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Multi-AGVs path planning based on improved ant colony algorithm

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Abstract

The scheduling problem in multi-automated guided vehicles (AGVs) system involves the job-shop scheduling problem and the vehicle routing problem. In the real world, the scheduling problem is limited by some constraint conditions such as the system should be able to avoid collisions and route correction is asked to be easily realized. This paper studies the scheduling and collision-free routing problem of AGVs. Mathematical programming model is given for this problem, and the algorithm is improved based on multi-objective programming to optimize the pheromone matrix. By calculation using available test problems, the performance of the two methods is compared. The improved ant colony algorithm is empirically evaluated. The result shows that the mathematical programming model has good effect but limited application scope. The improved algorithm improves the performance of the existing algorithm, and finally, the rationality of the improved algorithm for large instance key parameter settings and scheme selection is verified by eleven test samples.

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References

  1. Theorin A, Bengtsson K, Provost J et al (2017) An event-driven manufacturing information system architecture for Industry 4.0. Int J Prod Res 55(5):1–15

    Article  Google Scholar 

  2. Uzsoy R (1994) Scheduling a single batch processing machine with non-identical job sizes. Int J Prod Res 32(7):1615–1635

    Article  Google Scholar 

  3. Johnson SM (1954) Optimal two- and three-stage production schedules with setup times included. Naval Res Logist Q 1(1):61–68

    Article  Google Scholar 

  4. Zhao X, Guo X (2018) Branch and bound algorithm for solving hybrid flow shop robotic cells scheduling problem with blocking. J Comput Appl 38(7):2095–2099

    Google Scholar 

  5. Priore P, Ponte B, Puente J, Gómez A (2018) Learning based scheduling of flexible manufacturing systems using ensemble methods. Comput Ind Eng 126:282–291

    Article  Google Scholar 

  6. Shu H, Zhao G, Zhao D (2010) Two kinds of heuristic rules in synthesis scheduling of FMS. Manuf Technol Mach Tool 7:29–32

    Google Scholar 

  7. Sobeyko O, Mönch L (2016) Integrated process planning and scheduling for large-scale flexible job shops using metaheuristics. Int J Prod Res 55(2):392–409

    Article  Google Scholar 

  8. Blazewicz J, Burkard RE, Finke G et al (1994) Vehicle scheduling in two-cycle flexible manufacturing systems. Math Comput Model Int J 20(2):19–31

    Article  Google Scholar 

  9. Elias X, Paraskevi Z, Andreas N (2016) Path planning and scheduling for a fleet of autonomous vehicles. Robotica 34(10):2257–2273

    Article  Google Scholar 

  10. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybernet Part B Cybernet 26(1):29–41

    Article  Google Scholar 

  11. Fox B, Xiang W, Lee HP (2007) Industrial applications of the ant colony optimization algorithm. Int J Adv Manuf Technol 31(7-8):805–814

    Article  Google Scholar 

  12. Zhang S, Wong TN (2017) Flexible job-shop scheduling/rescheduling in dynamic environment: a hybrid MAS/ACO approach. Int J Prod Res 55(11):3173–3196

    Article  Google Scholar 

  13. You X, Liu S, Lv JQ (2017) Ant colony algorithm based on dynamic search strategy and its application on path planning of robot. Control Decis 3:29–32

    Google Scholar 

  14. Navarro G (2001) A guided tour to approximate string matching. ACM Comput Surv 33(1):31–88

    Article  Google Scholar 

  15. Mao C, Xiao L, Yu X, Chen J (2015) Adapting ant colony optimization to generate test data for software structural testing. Swarm Evol Comput 20:23–36

    Article  Google Scholar 

  16. Mao C, Yu X, Chen J, Chen J (2012) Generating test data for structural testing based on ant colony optimization. In: Proceedings of 12th International Conference on Quality Software (QSIC’12), pp 98–101

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Acknowledgements

The tenth Graduate Innovation Fund of Wuhan Institute of Technology (Grant Nos. CX2017075, CX2018199, CX2018206).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, China

    Guohong Yi, Zhili Feng, Pushan Li, Wang Jin & Siyuan Chen

  2. Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, China

    Guohong Yi

  3. Electronic Information School, Wuhan University, Wuhan, China

    Tiancan Mei

Authors
  1. Guohong Yi
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  2. Zhili Feng
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  3. Tiancan Mei
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  4. Pushan Li
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  5. Wang Jin
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  6. Siyuan Chen
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Correspondence to Zhili Feng.

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Yi, G., Feng, Z., Mei, T. et al. Multi-AGVs path planning based on improved ant colony algorithm. J Supercomput 75, 5898–5913 (2019). https://doi.org/10.1007/s11227-019-02884-9

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  • DOI: https://doi.org/10.1007/s11227-019-02884-9

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