Skip to main content

Advertisement

Log in

A Coscheduling Model of Automated Single-Beam Cranes and AGVs in Assembly Workshop

  • Research Article-Mechanical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The simultaneous scheduling of cranes and automated guided vehicles (AGVs) has been a critical problem, especially in traditional heavy-duty manufacturing enterprises. Previous studies mainly focus on the coscheduling problem for the container terminals rather than workshop. Hence, this study evaluated the coscheduling problem of automated single-beam cranes and AGVs incurred by an actual manufacturing environment. A joint scheduling model was established by considering the constraints of conflict-free, noncrossing, and safety distance as well as the feature of the material. This model considers the process and minimum makespan as the scheduling unit and optimization objective, respectively, including handling time and process time. Subsequently, a hybrid genetic algorithm and particle swarm optimization (HGA-PSO) was developed, and a check-repair procedure was designed to guarantee the feasibility of optimal solution. Finally, numerical illustrative experiments and nonparametric tests were conducted to verify the effectiveness of the proposed model and algorithm, and the results show that the proposed algorithm has better performance than PSO and GA in solving the proposed model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Kulak, O.: A decision support system for fuzzy multi-attribute selection of material handling equipments. Expert Syst. Appl. 29(2), 310–319 (2005). https://doi.org/10.1016/j.eswa.2005.04.004

    Article  Google Scholar 

  2. Yan, R.; Dunnett, S.J.; Jackson, L.M.: Novel methodology for optimising the design, operation and maintenance of a multi-AGV system. Reliab. Eng. Syst. Saf. 178, 130–139 (2018). https://doi.org/10.1016/j.ress.2018.06.003

    Article  Google Scholar 

  3. Heshmati, S.; Toffolo, T.A.M.; Vancroonenburg, W.; Vanden Berghe, G.: Crane-operated warehouses: integrating location assignment and crane scheduling. Comput. Ind. Eng. 129, 274–295 (2019). https://doi.org/10.1016/j.cie.2019.01.039

    Article  Google Scholar 

  4. Heger, J.; Voss, T.: Optimal Scheduling of AGVs in a Reentrant Blocking Job-shop. Procedia CIRP 67, 41–45 (2018). https://doi.org/10.1016/j.procir.2017.12.173

    Article  Google Scholar 

  5. Heger, J.; Voss, T.: Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules. Procedia CIRP 81, 1136–1141 (2019). https://doi.org/10.1016/j.procir.2019.03.281

    Article  Google Scholar 

  6. Nageswararao, M.; Narayanarao, K.; Ranagajanardhana, G.: Simultaneous Scheduling of Machines and AGVs in Flexible Manufacturing System with Minimization of Tardiness Criterion. Procedia Materials Science 5, 1492–1501 (2014). https://doi.org/10.1016/j.mspro.2014.07.336

    Article  Google Scholar 

  7. Miyamoto, T.; Inoue, K.: Local and random searches for dispatch and conflict-free routing problem of capacitated AGV systems. Comput. Ind. Eng. 91, 1–9 (2016). https://doi.org/10.1016/j.cie.2015.10.017

    Article  Google Scholar 

  8. Saidi-Mehrabad, M.; Dehnavi-Arani, S.; Evazabadian, F.; Mahmoodian, V.: An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Comput. Ind. Eng. 86, 2–13 (2015). https://doi.org/10.1016/j.cie.2015.01.003

    Article  Google Scholar 

  9. Fazlollahtabar, H.; Saidi-Mehrabad, M.; Balakrishnan, J.: Mathematical optimization for earliness/tardiness minimization in a multiple automated guided vehicle manufacturing system via integrated heuristic algorithms. Robot. Auton. Syst. 72, 131–138 (2015). https://doi.org/10.1016/j.robot.2015.05.002

    Article  Google Scholar 

  10. Murakami, K.: Time-space network model and MILP formulation of the conflict-free routing problem of a capacitated AGV system. Comput. Ind. Eng. 141 (2020). https://doi.org/10.1016/j.cie.2020.106270

  11. Bilge, Ü.; Tanchoco, J.M.A.: AGV systems with multi-load carriers: basic issues and potential benefits. J. Manu. Syst. 16(3), 159–174 (1997). https://doi.org/10.1016/S0278-6125(97)88885-1

    Article  Google Scholar 

  12. Grunow, M.; Gunther, H.O.; Lehmann, M.: Dispatching multi-load AGVs in highly automated seaport container terminals. OR Spectrum 26(2), 211–235 (2004). https://doi.org/10.1007/s00291-003-0147-1

    Article  MATH  Google Scholar 

  13. Chawla, V.K., Chanda, A.K., Angra, S., Rani, S.: Simultaneous Dispatching and Scheduling of Multi-Load AGVs in FMS-A Simulation Study. Materials Today: Proceedings 5(11, Part 3), 25358-25367 (2018). doi:https://doi.org/10.1016/j.matpr.2018.10.339

  14. Zhang, L.; Hu, Y.; Guan, Y.: Research on hybrid-load AGV dispatching problem for mixed-model automobile assembly line. Procedia CIRP 81, 1059–1064 (2019). https://doi.org/10.1016/j.procir.2019.03.251

