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Cluster approach integrating weighted geometric aggregation operator to appraise industrial robot: Knowledge based decision support system

Nitin Kumar Sahu (Department of Industrial and Production Engineering, Institute of Technology, Guru Ghasidas University, Bilaspur, India)
Atul Kumar Sahu (Department of Industrial and Production Engineering, Institute of Technology, Guru Ghasidas University, Bilaspur, India)
Anoop Kumar Sahu (Department of Mechanical Engineering, J.K. Institute of Engineering, Bilaspur, India)

Kybernetes

ISSN: 0368-492X

Article publication date: 15 January 2018

Issue publication date: 23 February 2018

Abstract

Purpose

Robot appraisement under various dimensions and directions is a crucial issue in real-time manufacturing scenario. Logistic robots are programable-independent movable devices capable of transporting stuffs in a logistic cycle. The purpose of this paper is to opt for the most economical robot under chains of criteria, which is always considered as a sizzling issue in an industrial domain.

Design/methodology/approach

The authors proposed a cluster approach, i.e. ratio analysis, reference point analysis and full mutification form, embedded type-2 fuzzy sets with weighted geometric aggregation operator (WGAO) to tackle the elected problem of industrial robot. The motive to use WGAO coupled with type-2 fuzzy sets is to effectively undertake the uncertainty associated with comprehensive information of professionals against defined dimensions. Furthermore, the cluster approach is used to carry out the comparative analysis for evaluating robust scores against candidate robot’s manufacturing firms, considering 59 crucial beneficial and non-beneficial dimensions. A case research study is carried out to demonstrate the validity of the proposed approach.

Findings

The most challenging task in real-time manufacturing scenario is robot selection for a particular industrial application. This problem has become more complex in recent years because of advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. In the past decade, robots have been selected in accordance with cost criteria excluding other beneficial criteria, which results in declined product quality, customer’s expectation, ill productivity, higher deliver time, etc. The proposed research incorporates the aforesaid issues and provides the various important attributes needed to be considered for the optimum evaluation and selection of industrial robots.

Research limitations/implications

The need for changes in the technological dimensions (speed, productivity, navigation, upgraded product demands, etc.) of robot was encountered as a hardship work for managers to take wise decision dealing with a wide range of availability of robot types and models with distinct features in the manufacturing firms. The presented work aids the managers in taking their decisions effectively while dealing with the aforesaid circumstances.

Originality/value

The proposed work suggests chains of dimensions (59 crucial beneficial and non-beneficial dimensions) that can be used by managers to measure the economic worth of robot to carry out logistic activities in updated manufacturing environment. The proposed work evolves as an effective cluster approach-embedded type-2 fuzzy sets with WGAO to assess manufacturing firms under availability of low information.

Keywords

Citation

Sahu, N.K., Sahu, A.K. and Sahu, A.K. (2018), "Cluster approach integrating weighted geometric aggregation operator to appraise industrial robot: Knowledge based decision support system", Kybernetes, Vol. 47 No. 3, pp. 487-524. https://doi.org/10.1108/K-11-2016-0332

Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited