Sustainability Appraisement of Industrial Robots by GRA for Real Automation Environment

Sustainability Appraisement of Industrial Robots by GRA for Real Automation Environment

Atul Kumar Sahu (Department of Mechanical Engineering, National Institute of Technology, Raipur, India), Harendra Kumar Narang (Department of Mechanical Engineering, National Institute of Technology, Raipur, India), Mridul Singh Rajput (Department of Mechanical Engineering, National Institute of Technology, Raipur, India) and Nitin Kumar Sahu (Department of Industrial and Production Engineering, Guru Ghasidas Vishwavidyalaya, Bilaspur, India)
DOI: 10.4018/IJSESD.2019070104

Abstract

A knowledge-based tool for executing the managerial decision-making process is presented in this work. The work evaluated the significant robot i.e. industrial machines for sustainably handling the real time manufacturing environment. The presented tool integrates the grey sets theory with grey relational approach (GRA) to support the decision-making process for opting most significant industrial robot. The performance mapping of industrial robots by GRA under grey set theory is presented for defining a sustainable real automation environment. The work offers the essence of both grey set theory and grey relational approach under a sole ring. The work implicates grey sets theory to capture the uncertainties associated with the evaluation of robot measures and implicated GRA to recognize the most valuable robot alternative. The proposed tool is developed by categorizing the list of qualitative and quantitative characteristics; which links the robot evaluation properties. The work attempts to draft a knowledgeable tool for effectively executing manufacturing activities by the robots.
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1. Introduction

In recent years; production managers and engineers have stresses on finding fruitful and less expensive ways for producing products and they believed to adapt robots for executing their jobs (Bard, 1986). Organizations are facing increasing labor costs and shortage of workers and thus investing in robotics (Qureshi and Syed, 2014). Robots have improved the physical work environment in numerous tasks such as spray-painting, grinding, materials handling, spot-welding and so on (Mårtensson, 1987). Availability of different ranges and types of industrial robots has provided an absolute collection of industrial robots to be used for locating and effectively executing flexible manufacturing systems (Vladimir, 1987). Nowadays; selection of an industrial robot for business applications is the challenging task due to amplification in functional characteristics and implication of numerous advance features onto the robots by the manufacturers (Khouja and Booth, 1995; Kumar and Garg, 2010; Goh et al., 1996).

The present work offered a group of performance measures needed for their favorable selection. In today's scenario; firm’s managers require supporting decision-making tools and platform to make crucial decisions in technological areas. The robot selection is one of the technological problem, which requires sophisticated tools and techniques to undertake market competitiveness and to plan future firms growth (Koulouriotis and Ketipi, 2011; Rao et al., 2011). The industrial robot acts like a “job-killer”, primarily when displaced workers are not redeployed within the enterprise (Ebel, 1987). Selecting an optimal robot amongst alternatives can confers competitive advantages and can efficiently encounter the customer's diverse demands by producing precise products to these demands. Sahu et al. (2017c) applied TOPSIS technique based on fuzzy theory to benchmark advanced manufacturing machines for manufacturing operations. Additionally; Sahu et al. (2018c) developed an approach based on weighted geometric aggregation operator to appraise the significant application of industrial robots. Parameshwaran et al. (2015) illustrated an integrated approach by utilizing Fuzzy Delphi Method to select the list of significant objective and subjective criterions based on the decision maker's perception. Tansel et al. (2013) developed two phase ROBSEL (robot selection decision support system) to facilitate decision makers in executing decision for robot selection and assessment. Chatterjee et al. (2010) utilized VIsekriterijumsko KOmpromisno Rangiranje (VIKOR) and ELimination and Et Choice Translating REality (ELECTRE) method in robot selection arena. The disclosed that their applied methods are potentially sound and can be used under the linguistic information.

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