Intelligent Scheduling of a Pulsating Assembly Flow Shop Considering a Multifunctional Automated Guided Vehicle
The pulsating assembly line is widely used in modern manufacturing, particularly in high-precision industries such as aerospace, where it greatly enhances production efficiency. To achieve overall optimization, both product scheduling and Automated Guided Vehicle (AGV) scheduling must be simultaneou...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2593 |
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| author | Hailong Song Shengluo Yang Shuoxin Yin Junyi Wang Zhigang Xu |
| author_facet | Hailong Song Shengluo Yang Shuoxin Yin Junyi Wang Zhigang Xu |
| author_sort | Hailong Song |
| collection | DOAJ |
| description | The pulsating assembly line is widely used in modern manufacturing, particularly in high-precision industries such as aerospace, where it greatly enhances production efficiency. To achieve overall optimization, both product scheduling and Automated Guided Vehicle (AGV) scheduling must be simultaneously optimized. However, existing research predominantly focuses on product scheduling, with limited attention given to AGV scheduling. This paper proposes an optimized solution for the pulsating assembly line scheduling problem, incorporating multifunctional AGV scheduling. A mathematical model is developed and three AGV selection strategies and three AGV standby strategies are designed to optimize AGV scheduling and control. To improve scheduling efficiency, nine heuristic strategies are introduced, along with the Variable Neighborhood Descent (VND) algorithm as a metaheuristic method for product scheduling. The VND algorithm refines the solution through multiple neighborhood searches, enhancing both the precision and efficiency of product scheduling. Our experimental results demonstrate that the proposed strategies significantly improve the production efficiency of pulsating assembly workshops, reduce AGV scheduling costs, and optimize overall production workflows. This study offers novel methods for intelligent scheduling in pulsating assembly workshops, contributing to the advancement of manufacturing toward “multiple varieties, small batches, and customization”. |
| format | Article |
| id | doaj-art-2a61b6a4281242bebf1fa3e14da06408 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2a61b6a4281242bebf1fa3e14da064082025-08-20T02:05:08ZengMDPI AGApplied Sciences2076-34172025-02-01155259310.3390/app15052593Intelligent Scheduling of a Pulsating Assembly Flow Shop Considering a Multifunctional Automated Guided VehicleHailong Song0Shengluo Yang1Shuoxin Yin2Junyi Wang3Zhigang Xu4Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaThe pulsating assembly line is widely used in modern manufacturing, particularly in high-precision industries such as aerospace, where it greatly enhances production efficiency. To achieve overall optimization, both product scheduling and Automated Guided Vehicle (AGV) scheduling must be simultaneously optimized. However, existing research predominantly focuses on product scheduling, with limited attention given to AGV scheduling. This paper proposes an optimized solution for the pulsating assembly line scheduling problem, incorporating multifunctional AGV scheduling. A mathematical model is developed and three AGV selection strategies and three AGV standby strategies are designed to optimize AGV scheduling and control. To improve scheduling efficiency, nine heuristic strategies are introduced, along with the Variable Neighborhood Descent (VND) algorithm as a metaheuristic method for product scheduling. The VND algorithm refines the solution through multiple neighborhood searches, enhancing both the precision and efficiency of product scheduling. Our experimental results demonstrate that the proposed strategies significantly improve the production efficiency of pulsating assembly workshops, reduce AGV scheduling costs, and optimize overall production workflows. This study offers novel methods for intelligent scheduling in pulsating assembly workshops, contributing to the advancement of manufacturing toward “multiple varieties, small batches, and customization”.https://www.mdpi.com/2076-3417/15/5/2593schedulingAGVassembly workshoppulsating assembly linemodelling and simulationmetaheuristics |
| spellingShingle | Hailong Song Shengluo Yang Shuoxin Yin Junyi Wang Zhigang Xu Intelligent Scheduling of a Pulsating Assembly Flow Shop Considering a Multifunctional Automated Guided Vehicle Applied Sciences scheduling AGV assembly workshop pulsating assembly line modelling and simulation metaheuristics |
| title | Intelligent Scheduling of a Pulsating Assembly Flow Shop Considering a Multifunctional Automated Guided Vehicle |
| title_full | Intelligent Scheduling of a Pulsating Assembly Flow Shop Considering a Multifunctional Automated Guided Vehicle |
| title_fullStr | Intelligent Scheduling of a Pulsating Assembly Flow Shop Considering a Multifunctional Automated Guided Vehicle |
| title_full_unstemmed | Intelligent Scheduling of a Pulsating Assembly Flow Shop Considering a Multifunctional Automated Guided Vehicle |
| title_short | Intelligent Scheduling of a Pulsating Assembly Flow Shop Considering a Multifunctional Automated Guided Vehicle |
| title_sort | intelligent scheduling of a pulsating assembly flow shop considering a multifunctional automated guided vehicle |
| topic | scheduling AGV assembly workshop pulsating assembly line modelling and simulation metaheuristics |
| url | https://www.mdpi.com/2076-3417/15/5/2593 |
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