For different applications, there are different robots having capabilities and specifications accordingly. For a particular application and industrial requirement, proper and suitable selection of robot is a difficult task. Numerous robot selection methods are available. Considering the research works on industrial robot selection, group best-worst method is employed in this paper for the proper selection of robots. Weighing the decision makers by considering their past experience is an important factor considered for expert and reliable selection of robot. Objective weights to describe the importance of the attributes along with the decision maker subjective preferences to describe the weights of the attribute are considered. Two problems are discussed for a detailed description and results are compared with the well-known group analytical hierarchy process method. The results show that due to lower minimum violation and lower total deviation, the proposed method performs better. Keywords Group best-worst method (G-BWM) Á Group analytic hierarchy process (G-AHP) Á Multiple attribute group decision making Á Subjective weights Á Objective weights Á integrated weights Communicated by V. Loia.
Best-worst method (BWM) is extended to uncertain situations, hesitant fuzzy best-worst method (HFBWM) is proposed by using hesitant fuzzy multiplicative preference relation for multiple-criteria group decision-making problems. The reference comparison of the best criterion and the worst criterion are described by the linguistic terms, which are expressed in hesitant fuzzy elements, of the decision makers. Weights of criteria are calculated by using score function. Using the concept of BWM, nonlinearly constrained optimization problems are formed to obtain hesitant fuzzy weights (HFWs) of different criteria and alternatives. To check the reliability of the HFBWM, consistency ratio is proposed. The advantage and suitability of the proposed HFBWM are determined by three case studies. The results indicate that the HFBWM, due to higher comparison consistency as compared to BWM, obtain plausible preference ranking for alternatives. K E Y W O R D S consistency ratio, hesitant fuzzy best-worst method, hesitant fuzzy reference comparison (HFRC), hesitant fuzzy sets (HFS), multiple-criteria decision-making
Industrial robots have different capabilities and specifications according to the required applications. It is becoming difficult to select a suitable robot for specific applications and requirements due to the availability of several types with different specifications of robots in the market. Best-worst method is a useful, highly consistent and reliable method to derive weights of criteria and it is worthy to integrate it with the evaluation based on distance from average solution (EDAS) method that is more applicable and needs fewer number of calculations as compared to other methods. An example is presented to show the validity and usability of the proposed methodology. Comparison of ranking results matches with the well-known distance-based approach, technique for order preference by similarity to ideal solution and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods showing the robustness of the best-worst EDAS hybrid method. Sensitivity analysis performed using eighty to one ratio shows that the proposed hybrid MCDM methodology is more stable and reliable.
The successful application of fuzzy control depends to a large extent on the parameters of some subjective decisions, such as fuzzy membership function (MF). Fuzzy logic controller (FLC) implementing augmented output MFs as compare to input MFs is presented to improve the accuracy, robustness, and performance of the system. The best possible combination of input and output MFs is introduced to distribute the uniform input MFs and augmented output MFs in the treatise. The simulation of the 2-Inputs 1-Output Fuzzy Control System is performed in many nonlinear processes. Then, the experimental outcomes of the uniformly and augmented distributed output MFs are compared under similar circumstances. The experimental outcomes are in a virtuous covenant with the simulation outcomes. The experimental outcomes show that the root mean square error (RMSE) is reduced around 75.3% and bringing down the relative error to the acceptable range (≤±10%). The control accuracy is improved and the robustness is boosted by reducing the RMSE through the FLC with augmented-distributed output MFs. Moreover, the cost and energy efficiency in any fuzzy system will be improved by implementing the augmented-distributed output MFs using the best possible combination of input and output MFs. INDEX TERMS Fuzzy control system, membership functions, relative error, root mean square error.
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