Stochastic robot failure management in an assembly line under industry 4.0 environment
Robot failures at stations pose a major challenge to the smooth functioning of fully automated assembly lines in an industry 4.0 environment. A probable solution to this problem is a redundant configuration wherein downstream stations automatically take over upstream operations in the event of a fai...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Production and Manufacturing Research: An Open Access Journal |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21693277.2024.2439275 |
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| author | Kuldip Singh Sangwan Anirudh Tusnial Suveg V Iyer |
| author_facet | Kuldip Singh Sangwan Anirudh Tusnial Suveg V Iyer |
| author_sort | Kuldip Singh Sangwan |
| collection | DOAJ |
| description | Robot failures at stations pose a major challenge to the smooth functioning of fully automated assembly lines in an industry 4.0 environment. A probable solution to this problem is a redundant configuration wherein downstream stations automatically take over upstream operations in the event of a failure. This paper proposes an improved integrated model of operation reallocation and robot allocation for stochastic failures of a robotic assembly line. A particle swarm optimization (PSO) algorithm is developed to solve the proposed integrated model. The novelty of the proposed algorithm is that it optimizes the production rate and power consumption simultaneously at the targeted production rate. The paper demonstrates the superiority of the proposed model over the genetic algorithm and differential evolution models. The robustness of the proposed model is evaluated at different production rates. The proposed model is capable of fulfilling organizational needs of production rate at the minimum energy consumption. |
| format | Article |
| id | doaj-art-2b3a2a32ad234b25b5f29e906305b298 |
| institution | Kabale University |
| issn | 2169-3277 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Production and Manufacturing Research: An Open Access Journal |
| spelling | doaj-art-2b3a2a32ad234b25b5f29e906305b2982025-01-17T08:35:22ZengTaylor & Francis GroupProduction and Manufacturing Research: An Open Access Journal2169-32772025-12-0113110.1080/21693277.2024.2439275Stochastic robot failure management in an assembly line under industry 4.0 environmentKuldip Singh Sangwan0Anirudh Tusnial1Suveg V Iyer2Department of Mechanical Engineering, Birla Institute of Technology and Science Pilani, Pilani, Rajasthan, IndiaDepartment of Mechanical Engineering, Birla Institute of Technology and Science Pilani, Pilani, Rajasthan, IndiaDepartment of Mechanical Engineering, Birla Institute of Technology and Science Pilani, Pilani, Rajasthan, IndiaRobot failures at stations pose a major challenge to the smooth functioning of fully automated assembly lines in an industry 4.0 environment. A probable solution to this problem is a redundant configuration wherein downstream stations automatically take over upstream operations in the event of a failure. This paper proposes an improved integrated model of operation reallocation and robot allocation for stochastic failures of a robotic assembly line. A particle swarm optimization (PSO) algorithm is developed to solve the proposed integrated model. The novelty of the proposed algorithm is that it optimizes the production rate and power consumption simultaneously at the targeted production rate. The paper demonstrates the superiority of the proposed model over the genetic algorithm and differential evolution models. The robustness of the proposed model is evaluated at different production rates. The proposed model is capable of fulfilling organizational needs of production rate at the minimum energy consumption.https://www.tandfonline.com/doi/10.1080/21693277.2024.2439275Energy efficient assembly lineparticle swarm optimizationredundant configurationstochastic failureindustry 4.0 |
| spellingShingle | Kuldip Singh Sangwan Anirudh Tusnial Suveg V Iyer Stochastic robot failure management in an assembly line under industry 4.0 environment Production and Manufacturing Research: An Open Access Journal Energy efficient assembly line particle swarm optimization redundant configuration stochastic failure industry 4.0 |
| title | Stochastic robot failure management in an assembly line under industry 4.0 environment |
| title_full | Stochastic robot failure management in an assembly line under industry 4.0 environment |
| title_fullStr | Stochastic robot failure management in an assembly line under industry 4.0 environment |
| title_full_unstemmed | Stochastic robot failure management in an assembly line under industry 4.0 environment |
| title_short | Stochastic robot failure management in an assembly line under industry 4.0 environment |
| title_sort | stochastic robot failure management in an assembly line under industry 4 0 environment |
| topic | Energy efficient assembly line particle swarm optimization redundant configuration stochastic failure industry 4.0 |
| url | https://www.tandfonline.com/doi/10.1080/21693277.2024.2439275 |
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