The m-polar fuzzy ELECTRE-I integrated AHP approach for selection of non-traditional machining processes

Abstract

In the literature, there are a number of decision-making strategies that can be used to choose the best NTM processes. Chen (2014) introduced a novel method to handle fuzzy data that includes multipolar uncertainty, referred to as the m-polar fuzzy set (mFS) approach. The mFS method, along with other multi-criteria decision-making (MCDM) techniques, is a good way to choose between options. An illustration of such a combination is the mFS elimination and choice translating reality-I (ELECTRE-I) . A criteria weight approach is also needed to increase the accuracy of the mFS ELECTRE-I method. The mFS ELECTRE-I method and the analytical hierarchy process (AHP) criteria weight calculation method are combined in the current work. The unique thing about this method is that it can be used to solve both MCDM and MCGDM problems by combining the mFS ELECTRE-I with the AHP criteria weight method. A single-dimensional weight sensitivity analysis is performed to confirm the technique’s stability for different criterion weights for the AHP method on alternative rank performance. The results of the NTM process selection are validated by previous research findings. EDM turned out to be the best way to create machine precise holes on duralumin alloy, and ECM turned out to be the best alternative to generate the surface of revolution in stainless steel. A C++-based soft solution that uses the mFS ELECTRE-I technique to analyze various MCDM and MCGDM problems has been developed. With the soft solution, you can fix problems with selecting the FMS, the robot, and so on.

Keywords:

• m-polar
• ELECTRE-I
• AHP
• TOPSIS
• PROMEETHEE

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Madan Jagtap

Madan Jagtap is an Assistant Professor in Mechanical Engineering Department in Saraswati College of Engineering, and PhD Research Scholar in Mechanical Engineering Department in the Veermata Jijabai Technological Institute, Matunga, Affiliated to the Mumbai University, Mumbai, India. He earned B.E. in Aeronautical Engineering from Aeronautical Society of India, New Delhi, India. Masters in Manufacturing Systems Engineering from Saraswati College of Engineering Affiliated to Mumbai University, India. He has published journal and conference papers. His research interests include manufacturing, simulation, optimization, reliability and Uncertainty.

Prasad Karande

Prasad Karande is an Associate Professor in Department of Mechanical Engineering at the Veermata Jijabai Technological Institute, Mumbai, India. He earned B.E. in Mechanical Engineering from Shivaji University, India, Masters in Production Engineering with specialization in Manufacturing from Mumbai University, India and PhD in Engineering from Jadavpur University, Kolkata, India. He has published many research papers in leading journal and conferences. Dr. Karande has taught under graduate and post-graduate courses in industrial engineering, operations research, supply chain management etc. His research interests include multi-criteria decision-making, supplier selection, optimization and manufacturing.