A Mixed-Integer Linear Programming Model for Addressing Efficient Flexible Flow Shop Scheduling Problem with Automatic Guided Vehicles Consideration
With the development of Industry 4.0, discrete manufacturing systems are accelerating their transformation toward flexibility and intelligence to meet the market demand for various products and small-batch production. The flexible flow shop (FFS) paradigm enhances production flexibility, but existin...
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2025-03-01
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| author | Dekun Wang Hongxu Wu Wengang Zheng Yuhao Zhao Guangdong Tian Wenjie Wang Dong Chen |
| author_facet | Dekun Wang Hongxu Wu Wengang Zheng Yuhao Zhao Guangdong Tian Wenjie Wang Dong Chen |
| author_sort | Dekun Wang |
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| description | With the development of Industry 4.0, discrete manufacturing systems are accelerating their transformation toward flexibility and intelligence to meet the market demand for various products and small-batch production. The flexible flow shop (FFS) paradigm enhances production flexibility, but existing studies often address FFS scheduling and automated guided vehicle (AGV) path planning separately, resulting in resource competition conflicts, such as equipment idle time and AGV congestion, which prolong the manufacturing cycle time and reduce system energy efficiency. To solve this problem, this study proposes an integrated production–transportation scheduling framework (FFSP-AGV). By using the adjacent sequence modeling idea, a mixed-integer linear programming (MILP) model is established, which takes into account the constraints of the production process and AGV transportation task conflicts with the aim of minimizing the makespan and improving overall operational efficiency. Systematic evaluations are carried out on multiple test instances of different scales using the CPLEX solver. The results show that, for small-scale instances (job count ≤10), the MILP model can generate optimal scheduling solutions within a practical computation time (several minutes). Moreover, it is found that there is a significant marginal diminishing effect between AGV quantity and makespan reduction. Once the number of AGVs exceeds 60% of the parallel equipment capacity, their incremental contribution to cycle time reduction becomes much smaller. However, the computational complexity of the model increases exponentially with the number of jobs, making it slightly impractical for large-scale problems (job count > 20). This research highlights the importance of integrated production–transportation scheduling for reducing manufacturing cycle time and reveals a threshold effect in AGV resource allocation, providing a theoretical basis for collaborative optimization in smart factories. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-03-01 |
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| spelling | doaj-art-e655657ee3a247beb35af5717c0765c32025-08-20T03:43:30ZengMDPI AGApplied Sciences2076-34172025-03-01156313310.3390/app15063133A Mixed-Integer Linear Programming Model for Addressing Efficient Flexible Flow Shop Scheduling Problem with Automatic Guided Vehicles ConsiderationDekun Wang0Hongxu Wu1Wengang Zheng2Yuhao Zhao3Guangdong Tian4Wenjie Wang5Dong Chen6School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Machinery and Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, ChinaWith the development of Industry 4.0, discrete manufacturing systems are accelerating their transformation toward flexibility and intelligence to meet the market demand for various products and small-batch production. The flexible flow shop (FFS) paradigm enhances production flexibility, but existing studies often address FFS scheduling and automated guided vehicle (AGV) path planning separately, resulting in resource competition conflicts, such as equipment idle time and AGV congestion, which prolong the manufacturing cycle time and reduce system energy efficiency. To solve this problem, this study proposes an integrated production–transportation scheduling framework (FFSP-AGV). By using the adjacent sequence modeling idea, a mixed-integer linear programming (MILP) model is established, which takes into account the constraints of the production process and AGV transportation task conflicts with the aim of minimizing the makespan and improving overall operational efficiency. Systematic evaluations are carried out on multiple test instances of different scales using the CPLEX solver. The results show that, for small-scale instances (job count ≤10), the MILP model can generate optimal scheduling solutions within a practical computation time (several minutes). Moreover, it is found that there is a significant marginal diminishing effect between AGV quantity and makespan reduction. Once the number of AGVs exceeds 60% of the parallel equipment capacity, their incremental contribution to cycle time reduction becomes much smaller. However, the computational complexity of the model increases exponentially with the number of jobs, making it slightly impractical for large-scale problems (job count > 20). This research highlights the importance of integrated production–transportation scheduling for reducing manufacturing cycle time and reveals a threshold effect in AGV resource allocation, providing a theoretical basis for collaborative optimization in smart factories.https://www.mdpi.com/2076-3417/15/6/3133integrated schedulingflexible flow shop schedulingmakespanAGV schedulingmixed-integer linear programmingexact algorithm |
| spellingShingle | Dekun Wang Hongxu Wu Wengang Zheng Yuhao Zhao Guangdong Tian Wenjie Wang Dong Chen A Mixed-Integer Linear Programming Model for Addressing Efficient Flexible Flow Shop Scheduling Problem with Automatic Guided Vehicles Consideration Applied Sciences integrated scheduling flexible flow shop scheduling makespan AGV scheduling mixed-integer linear programming exact algorithm |
| title | A Mixed-Integer Linear Programming Model for Addressing Efficient Flexible Flow Shop Scheduling Problem with Automatic Guided Vehicles Consideration |
| title_full | A Mixed-Integer Linear Programming Model for Addressing Efficient Flexible Flow Shop Scheduling Problem with Automatic Guided Vehicles Consideration |
| title_fullStr | A Mixed-Integer Linear Programming Model for Addressing Efficient Flexible Flow Shop Scheduling Problem with Automatic Guided Vehicles Consideration |
| title_full_unstemmed | A Mixed-Integer Linear Programming Model for Addressing Efficient Flexible Flow Shop Scheduling Problem with Automatic Guided Vehicles Consideration |
| title_short | A Mixed-Integer Linear Programming Model for Addressing Efficient Flexible Flow Shop Scheduling Problem with Automatic Guided Vehicles Consideration |
| title_sort | mixed integer linear programming model for addressing efficient flexible flow shop scheduling problem with automatic guided vehicles consideration |
| topic | integrated scheduling flexible flow shop scheduling makespan AGV scheduling mixed-integer linear programming exact algorithm |
| url | https://www.mdpi.com/2076-3417/15/6/3133 |
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