal
Development of a decision support system for robot selection
Robotics and Computer-Integrated Manufacturing
Fuzzy optimality based decision making under imperfect information without utility
Fuzzy Optimization and Decision Making
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The problem of Robot Selection is of great relevance in the present times of automation. Traditionally such problems were addressed using conventional techniques of Multi Criteria Decision Making such as The Analytic Hierarchy Process (AHP) and The Multi Attribute Utility Theory (MAUT). This paper proposes a methodology for solving common Robot Selection problems using a modification of the conventional AHP by incorporating `Fuzzy Linguistic Variables' in place of numbers. The methodology encapsulates creation of Fuzzy Interface for conversion of input and output variables into suitable linguistic variables. Further, employing the fuzzification process by assigning the linguistic variables to numerical values of the membership functions and formulating suitable decision rules, the procedure culminates into the defuzzification process for converting fuzzy output into crisp value and obtaining the result in the form of Fuzzy Score. The proposed model is explained using a numerical example. The paper also presents a validation of the proposed methodology over real world problems and provides directions for future research towards the end.