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An integrated MCDM method for robot selection under interval‐valued Pythagorean uncertain linguistic environment
Author(s) -
Liu HuChen,
Quan MeiYun,
Shi Hua,
Guo Chao
Publication year - 2019
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22047
Subject(s) - pythagorean theorem , vagueness , multiple criteria decision analysis , computer science , closeness , selection (genetic algorithm) , ranking (information retrieval) , quality function deployment , interval (graph theory) , robot , context (archaeology) , artificial intelligence , quality (philosophy) , management science , operations research , mathematics , engineering , fuzzy logic , operations management , geometry , combinatorics , mathematical analysis , paleontology , philosophy , value engineering , epistemology , biology
Robots have received considerable attention in many manufacturing companies due to their great capabilities and characteristics. Selecting an appropriate robot for a specific application can be regarded as a challenging multicriteria decision‐making problem. Furthermore, decision makers are inclined to represent their opinions by using linguistic terms owing to their ambiguous thinking. In this regard, we put forward a novel robot selection model by integrating quality function development (QFD) theory and qualitative flexible multiple criteria method (QUALIFLEX) under interval‐valued Pythagorean uncertain linguistic context. For the developed model, the evaluations given by decision makers are presented as interval‐valued Pythagorean uncertain linguistic sets for dealing with the uncertainty and vagueness of decision makers’ information. An extended QFD method is used for determining criteria weights from the perspective of customers. A modified QUALIFLEX technique based on closeness degree is utilized to generate the ranking order of alternative robots and determine the most suitable one. Finally, an empirical example of an auto manufacturing company is applied to clarify the effectiveness and accuracy of the proposed robot selection approach.