An interval 2-tuple linguistic MCDM method for robot evaluation and selection

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 variables. An example is cited for demonstrating the feasibility and practicability of the proposed method, and results show that the ITL-TOPSIS is an effective decision-making tool for robot evaluation and selection with uncertain and incomplete information.