Effective Hybrid Genetic Algorithm for Multi-Robot Task Allocation with Limited Span

Research findings on robotics discuss a multi-robot task allocation problem, where industrial robots with limited working spans need to jointly perform weld lines in large workpieces. The objective is to minimize the cycle time when scheduling robots to work together efficiently. A mathematical model and an effective hybrid genetic algorithm are proposed to solve this problem, which includes a specific region division method, a dedicated route-based crossover, and an effective neighborhood-based local search procedure. Extensive experimental results show that the algorithm significantly outperforms two refer methods with an average improvement of 6.06% and 4.6%.

Key Takeaways:

• The multi-robot task allocation problem involves a set of industrial robots with limited working spans that need to jointly perform a set of weld lines in large workpieces.
• The objective of this problem is to minimize the cycle time when scheduling robots to work together efficiently.
• A mathematical model and an effective hybrid genetic algorithm are proposed to solve this problem, which includes a specific region division method, a dedicated route-based crossover, and an effective neighborhood-based local search procedure.
• Extensive experimental results on three benchmark instances show that the algorithm significantly outperforms two refer methods with an average improvement of 6.06% and 4.6%.
• The algorithm's effectiveness is also verified through additional experiments on real-world instances.
• The research has been peer-reviewed and published in the Journal of Engineering.
• The work was funded by the National Key RD Plan of China, National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities, Shenzhen Science and Technology Program, and Shanghai Pujiang Program.
• The research team includes Bo Zhou, Wenbo Liu, Yongcong Zhang, Pengfei He, Shihua Li, and Zhian Kuang from Southeast University.

Statistics:

• 6.06% average improvement of the algorithm over two refer methods on three benchmark instances.
• 4.6% average improvement of the algorithm over two refer methods on three benchmark instances.
• 280: The volume number of the publication in which the research was published (Expert Systems With Applications, 2025).
• 2025: The year in which the research was published.

Sources:

• An Effective Hybrid Genetic Algorithm for the Multi-robot Task Allocation Problem With Limited Span (Expert Systems With Applications, 2025, Vol. 280).
• Southeast University.