An interval 2-tuple linguistic MCDM method for robot evaluation and selection
| Autor: | Jing Wu, Ren Minglun, Hu-Chen Liu, Qing-Lian Lin |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
business.industry
Strategy and Management TOPSIS Interval (mathematics) Management Science and Operations Research Machine learning computer.software_genre Multiple-criteria decision analysis Industrial and Manufacturing Engineering Linguistics Term (time) Complete information Robot Artificial intelligence Tuple business computer Selection (genetic algorithm) Mathematics |
| Zdroj: | International Journal of Production Research. 52:2867-2880 |
| ISSN: | 1366-588X 0020-7543 |
| DOI: | 10.1080/00207543.2013.854939 |
| Popis: | Nowadays selection of an optimal robot has become a challenging task for manufacturers with the increment of production demands and availability of more different robot models. Robot selection for a particular industrial application can be viewed as a complicated multi-criteria decision-making problem which requires consideration of a number of alternative robots and conflicting subjective and objective criteria. Furthermore, decision-makers tend to use multigranularity linguistic term sets to express their assessments on the subjective criteria, and there usually exists uncertain and incomplete assessment information. In this paper, an interval 2-tuple linguistic TOPSIS (ITL-TOPSIS) method is proposed to handle the robot selection problem under uncertain and incomplete information environment. This method considers both subjective judgements and objective information in real-life applications, and models the uncertainty and diversity of decision-makers’ assessments using interval 2-tuple linguistic varia... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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