Enhancing Mass Customization Manufacturing: Multiobjective Metaheuristic Algorithms for flow shop Production in Smart Industry
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909284157095936 |
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| author | Rossit, Diego Rossit, Daniel Nesmachnow, Sergio |
| author_facet | Rossit, Diego Rossit, Daniel Nesmachnow, Sergio |
| contents | The current landscape of massive production industries is undergoing significant transformations driven by emerging customer trends and new smart manufacturing technologies. One such change is the imperative to implement mass customization, wherein products are tailored to individual customer specifications while still ensuring cost efficiency through large-scale production processes. These shifts can profoundly impact various facets of the industry. This study focuses on the necessary adaptations in shop-floor production planning. Specifically, it proposes the use of efficient evolutionary algorithms to tackle the flowshop with missing operations, considering different optimization objectives: makespan, weighted total tardiness, and total completion time. An extensive computational experimentation is conducted across a range of realistic instances, encompassing varying numbers of jobs, operations, and probabilities of missing operations. The findings demonstrate the competitiveness of the proposed approach and enable the identification of the most suitable evolutionary algorithms for addressing this problem. Additionally, the impact of the probability of missing operations on optimization objectives is discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15802 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Mass Customization Manufacturing: Multiobjective Metaheuristic Algorithms for flow shop Production in Smart Industry Rossit, Diego Rossit, Daniel Nesmachnow, Sergio Neural and Evolutionary Computing The current landscape of massive production industries is undergoing significant transformations driven by emerging customer trends and new smart manufacturing technologies. One such change is the imperative to implement mass customization, wherein products are tailored to individual customer specifications while still ensuring cost efficiency through large-scale production processes. These shifts can profoundly impact various facets of the industry. This study focuses on the necessary adaptations in shop-floor production planning. Specifically, it proposes the use of efficient evolutionary algorithms to tackle the flowshop with missing operations, considering different optimization objectives: makespan, weighted total tardiness, and total completion time. An extensive computational experimentation is conducted across a range of realistic instances, encompassing varying numbers of jobs, operations, and probabilities of missing operations. The findings demonstrate the competitiveness of the proposed approach and enable the identification of the most suitable evolutionary algorithms for addressing this problem. Additionally, the impact of the probability of missing operations on optimization objectives is discussed. |
| title | Enhancing Mass Customization Manufacturing: Multiobjective Metaheuristic Algorithms for flow shop Production in Smart Industry |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2407.15802 |