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Scheduling of AGV with Group Operation Area in Automated Terminal by Hybrid Genetic Algorithm

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Proceedings of the Fifteenth International Conference on Management Science and Engineering Management (ICMSEM 2021)

Abstract

Scheduling is one of the very important tools for treating a complex combinatorial optimization problem (COP) model, where it can have a major impact on the productivity of a manufacturing process. Most models of scheduling are confirmed as NP-hard or NP-complete problems. The aim at scheduling is to find a schedule with the best performance through selecting resources for each operation, the sequence of each resource and the beginning time. Genetic algorithm (GA) is one of the most efficient methods among metaheuristics for solving the real-world manufacturing problems. In this paper, we survey the literature review on the optimization of Automate Guide Vehicle (AGV) transportation efficiency in the terminal, especially how to reduce the waiting time of AGV. From the point of AGV road blocking, the scheduling mode of group operation area is proposed. In order to minimize the maximum completion time of AGV, an AGV scheduling optimization model is established considering the interference constraints and AGV congestion in the actual operation of the terminal. Hybrid Genetic Algorithm with Fuzzy Logic Controller (HGA-FLC) is used to simulate the behavior of AGVs, and different scale examples are designed to solve the problem. Compared with GA, the experimental results show that this algorithm can effectively improve the efficiency of AGVs operation, reduce the waiting time and number of jams of AGV, which provide the basis of the actual operation of the terminal.

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References

  1. Brucker, P.: Scheduling algorithms. J.-Oper. Res. Soc. 50, 774 (1999)

    Google Scholar 

  2. Chen, L., Langevin, A., Lu, Z.: Integrated scheduling of crane handling and truck transportation in a maritime container terminal. Eur. J. Oper. Res. 225(1), 142–152 (2013)

    Article  Google Scholar 

  3. Chen, L.H., Gao, Z.J., et al.: The integrated yard truck and yard crane scheduling and storage allocation problem at container terminals. Appl. Mech. Mater. 587, 1797–1800 (2014)

    Article  Google Scholar 

  4. Gen, M., Lin, L.: Nature-inspired and evolutionary techniques for automation. In: Nof, S. (ed.) Springer Handbook of Automation. Springer, Cham (2021)

    Google Scholar 

  5. Gen, M., Cheng, R., Lin, L.: Network Models and Optimization: Multiobjective Genetic Algorithm Approach. Springer, London (2008). https://doi.org/10.1007/978-1-84800-181-7

    Book  MATH  Google Scholar 

  6. He, J., Huang, Y., et al.: Integrated internal truck, yard crane and quay crane scheduling in a container terminal considering energy consumption. Expert Syst. Appl. 42(5), 2464–2487 (2015)

    Article  Google Scholar 

  7. Hwang, H., Moon, S., Gen, M.: An integrated model for the design of end-of-aisle order picking system and the determination of unit load sizes of AGVs. Comput. Ind. Eng. 42(2–4), 249–258 (2002)

    Article  Google Scholar 

  8. Kaveshgar, N., Huynh, N.: Integrated quay crane and yard truck scheduling for unloading inbound containers. Int. J. Prod. Econ. 159, 168–177 (2015)

    Article  Google Scholar 

  9. Lee, L.H., Chew, E.P., et al.: Vehicle dispatching algorithms for container transshipment hubs. OR Spectr. 32(3), 663–685 (2010)

    Article  Google Scholar 

  10. Lin, L., Gen, M.: Hybrid evolutionary optimisation with learning for production scheduling: state-of-the-art survey on algorithms and applications. Int. J. Prod. Res. 56(1–2), 193–223 (2018)

    Article  Google Scholar 

  11. Lin, L., Shinn, S.W., et al.: Network model and effective evolutionary approach for AGV dispatching in manufacturing system. J. Intell. Manuf. 17(4), 465–477 (2006)

    Article  Google Scholar 

  12. Nishimura, E., Imai, A., Papadimitriou, S.: Yard trailer routing at a maritime container terminal. Transp. Res. Part E Logist. Transp. Rev. 41(1), 53–76 (2005)

    Article  Google Scholar 

  13. Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems. Springer, Cham (2018)

    Google Scholar 

  14. Tang, L., Zhao, J., Liu, J.: Modeling and solution of the joint quay crane and truck scheduling problem. Eur. J. Oper. Res. 236(3), 978–990 (2014)

    Article  MathSciNet  Google Scholar 

  15. Vahdani, B., Mansour, F., et al.: Bi-objective optimization for integrating quay crane and internal truck assignment with challenges of trucks sharing. Knowl.-Based Syst. 163, 675–692 (2019)

    Article  Google Scholar 

  16. Yang, Y., Zhong, M., et al.: An integrated scheduling method for AGV routing in automated container terminals. Comput. Ind. Eng. 126, 482–493 (2018)

    Article  Google Scholar 

  17. Yu, X., Gen, M.: Introduction to Evolutionary Algorithms. Springer, London (2010). https://doi.org/10.1007/978-1-84996-129-5

    Book  MATH  Google Scholar 

  18. Yun, Y., Gen, M.: Performance analysis of adaptive genetic algorithms with fuzzy logic and heuristics. Fuzzy Optim. Decis. Making 2, 161–175 (2003)

    Article  MathSciNet  Google Scholar 

  19. Zhong, M., Yang, Y., et al.: Multi-AGV scheduling for conflict-free path planning in automated container terminals. Comput. Ind. Eng. 142, 106371 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

This work is partly supported by the National Natural Science Foundation of China under Grant (71471110, 71301101, and 61540045); Science and Technology Commission of Shanghai Municipality (14170501500, 16DZ1201402); Leading Academic Discipline Project of Shanghai Municipal Education Commission (J50604); Social Science Foundation of Shaanxi Province (2015D060) and Grant-in-Aid for Scientific Research (C) of Japan Society of Promotion of Science (JSPS: No. 19K12148). Also, I would like to express my gratitude to my classmates. They gave me a lot of suggestions in this study, which prompted me to finish this paper.

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Correspondence to Chengji Liang .

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Shen, Z., Liang, C., Gen, M. (2021). Scheduling of AGV with Group Operation Area in Automated Terminal by Hybrid Genetic Algorithm. In: Xu, J., García Márquez, F.P., Ali Hassan, M.H., Duca, G., Hajiyev, A., Altiparmak, F. (eds) Proceedings of the Fifteenth International Conference on Management Science and Engineering Management. ICMSEM 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-79203-9_33

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