Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective Approach
In today’s global and volatile market, manufacturing enterprises are subjected to intense global competition, increasingly shortened product lifecycles and increased product customization and tailoring while being pressured to maintain a high degree of cost-efficiency. As a consequence, p...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9584876/ |
| _version_ | 1819005296465412096 |
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| author | Carlos Alberto Barrera Diaz Tehseen Aslam Amos H. C. Ng |
| author_facet | Carlos Alberto Barrera Diaz Tehseen Aslam Amos H. C. Ng |
| author_sort | Carlos Alberto Barrera Diaz |
| collection | DOAJ |
| description | In today’s global and volatile market, manufacturing enterprises are subjected to intense global competition, increasingly shortened product lifecycles and increased product customization and tailoring while being pressured to maintain a high degree of cost-efficiency. As a consequence, production organizations are required to introduce more new product models and variants into existing production setups, leading to more frequent ramp-up and ramp-down scenarios when transitioning from an outgoing product to a new one. In order to cope with such as challenge, the setup of the production systems needs to shift towards reconfigurable manufacturing systems (RMS), making production capable of changing its function and capacity according to the product and customer demand. Consequently, this study presents a simulation-based multi-objective optimization approach for system re-configuration of multi-part flow lines subjected to scalable capacities, which addresses the assignment of the tasks to workstations and buffer allocation for simultaneously maximizing throughput and minimizing total buffer capacity to cope with fluctuating production volumes. To this extent, the results from the study demonstrate the benefits that decision-makers could gain, particularly when they face trade-off decisions inherent in today’s manufacturing industry by adopting a Simulation-Based Multi-Objective Optimization (SMO) approach. |
| first_indexed | 2024-12-20T23:50:32Z |
| format | Article |
| id | doaj.art-4add997f14084e8aa8eb24f87bf518ef |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T23:50:32Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4add997f14084e8aa8eb24f87bf518ef2022-12-21T19:22:50ZengIEEEIEEE Access2169-35362021-01-01914419514421010.1109/ACCESS.2021.31222399584876Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective ApproachCarlos Alberto Barrera Diaz0https://orcid.org/0000-0003-3541-9330Tehseen Aslam1Amos H. C. Ng2Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, Skövde, SwedenDivision of Intelligent Production Systems, School of Engineering Science, University of Skövde, Skövde, SwedenDivision of Intelligent Production Systems, School of Engineering Science, University of Skövde, Skövde, SwedenIn today’s global and volatile market, manufacturing enterprises are subjected to intense global competition, increasingly shortened product lifecycles and increased product customization and tailoring while being pressured to maintain a high degree of cost-efficiency. As a consequence, production organizations are required to introduce more new product models and variants into existing production setups, leading to more frequent ramp-up and ramp-down scenarios when transitioning from an outgoing product to a new one. In order to cope with such as challenge, the setup of the production systems needs to shift towards reconfigurable manufacturing systems (RMS), making production capable of changing its function and capacity according to the product and customer demand. Consequently, this study presents a simulation-based multi-objective optimization approach for system re-configuration of multi-part flow lines subjected to scalable capacities, which addresses the assignment of the tasks to workstations and buffer allocation for simultaneously maximizing throughput and minimizing total buffer capacity to cope with fluctuating production volumes. To this extent, the results from the study demonstrate the benefits that decision-makers could gain, particularly when they face trade-off decisions inherent in today’s manufacturing industry by adopting a Simulation-Based Multi-Objective Optimization (SMO) approach.https://ieeexplore.ieee.org/document/9584876/Multi-objective optimizationreconfigurable manufacturing systemssimulation-based optimizationgenetic algorithm |
| spellingShingle | Carlos Alberto Barrera Diaz Tehseen Aslam Amos H. C. Ng Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective Approach IEEE Access Multi-objective optimization reconfigurable manufacturing systems simulation-based optimization genetic algorithm |
| title | Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective Approach |
| title_full | Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective Approach |
| title_fullStr | Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective Approach |
| title_full_unstemmed | Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective Approach |
| title_short | Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective Approach |
| title_sort | optimizing reconfigurable manufacturing systems for fluctuating production volumes a simulation based multi objective approach |
| topic | Multi-objective optimization reconfigurable manufacturing systems simulation-based optimization genetic algorithm |
| url | https://ieeexplore.ieee.org/document/9584876/ |
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