Multi-AGV dispatching and routing problem based on a three-stage decomposition method

Yejun Hu; Liangcai Dong; Lei Xu; Yejun Hu; Liangcai Dong; Lei Xu

doi:10.3934/mbe.2020279

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 5: 5150-5172. doi: 10.3934/mbe.2020279

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Research article

Multi-AGV dispatching and routing problem based on a three-stage decomposition method

College of Logistics Engineering, Shanghai Maritime University, Shanghai, 201306, China

Received: 26 April 2020 Accepted: 13 July 2020 Published: 31 July 2020

Automatic guided vehicle (AGV) is a device for horizontal transportation between quay cranes and yard cranes in an automated container terminal. In which dispatching and routing problem (DRP) of the AGV system is a vital as well as basic issue. In the application of the actual AGV system, several practical factors including avoiding conflicts, path smoothness, difficulty in adjusting routes and anti-interference must be considered. The present study establishes the model with the goal of minimizing AGV travel distance, reducing operation time and response time. Furthermore, a three-stage decomposition solution to the problem was proposed by combining the advantages of pre-planning algorithm and real-time planning algorithm, which combines A^* algorithm with the principle of time window to plan the path of each AGV in time order. Finally, the effectiveness of this method in path search and time optimization is illustrated and the system efficiency is improved by comparing and analyzing the calculation examples of different scales.
- Multi-AGV systems,
- dispatching and routing problem,
- three-stage decomposition,
- no conflict
Citation: Yejun Hu, Liangcai Dong, Lei Xu. Multi-AGV dispatching and routing problem based on a three-stage decomposition method[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 5150-5172. doi: 10.3934/mbe.2020279

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Abstract

Automatic guided vehicle (AGV) is a device for horizontal transportation between quay cranes and yard cranes in an automated container terminal. In which dispatching and routing problem (DRP) of the AGV system is a vital as well as basic issue. In the application of the actual AGV system, several practical factors including avoiding conflicts, path smoothness, difficulty in adjusting routes and anti-interference must be considered. The present study establishes the model with the goal of minimizing AGV travel distance, reducing operation time and response time. Furthermore, a three-stage decomposition solution to the problem was proposed by combining the advantages of pre-planning algorithm and real-time planning algorithm, which combines A^* algorithm with the principle of time window to plan the path of each AGV in time order. Finally, the effectiveness of this method in path search and time optimization is illustrated and the system efficiency is improved by comparing and analyzing the calculation examples of different scales.

References

[1]	C. L. Liu, H. Jula, P. A. Loannou, Design, simulation, and evaluation of automated container terminals, IEEE Trans. Intell. Transp. Syst., 3 (2002), 12-26.
[2]	H. T. Hu, X. Chen, T. Wang, Y. Zhang, A three-stage decomposition method for the joint vehicle dispatching and storage allocation problem in automated container terminals, Comput. Ind. Eng., 129 (2019), 90-101.
[3]	H. Fazlollahtabar, M. Saidi-Mehrabad, Autonomous guided vehicles: Methods and models for optimal path planning, Studies in Systems, Decision and Control, 20 (2015).
[4]	M. S. Zhong, Y. S. Yang, Y. M. Zhou, Free-conflict AGV path planning in automated terminals based on speed control, Comp. Sci., 2019. DOI: 10.11896/j.issn.1002-137x.2019.07.047.
[5]	I. Draganjac, T. Petrovic, D. Miklic, Z. Kovacic, J. Orsulic, Highly-scalable traffic management of autonomous industrial transportation systems, Robot Cim-Int. Manuf., 63 (2020), 101915.
[6]	J. B. Xin, R. R. Negenborn, F. Corman, G. Lodewijks, Control of interacting machines in automated container terminals using a sequential planning approach for collision avoidance, Transp. Res. Part C Emerg. Technol., 60 (2015), 377-396.
[7]	E. Gawrilow, M. Klimm, Conflict-free vehicle routing, Euro. J. Transp. Logist., 1 (2012), 87-111.
[8]	C. Oboth, R. Batta, M. Karwan, Dynamic conflict-free routing of automated guided vehicles, Int. J. Prod. Res., 37 (1999), 2003-2030.
[9]	T. J. Chen, Y. Sun, W. Dai, On the Shortest and Conflict-Free Path Planning of Multi-AGV System Based on Dijkstra Algorithm and the Dynamic Time-Window Method, Adv. Mater. Res., 645 (2013), 267-271.
[10]	L. H. He, P. H. Lou, X. M. Qian, Conflict-free automated guided vehicles routing based on time window, Comput. Integr. Manuf., 16 (2010), 2630-2634.
[11]	X. Y. Ma, Y. M. Bian, F. Gao, An improved shuffled frog leaping algorithm for multiload AGV dispatching in automated container terminals, Math. Probl. Eng., 2020 (2020), 1-13.
[12]	P. E. Hart, N. J. Nilsson, B. Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE Trans. Syst. enc. Cybern., 4 (1972), 28-29.
[13]	K. Daniel, A. Nash, S. Koenig, A. Felner: Any-angle path planning on grids, J. Artif. Intell. Res., 39 (2010), 533-579.
[14]	Y. Xin, H. Liang, M. Du, An improved A^* algorithm for searching infinite neighbourhoods, Robot, 2014. DOI: 10.13973/j.cnki.robot.2014.0627.
[15]	D. Frantiek, A. Babinec, M. Kajan, Path planning with modified a Star Algorithm for a mobile robot, Proced. Eng., 96 (2014), 59-69.
[16]	M. X. Lin, K. Yuan, C. Shi, Y. Wang, Path planning of mobile robot based on improved A∗ algorithm, IEEE Control Syst., DOI: 2017. 10.1109/CCDC.2017.7979125.
[17]	U. A. Umar, M. K. A. Ariffin, N. Ismail, Priority-based genetic algorithm for conflict-free automated guided vehicle routing, Proced. Eng., 50 (2012), DOI: 10.1016/j.proeng.2012.10.080.
[18]	J. Luo, Y. Wu, Modelling of dual-cycle strategy for container storage and vehicle scheduling problems at automated container terminals, Transp. Res. Part E., 79 (2015), 49-64.
[19]	S. Maza, P. Castagna, P, Conflict-free AGV routing in bi-directional network. Emerging Technologies and Factory Automation, IEEE Symp. Emerg. Technol. Fact. Autom. ETFA, DOI: 2001. 10.1109/ETFA.2001.997777.
[20]	M. S. Zhong, Y. S. Yang, Multi-AGV scheduling for conflict-free path planning in automated container terminals, Comput. Ind. Eng., 142 (2020), 106371.
[21]	G. Qing, Z. Zheng, X. Yue, Path-planning of automated guided vehicle based on improved Dijkstra algorithm, Control Decision Conference, IEEE, 7 (2017), 138-143.

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