Solving robot selection problem by a new interval-valued hesitant fuzzy multi-attributes group decision method
محورهای موضوعی : مجله بین المللی ریاضیات صنعتی
S. M. ‎Mousavi‎
1
(Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.)
B. Vahdani
2
(Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.)
H. Gitinavard‎
3
(Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran.)
H. Hashemi‎
4
(Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran)
کلید واژه: Robot selection problem, Group decision making analysis, Interval-valued hesitant fuzzy,
چکیده مقاله :
Selecting the most suitable robot among their wide range of specifications and capabilities is an important issue to perform the hazardous and repetitive jobs. Companies should take into consideration powerful group decision-making (GDM) methods to evaluate the candidates or potential robots versus the selected attributes (criteria). In this study, a new GDM method is proposed by utilizing the complex proportional assessment method under interval-valued hesitant fuzzy (IVHF)-environment. In the proposed method, a group of experts is established to evaluate the candidates or alternatives among the conflicted attributes. In addition, experts assign their preferences and judgments about the rating of alternatives and the relative importance of each attribute by linguistic terms which are converted to interval-valued hesitant fuzzy elements (IVHFEs). Also, the attributes’ weights and experts’ weights are applied in procedure of the proposed interval-valued hesitant fuzzy group decision-making (IVHF-GDM) method. Hence, the experts’ opinions about the relative importance of each attribute are considered in determination of attributes’ weights. Thus, we propose a hybrid maximizing deviation method under uncertainty. Finally, an illustrative example is presented to show the feasibility of the proposed IVHF-GDM method and also the obtained ranking results are compared with a recent method from the literature.
