Li Junjun, Xu Bowei, Yang Yongsheng, Wu Huafeng


  1. [1] T. Miyamoto and K. Inoue. Local and random searches for dispatch and conflict-free routing problem of capacitatedAGV systems. Computers & Industrial Engineering, 91, 2016, 1-9.
  2. [2] M. Zhang, R. Batta and R. Nagi. Modeling of workflow congestion and optimization of flow routing in amanufacturing/warehouse facility. Management Science, 55, 2009, 267-280.
  3. [3] S. J. Shao, Z. Y. Xia, G. D. Chen, J. Zhang, Y. Hu and J. W. Zhang. A new scheme of multiple automated guidedvehicle system for collision and deadlock free. IEEE International Conference on Information Science &Technology, 2014, 606-610.
  4. [4] D. Roy, A. Gupta and R. Koster. A non-linear traffic flow-based queuing model to estimate container terminalthroughput with AGVs. International Journal of Production Research, 54 (2), 2015, 1-21.
  5. [5] H. Fazlollahtabar, M. Saidimehrabad and E. Masehian. Mathematical model for deadlock resolution in multiple AGVscheduling and routing network: a case study. Industrial Robot, 42 (3), 2015, 252-263.
  6. [6] D. Wu, Y. Sun and X. Wang. An Improved RRT Algorithm for Crane Path Planning. International Journal ofRobotics & Automation, 31(2), 2016, 84-92.
  7. [7] M. Alajlan, I. Chaari and A. Koubaa. Global Robot Path Planning Using GA for Large Grid Maps: Modelling,Performance and Experimentation. International Journal of Robotics & Automation, 31(6), 2016, 484-495.
  8. [8] L. Li, X. Wang and D. Xu. An Accurate Path Planning Algorithm Based on Triangular Meshes in Robotic FibrePlacement. International Journal of Robotics & Automation, 32 (1), 2017, 22-32.
  9. [9] Z. Lu. Modeling of yard congestion and optimization of yard template in container ports, Transportation ResearchPart B, 90, 2016, 83-104.
  10. [10] T. T. Mac, C. Copot, D. T. Tran and R. D. Keyser. A hierarchical global path planning approach for mobile robotsbased on multi-objective particle swarm optimization. Applied Soft Computing, 59, 2017, 68–76.
  11. [11] J. Wu, L. Yang, T. Li, C. Zhang and Z. Li. Rule-based fuzzy classifier based on quantum ant optimization algorithm.J Intell Fuzzy Syst, 29(6), 2015, 2365-2371.
  12. [12] M. Liu, F. Zhang, Y. Ma, H. R. Pota and W. Shen. Evacuation path optimization based on quantum ant colonyalgorithm. Adv Eng Inform, 30 (3), 2016, 259-267.
  13. [13] X. Chen, X. Xia and R. Yu. Quantum Ant Colony Algorithm Based on Bloch Coordinates. Springer BerlinHeidelberg, 7473, 2012, 405-412.
  14. [14] J. Li, S. Zhao and A. Lu. Quantum ant colony optimization algorithm based on Bloch spherical search. Int J Eng Sci,4(4), 2015, 41-51.
  15. [15] O. Khatib. Real-time obstacle avoidance for manipulators and mobile robots. Proceedings of IEEE InternationalConference on Robotics and Automation. Washington, DC: IEEE, 1990, 500-505.
  16. [16] K. Talan and G. R. Bamnote. Shortest Path Finding Using a Star Algorithm and Minimum weight Node FirstPrinciple. International Journal of Innovative Research in Computer and Communication Engineering, 3(2), 2015,1258-1262.
  17. [17] S. Zhang, Y. Yang, C. Liang, B. Xu and J. Li. Optimal Control of Multiple AGV Path Conflict in AutomatedTerminals. Journal of Transportation Systems Engineering and Information Technology, 17 (2), 2017, 83-89. (inChinese)
  18. [18] F. Zhang, M. Liu, Z. Zhou, W. M. Shen. Quantum ant colony algorithm-based emergency evacuation path choicealgorithm. IEEE International Conference on Computer Supported Cooperative Work in Design, 2013, 576-580

Important Links:

Go Back