| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 380414 | Engineering Applications of Artificial Intelligence | 2015 | 11 Pages |
•Presenting a new intuitionistic fuzzy grey model for complex decision-making problems.•Introducing a new grey relational analysis to analyze the extent of connections.•Proposing a new intuitionistic fuzzy compromise ratio index to prioritize scenarios.•Developing a weighting method based on a generalized version of the intuitionistic fuzzy and entropy•Providing a real case study for the inspection planning problem.
Most of complex selection problems in real-life applications are considered under multiple conflicting attributes for manufacturing firms. The appropriate selection plays an important role in the firm׳s performance from the tactical and operational viewpoints. The classical methods for the selection problems in manufacturing firms are inadequate to deal with uncertainties, including insufficiency in information availability and the imprecise or vague nature in experts׳ judgments and preferences. To overcome these difficulties, this paper introduces a novel distance-based decision model for the multi-attributes analysis by considering the concepts of intuitionistic fuzzy sets (IFSs), grey relations and compromise ratio approaches. A weighting method for the attributes is first developed based on a generalized version of the entropy and IFSs along with experts׳ judgments. Then, a new grey relational analysis is introduced to analyze the extent of connections between two potential scenarios by an intuitionistic fuzzy distance measurement. Finally, a new intuitionistic fuzzy compromise ratio index to prioritize the scenarios is proposed by considering the weight of the strategy for the maximum group utility in intuitionistic fuzzy grey environment. The feasibility and practicability of the proposed distance-based decision model is illustrated in detail, and it is implemented in a real case study to the inspection planning for the oil pump housing from Renault automobile manufacturing.
