Optimizing Cellular Manufacturing Systems Through Multi-Objective Cobot Coordination and Tool Allocation

S M Saleemuddin; Sanjeev Reddy K Hudgikar

doi:10.17485/IJST/v17i14.72

Article

Optimizing Cellular Manufacturing Systems Through Multi-Objective Cobot Coordination and Tool Allocation

VIEWS 99
PDF 34

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i14.72

Year: 2024, Volume: 17, Issue: 14, Pages: 1430-1438

Original Article

Optimizing Cellular Manufacturing Systems Through Multi-Objective Cobot Coordination and Tool Allocation

S M Saleemuddin^1,2*, Sanjeev Reddy K Hudgikar³

¹Research Scholar, VTU, Belagavi, Karnataka, India
²Assistant Professor, AITS, Rajampet, Andhra Pradesh, India
³Professor, Sharnbasva University, Kalaburgi, Karnataka, India

*Corresponding Author
Email: [email protected]

Received Date:09 January 2024, Accepted Date:06 March 2024, Published Date:02 April 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: This study aims to enhance cellular manufacturing systems by optimizing cobot and tool assignments, maximizing flexibility, and minimizing production time, workload imbalances, energy consumption, error rates and rework. Methods: This study employs a sophisticated multi-objective optimization approach, integrating constraints into the cellular manufacturing system using advanced linear or integer programming techniques. The model is designed to dynamically adapt in real-time, allowing for flexibility in response to evolving production needs. We systematically evaluate cobot and tool assignments, balancing conflicting objectives within a comprehensive mathematical framework. The optimization process is fine-tuned to consider machine capacities, part type assignments, and tool compatibility, ensuring the practicality and realism of the proposed solutions. The overarching goal is to identify optimal configurations that minimize production time, workload imbalances, energy consumption, error rates and rework while maximizing system adaptability. Findings: The optimal cobot and tool assignments, determined through the multi-objective optimization model, yielded substantial improvements across critical metrics compared to a scenario without cobots. This data showcases a 26% reduction in production time, a 20% decrease in workload imbalance, a 20% improvement in flexibility, a 28% reduction in energy consumption, and a 26% decrease in error rates and rework when utilizing the proposed multi-objective optimization approach. These tangible improvements underscore the practical benefits of integrating cobots in cellular manufacturing systems.Top of Form Novelty: This study introduces a novel multi-objective optimization approach for cellular manufacturing, enhancing adaptability and efficiency through strategic cobot and tool assignments.

Keywords: Cellular Manufacturing Systems, Cobots, Tool Assignment, MultiObjective Optimization, Production Time, Workload Balancing, Energy Consumption, Error Rates, Flexibility

References

Akella P, Peshkin M, Colgate ED, Wannasuphoprasit W, Nagesh N, Wells J, et al. Cobots for the automobile assembly line. In: Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C). (Vol. 1, pp. 728-733) IEEE. 2002.
Antonelli D, Aliev K. Robust assembly task assignment in Human Robot Collaboration as a Markov Decision Process problem. Procedia CIRP. 2022;112:174–179. Available from: https://doi.org/10.1016/j.procir.2022.09.068
Baltrusch SJ, Krause F, Vries AWD, Dijk WV, Looze MPD. What about the human in human robot collaboration? A literature review on HRC’s effects on aspects of job quality. Ergonomics. 2022;65(5):719–740. Available from: https://doi.org/10.1080/00140139.2021.1984585
Bisen AS, Payal H. Collaborative robots for industrial tasks: A review. Materials Today: Proceedings. 2022;52(Part 3):500–504. Available from: https://doi.org/10.1016/j.matpr.2021.09.263
Boschetti G, Bottin M, Faccio M, Minto R. Multi-robot multi-operator collaborative assembly systems: a performance evaluation model. Journal of Intelligent Manufacturing. 2021;32(5):1455–1470. Available from: https://doi.org/10.1007/s10845-020-01714-7
Bruno G, Antonelli D. Dynamic task classification and assignment for the management of human-robot collaborative teams in workcells. The International Journal of Advanced Manufacturing Technology. 2018;98(9-12):2415–2427. Available from: https://doi.org/10.1007/s00170-018-2400-4
Casalino A, Zanchettin AM, Piroddi L, Rocco P. Optimal Scheduling of Human–Robot Collaborative Assembly Operations With Time Petri Nets. IEEE Transactions on Automation Science and Engineering. 2021;18(1):70–84. Available from: https://doi.org/10.1109/TASE.2019.2932150
Chen FF, Adam EE. The impact of flexible manufacturing systems on productivity and quality. IEEE Transactions on Engineering Management. 1991;38(1):33–45. Available from: https://doi.org/10.1109/17.65758
Mura MD, Dini G. Designing assembly lines with humans and collaborative robots: A genetic approach. CIRP Annals. 2019;68(1):1–4. Available from: https://doi.org/10.1016/j.cirp.2019.04.006
Yılmaz E. A mathematical programming model with present value method for optimum design of flexible cellular manufacturing systems. Journal of Engineering Research. 2023;11(3):100–111. Available from: https://doi.org/10.36909/jer.17241
Gjeldum N, Aljinovic A, Zizic MC, Mladineo M. Collaborative robot task allocation on an assembly line using the decision support system. International Journal of Computer Integrated Manufacturing. 2022;35(4-5):510–526. Available from: https://doi.org/10.1080/0951192X.2021.1946856
Katiraee N, Calzavara M, Finco S, Battini D, Battaïa O. Consideration of workers’ differences in production systems modelling and design: State of the art and directions for future research. International Journal of Production Research. 2021;59(11):3237–3268. Available from: https://doi.org/10.1080/00207543.2021.1884766
Keshvarparast A, Katiraee N, Finco S, Battini D. Cobots implementation in manufacturing systems: literature review and open questions. Proceedings of the Summer School Francesco Turco. 2021. Available from: https://www.research.unipd.it/handle/11577/3440137#
Maderna R, Pozzi M, Zanchettin AM, Rocco P, Prattichizzo D. Flexible scheduling and tactile communication for human–robot collaboration. Robotics and Computer-Integrated Manufacturing. 2022;73:102233. Available from: https://doi.org/10.1016/j.rcim.2021.102233
Saleemuddin SM, Hudgikar SRK. Optimization Of Cobots And Operators Assignment In Cellular Manufacturing Systems: A Mathematical Model And Heuristic Approach. U.P.B. Scientific Bulletin, Series D. 2023;85(4):165–178. Available from: https://www.scientificbulletin.upb.ro/rev_docs_arhiva/fullf7f_261860.pdf
Reddy NS, Lalitha MP, Pandey SP, Venkatesh GS. Simultaneous scheduling of machines and tools in a multi-machine FMS with alternative routing using symbiotic organisms search algorithm. Journal of Engineering Research. 2022;10(3A):274–297. Available from: https://doi.org/10.36909/jer.10653

Copyright

2024 Saleemuddin & Hudgikar. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)