An intelligent automated guided vehicle scheduling framework for manufacturing: Balancing energy, efficiency, and task completion
•Developed MO-MIP model to optimize AGV scheduling in manufacturing systems•Applied NSGA-II and NSGA-III for efficient multi-objective optimization•Tested on three distinct manufacturing workshop scenarios•Outperformed traditional algorithms across three industrial simulations In recent years, the w...
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| Vydáno v: | Swarm and evolutionary computation Ročník 98; s. 102127 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.10.2025
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| ISSN: | 2210-6502 |
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| Abstract | •Developed MO-MIP model to optimize AGV scheduling in manufacturing systems•Applied NSGA-II and NSGA-III for efficient multi-objective optimization•Tested on three distinct manufacturing workshop scenarios•Outperformed traditional algorithms across three industrial simulations
In recent years, the widespread usage of Automated Guided Vehicles (AGVs) has become prevalent in material transportation systems of industries. The AGVs are known for their operational flexibility and efficiency, but efficient scheduling remains a crucial issue due to the conflicting factors, including deviation penalties for task execution times, power consumption, overall task completion time, collision risk, and utilization efficiency. To address this, this research employs a multi-objective mixed-integer programming model (MO-MIP) to formulate the scheduling problem of AGVs. The optimization algorithms, such as Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Reference Point-based Non-dominated Sorting Genetic Algorithm (NSGA-III) are utilized to obtain the Pareto optimal solutions in solving the scheduling problem. The simulation experiment on three distinct manufacturing workshop scenarios was conducted to examine the effectiveness of the model. The outcomes illustrated that the NSGA-II and NSGA-III exhibit reduced penalty cost, power consumption, collision risk, task completion time, and higher utilization efficiency. These algorithms also showed better computational efficiency and outperformed baseline algorithms under three manufacturing scenarios. These outcomes indicate that the proposed method is a promising solution for the industrial sector to perform material transportation in an efficient manner. |
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| AbstractList | •Developed MO-MIP model to optimize AGV scheduling in manufacturing systems•Applied NSGA-II and NSGA-III for efficient multi-objective optimization•Tested on three distinct manufacturing workshop scenarios•Outperformed traditional algorithms across three industrial simulations
In recent years, the widespread usage of Automated Guided Vehicles (AGVs) has become prevalent in material transportation systems of industries. The AGVs are known for their operational flexibility and efficiency, but efficient scheduling remains a crucial issue due to the conflicting factors, including deviation penalties for task execution times, power consumption, overall task completion time, collision risk, and utilization efficiency. To address this, this research employs a multi-objective mixed-integer programming model (MO-MIP) to formulate the scheduling problem of AGVs. The optimization algorithms, such as Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Reference Point-based Non-dominated Sorting Genetic Algorithm (NSGA-III) are utilized to obtain the Pareto optimal solutions in solving the scheduling problem. The simulation experiment on three distinct manufacturing workshop scenarios was conducted to examine the effectiveness of the model. The outcomes illustrated that the NSGA-II and NSGA-III exhibit reduced penalty cost, power consumption, collision risk, task completion time, and higher utilization efficiency. These algorithms also showed better computational efficiency and outperformed baseline algorithms under three manufacturing scenarios. These outcomes indicate that the proposed method is a promising solution for the industrial sector to perform material transportation in an efficient manner. |
| ArticleNumber | 102127 |
| Author | Huo, Xiang Nie, Lei |
| Author_xml | – sequence: 1 givenname: Xiang surname: Huo fullname: Huo, Xiang email: 98940412@bjtu.edu.cn – sequence: 2 givenname: Lei surname: Nie fullname: Nie, Lei |
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| Cites_doi | 10.1016/j.engappai.2023.105944 10.1016/j.swevo.2024.101651 10.1016/j.ress.2021.108264 10.1016/j.asoc.2024.111846 10.1016/j.advengsoft.2024.103696 10.1016/j.cie.2023.109093 10.1016/j.jmsy.2023.03.007 10.1080/00207543.2024.2325583 10.1016/j.cor.2024.106674 10.1016/j.eswa.2022.117738 10.1080/00207543.2024.2316476 10.1016/j.eswa.2021.114779 10.1016/j.eswa.2023.122197 10.1016/j.simpat.2024.102945 10.1016/j.trc.2024.104548 10.1016/j.cie.2024.110686 10.1016/j.cie.2018.10.007 10.1109/TASE.2019.2963285 10.1016/j.cie.2023.109611 10.1016/j.tre.2023.103110 10.1016/j.compstruct.2025.118921 10.1016/j.aei.2024.102804 10.1016/j.rcim.2022.102397 10.1109/TASE.2020.3015110 10.1016/j.cie.2021.107791 10.1016/j.compeleceng.2024.109824 10.3390/sym16081030 10.3390/math12152300 10.1016/j.ymssp.2023.110986 10.3390/math13030340 10.1016/j.trb.2023.102871 10.1016/j.trc.2024.104724 10.1016/j.robot.2024.104683 10.1016/j.compstruc.2023.107213 |
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| Keywords | Multi-objective mixed integer programming Non-dominated sorting genetic algorithm Automatic guided vehicle Reference point-based non-dominated sorting genetic algorithm and task scheduling |
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| SubjectTerms | Automatic guided vehicle Multi-objective mixed integer programming Non-dominated sorting genetic algorithm Reference point-based non-dominated sorting genetic algorithm and task scheduling |
| Title | An intelligent automated guided vehicle scheduling framework for manufacturing: Balancing energy, efficiency, and task completion |
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