Assembly line balancing and optimal scheduling for flexible manufacturing workshop
In order to adapt to the changing and personalized market demand, the traditional single-type mass production manufacturing mode is gradually changing to multi-species small batch personalized custom production, flexible manufacturing in machinery manufacturing occupies an increasingly important pos...
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| Vydáno v: | Journal of mechanical science and technology Ročník 38; číslo 6; s. 2757 - 2772 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Seoul
Korean Society of Mechanical Engineers
01.06.2024
Springer Nature B.V 대한기계학회 |
| Témata: | |
| ISSN: | 1738-494X, 1976-3824 |
| On-line přístup: | Získat plný text |
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| Abstract | In order to adapt to the changing and personalized market demand, the traditional single-type mass production manufacturing mode is gradually changing to multi-species small batch personalized custom production, flexible manufacturing in machinery manufacturing occupies an increasingly important position. However, complexity, uncertainty, multi-objective and multi-constraints are the problems faced by production scheduling in flexible manufacturing workshop, which restricts the intelligent transformation of enterprises. This paper takes an assembly shop as the research object, and carries out research on assembly line balancing and multi-automatic guided vehicle (AGV) scheduling problems in shop production. Firstly, for the multi-objective assembly line balancing problem with fluctuating demand, the relevant assembly line model is established by changing only the number of workstations and replanning the boundary, and an improved multi-objective whale optimization algorithm is proposed to reduce the rebalancing cost of the assembly line. Secondly, the multi-AGV dynamic scheduling problem with corresponding scheduling objective weights according to the current number of AGVs is analyzed, and a mathematical model is established to maximize the value of the objective function by considering a variety of factors under the premise of meeting the production requirements of the assembly line. Then, a scheduling rule selection method based on neural network and knowledge base is proposed to determine the optimal combination of scheduling rules for different system states. Finally, a simulation analysis of an assembly workshop example is carried out to verify the feasibility and effectiveness of the proposed method in solving the assembly line balancing problem and the multi-AGV scheduling problem, which improves the manufacturing efficiency and the resilience of the workshop. |
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| AbstractList | In order to adapt to the changing and personalized market demand, the traditional single-type mass production manufacturing mode is gradually changing to multi-species small batch personalized custom production, flexible manufacturing in machinery manufacturing occupies an increasingly important position. However, complexity, uncertainty, multi-objective and multi-constraints are the problems faced by production scheduling in flexible manufacturing workshop, which restricts the intelligent transformation of enterprises. This paper takes an assembly shop as the research object, and carries out research on assembly line balancing and multi-automatic guided vehicle (AGV) scheduling problems in shop production. Firstly, for the multi-objective assembly line balancing problem with fluctuating demand, the relevant assembly line model is established by changing only the number of workstations and replanning the boundary, and an improved multi-objective whale optimization algorithm is proposed to reduce the rebalancing cost of the assembly line. Secondly, the multi-AGV dynamic scheduling problem with corresponding scheduling objective weights according to the current number of AGVs is analyzed, and a mathematical model is established to maximize the value of the objective function by considering a variety of factors under the premise of meeting the production requirements of the assembly line. Then, a scheduling rule selection method based on neural network and knowledge base is proposed to determine the optimal combination of scheduling rules for different system states. Finally, a simulation analysis of an assembly workshop example is carried out to verify the feasibility and effectiveness of the proposed method in solving the assembly line balancing problem and the multi-AGV scheduling problem, which improves the manufacturing efficiency and the resilience of the workshop. In order to adapt to the changing and personalized market demand, the traditional single-type mass production manufacturing mode is gradually changing to multi-species small batch personalized custom production, flexible manufacturing in machinery manufacturing occupies an increasingly important position. However, complexity, uncertainty, multiobjective and multi-constraints are the problems faced by production scheduling in flexible manufacturing workshop, which restricts the intelligent transformation of enterprises. This paper takes an assembly shop as the research object, and carries out research on assembly line balancing and multi-automatic guided vehicle (AGV) scheduling problems in shop production. Firstly, for the multi-objective assembly line balancing problem with fluctuating demand, the relevant assembly line model is established by changing only the number of workstations and replanning the boundary, and an improved multi-objective whale optimization algorithm is proposed to reduce the rebalancing cost of the assembly line. Secondly, the multi-AGV dynamic scheduling problem with corresponding scheduling objective weights according to the current number of AGVs is analyzed, and a mathematical model is established to maximize the value of the objective function by considering a variety of factors under the premise of meeting the production requirements of the assembly line. Then, a scheduling rule selection method based on neural network and knowledge base is proposed to determine the optimal combination of scheduling rules for different system states. Finally, a simulation analysis of an assembly workshop example is carried out to verify the feasibility and effectiveness of the proposed method in solving the assembly line balancing problem and the multi-AGV scheduling problem, which improves the manufacturing efficiency and the resilience of the workshop. KCI Citation Count: 0 |
| Author | Zhang, Song Hou, Wen |
| Author_xml | – sequence: 1 givenname: Wen surname: Hou fullname: Hou, Wen organization: School of Mechanical Engineering, Shandong University, Key Laboratory of High-efficiency and Clean Mechanical Manufacture, Ministry of Education, Shandong University – sequence: 2 givenname: Song surname: Zhang fullname: Zhang, Song email: zhangsong@sdu.edu.cn organization: School of Mechanical Engineering, Shandong University, Key Laboratory of High-efficiency and Clean Mechanical Manufacture, Ministry of Education, Shandong University |
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| Keywords | Intelligent manufacturing Dynamic scheduling Assembly line balancing Neural network Whale optimization algorithm |
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| SubjectTerms | Algorithms Assembly lines Automated guided vehicles Balancing Control Customization Dynamical Systems Effectiveness Engineering Flexible manufacturing systems Industrial and Production Engineering Knowledge bases (artificial intelligence) Knowledge management Manufacturing Mass production Mechanical Engineering Multiple objective analysis Neural networks Optimization Original Article Production scheduling Scheduling Vibration Workshops 기계공학 |
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