Selection of industrial robot for manufacturing system applying MCDM techniques
An industrial robot is a mechanical device programmed for automated production tasks in industrial settings, representing flexible automation due to its reprogramming nature. The manufacturing sector has witnessed significant growth in the adoption of industrial robots and automation, driven by the pursuit of enhanced production efficiency, safety and quality assurance. Contrary to viewing robot automation as a potential threat, combining robots with human employees amplifies the benefits, aiding manufacturers in achieving production goals more efficiently. Industrial robots offer advantages such as high productivity, consistent speed, quality output, improved workplace safety and optimized floor space utilization. This article discusses the challenging task of selecting a suitable industrial robot for manufacturing organizations, given various alternatives. Robot manufacturers contribute leading features to their product development, making the choice of a versatile robot a complicated task. Decision-makers aim to select the most suitable industrial robot for societal problems by applying the strategy of minimum cost and maximum profit. Utilizing PROMETHEE-II and WASPAS methods in the context of a Fermatean picture fuzzy environment, is the most appropriate task for such complex problems. Initial fuzzy decision-making matrix is constructed, followed by its normalization. Pair-wise comparisons of each criterion is performed, leading to the preference index matrix. Positive and negative rankings are determined and the overall net flow is calculated. The result of the final ranking using Fermatean picture fuzzy PROMETHEE-II and WASPAS methods indicate that Cybotech Electric robot is the finest choice among the five alternatives.
Authors
- Kavyashree Basavaraju (ORCID: https://orcid.org/0000-0001-9517-0552)
- S. Shanthi
Institutions
- Annamalai University (IN)
Publication Details
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1109/amathe65477.2025.11080752
- Citations
- 1
- Primary Topic
- Advanced Manufacturing and Logistics Optimization
- Type
- article
- Field-Weighted Citation Impact
- 1.24