Research on Production Balance Technology of Robotic Assembly Line Based on Fruit Fly Optimization Algorithm

To address the balancing problem of the robot bilateral assembly line, a mathematical model is constructed with cycle time and energy consumption as optimization objectives, subject to multiple constraints. This model is solved using an Improved Self-adaptive Fruit Fly Optimization Algorithm (ISFOA)...

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Vydáno v:International Conference on Industrial Mechatronics and Automation (Online) s. 789 - 794
Hlavní autoři: Liu, Guanquan, Jiao, Ying, Liu, Kun, Wu, Yibo
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 03.08.2025
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ISSN:2152-744X
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Abstract To address the balancing problem of the robot bilateral assembly line, a mathematical model is constructed with cycle time and energy consumption as optimization objectives, subject to multiple constraints. This model is solved using an Improved Self-adaptive Fruit Fly Optimization Algorithm (ISFOA). In the algorithm design, tent chaotic mapping is introduced during population initialization to ensure uniform distribution of the population. Additionally, an individual information utilization mechanism is incorporated, where there is an 80% probability of generating the next generation based on the global optimum and a 20% probability of generating it through random uniform perturbation of individuals, thereby fully utilizing population information. Simulated annealing-based multiple mutation rules and local perturbation are also integrated into the algorithm. Considering the characteristics of the model, a four-layer coding scheme is designed, and a decoding method is employed to achieve a better balance between local and global optima. Finally, the effectiveness of the proposed ISFOA algorithm for solving the Robotic Two-sided Assembly Line Balancing Problem of Type-2 (RTALBP-2) is verified by solving various cases of different scales and comparing its performance with other algorithms.
AbstractList To address the balancing problem of the robot bilateral assembly line, a mathematical model is constructed with cycle time and energy consumption as optimization objectives, subject to multiple constraints. This model is solved using an Improved Self-adaptive Fruit Fly Optimization Algorithm (ISFOA). In the algorithm design, tent chaotic mapping is introduced during population initialization to ensure uniform distribution of the population. Additionally, an individual information utilization mechanism is incorporated, where there is an 80% probability of generating the next generation based on the global optimum and a 20% probability of generating it through random uniform perturbation of individuals, thereby fully utilizing population information. Simulated annealing-based multiple mutation rules and local perturbation are also integrated into the algorithm. Considering the characteristics of the model, a four-layer coding scheme is designed, and a decoding method is employed to achieve a better balance between local and global optima. Finally, the effectiveness of the proposed ISFOA algorithm for solving the Robotic Two-sided Assembly Line Balancing Problem of Type-2 (RTALBP-2) is verified by solving various cases of different scales and comparing its performance with other algorithms.
Author Liu, Kun
Liu, Guanquan
Jiao, Ying
Wu, Yibo
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  givenname: Yibo
  surname: Wu
  fullname: Wu, Yibo
  email: 1614491522@qq.com
  organization: College of Management Engineering, Qingdao University of Technology,Qingdao,Shandong,China
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Snippet To address the balancing problem of the robot bilateral assembly line, a mathematical model is constructed with cycle time and energy consumption as...
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StartPage 789
SubjectTerms Assembly
Assembly line balancing
Convergence
Encoding
Energy consumption
Fruit fly optimization algorithm
Mathematical models
Mixed-integer programming model
Optimization
Perturbation methods
Production
Programming
Robot kinematics
Robotic two-sided assembly line
Sequence-dependent
Title Research on Production Balance Technology of Robotic Assembly Line Based on Fruit Fly Optimization Algorithm
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