Affiliation:
1. School of Computer Science and Technology, Qinghai Normal University, Xining 810008, Qinghai, China
2. School of Mathematics and Statistics, Qinghai Normal University, Xining 810008, Qinghai, China
Abstract
<abstract><p>The multi-point dynamic aggregation problem (MPDAP) comes mainly from real-world applications, which is characterized by dynamic task assignation and routing optimization with limited resources. Due to the dynamic allocation of tasks, more than one optimization objective, limited resources, and other factors involved, the computational complexity of both route programming and resource allocation optimization is a growing problem. In this manuscript, a task scheduling problem of fire-fighting robots is investigated and solved, and serves as a representative multi-point dynamic aggregation problem. First, in terms of two optimized objectives, the cost and completion time, a new bilevel programming model is presented, in which the task cost is taken as the leader's objective. In addition, in order to effectively solve the bilevel model, a differential evolution is developed based on a new matrix coding scheme. Moreover, some percentage of high-quality solutions are applied in mutation and selection operations, which helps to generate potentially better solutions and keep them into the next generation of population. Finally, the experimental results show that the proposed algorithm is feasible and effective in dealing with the multi-point dynamic aggregation problem.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Reference32 articles.
1. B. Xin, Y. G. Zhu, Y. L. Ding, G. Q. Gao, Coordinated motion planning of multiple robots in multi-point dynamic aggregation task, in Proceedings IEEE International Conference on Control and Automation, Kathmandu, Nepal, 2016,933–938. https://doi.org/10.1109/ICCA.2016.7505398
2. V. Akbari, F. S. Salman, Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity, European J. Operat. Res., 257 (2017), 625–640. https://doi.org/10.1016/j.ejor.2016.07.043
3. A. Khan, B. Rinner, A. Cavallaro, Cooperative robots to observe moving targets: Review, IEEE Transact. Cybernet., 48 (2018), 187–198. https://doi.org/10.1109/TCYB.2016.2628161
4. W. Q. Jin, S. Q. Dong, C. Q. Yu, Q. Q. Luo, A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning, Comput. Biol. Med., 146 (2022), 105560. https://doi.org/10.1016/j.compbiomed.2022.105560
5. R. P. Yuan, J. T. Li, X. L. Wang, L. Y. He, Multirobot task allocation in e-commerce robotic mobile fulfillment systems, Math. Problems Eng., (2021), Article ID 6308950. https://doi.org/10.1155/2021/6308950
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献