    Article  Google Scholar 

  15. Gharehgozli, A.H.; Yu, Y.; de Koster, R.; Udding, J.T.: An exact method for scheduling a yard crane. Eur. J. Oper. Res. 235(2), 431–447 (2014). https://doi.org/10.1016/j.ejor.2013.09.038

    Article  MathSciNet  MATH  Google Scholar 

  16. Liang, C.-J.; Chen, M.; Gen, M.; Jo, J.: A multi-objective genetic algorithm for yard crane scheduling problem with multiple work lines. J. Intell. Manuf. 25(5), 1013–1024 (2013). https://doi.org/10.1007/s10845-013-0792-4

    Article  Google Scholar 

  17. Chu, F.; He, J.; Zheng, F.; Liu, M.: Scheduling multiple yard cranes in two adjacent container blocks with position-dependent processing times. Comput. Ind. Eng. 136, 355–365 (2019). https://doi.org/10.1016/j.cie.2019.07.013

    Article  Google Scholar 

  18. Guo, P.; Wang, L.; Xue, C.; Wang, Y.: Dispatching Rules for Scheduling Twin Automated Gantry Cranes in an Automated Railroad Container Terminal. Arab. J. Sci. Eng. 45(3), 2205–2217 (2020). https://doi.org/10.1007/s13369-019-04176-z

    Article  Google Scholar 

  19. Li, J., Xu, A., Zang, X.: Simulation-based solution for a dynamic multi-crane-scheduling problem in a steelmaking shop. Int. J. Prod. Res., 1-15 (2019). https://doi.org/10.1080/00207543.2019.1687952

  20. Boysen, N.; Stephan, K.: A survey on single crane scheduling in automated storage/retrieval systems. Eur. J. Oper. Res. 254(3), 691–704 (2016). https://doi.org/10.1016/j.ejor.2016.04.008

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhong, M., Yang, Y., Dessouky, Y., Postolache, O.: Multi-AGV scheduling for conflict-free path planning in automated container terminals. Comput. Ind. Eng. 142 (2020). https://doi.org/10.1016/j.cie.2020.106371

  22. Yang, Y.; Zhong, M.; Dessouky, Y.; Postolache, O.: An integrated scheduling method for AGV routing in automated container terminals. Comput. Ind. Eng. 126, 482–493 (2018). https://doi.org/10.1016/j.cie.2018.10.007

    Article  Google Scholar 

  23. Chen, X.; He, S.; Zhang, Y.; Tong, L.; Shang, P.; Zhou, X.: Yard crane and AGV scheduling in automated container terminal: a multi-robot task allocation framework. Transportation Research Part C: Emerging Technologies 114, 241–271 (2020). https://doi.org/10.1016/j.trc.2020.02.012

    Article  Google Scholar 

  24. Kaveshgar, N.; Huynh, N.: Integrated quay crane and yard truck scheduling for unloading inbound containers. Int. J. Product. Econ. 159, 168–177 (2015). https://doi.org/10.1016/j.ijpe.2014.09.028

    Article  Google Scholar 

  25. Zeng, C.; Tang, J.; Yan, C.: Job-shop cell-scheduling problem with inter-cell moves and automated guided vehicles. J. Intell. Manuf. 26(5), 845–859 (2014). https://doi.org/10.1007/s10845-014-0875-x

    Article  Google Scholar 

  26. 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). https://doi.org/10.1016/j.ejor.2013.08.050

    Article  MathSciNet  MATH  Google Scholar 

  27. Lu, H.; Wang, S.: A study on multi-ASC scheduling method of automated container terminals based on graph theory. Comput. Ind. Eng. 129, 404–416 (2019). https://doi.org/10.1016/j.cie.2019.01.050

    Article  Google Scholar 

  28. Liang, C.J.; Li, M.M.; Lu, B.; Gu, T.Y.; Jo, J.; Ding, Y.: Dynamic configuration of QC allocating problem based on multi-objective genetic algorithm. J. Intell. Manuf. 28(3), 847–855 (2017). https://doi.org/10.1007/s10845-015-1035-7

    Article  Google Scholar 

  29. Liu, Z.; Guo, S.; Wang, L.: Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption. J. Clean. Prod. 211, 765–786 (2019). https://doi.org/10.1016/j.jclepro.2018.11.231

    Article  Google Scholar 

  30. Mokhtari, H.; Noroozi, A.: An efficient chaotic based PSO for earliness/tardiness optimization in a batch processing flow shop scheduling problem. J. Intell. Manuf. 29(5), 1063–1081 (2015). https://doi.org/10.1007/s10845-015-1158-x

    Article  Google Scholar 

  31. Thakur, P.; Srivastava, D.C.; Gupta, P.K.: The genetic algorithm: A robust method for stress inversion. J. Struct. Geol. 94, 227–239 (2017). https://doi.org/10.1016/j.jsg.2016.11.015

    Article  Google Scholar 

  32. Lu, P.-H.; Wu, M.-C.; Tan, H.; Peng, Y.-H.; Chen, C.-F.: A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems. J. Intell. Manuf. 29(1), 19–34 (2015). https://doi.org/10.1007/s10845-015-1083-z

    Article  Google Scholar 

  33. García, S.; Molina, D.; Lozano, M.; Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization. J. Heuristics 15(6), 617–644 (2008). https://doi.org/10.1007/s10732-008-9080-4

    Article  MATH  Google Scholar 

Download references

Funding

This research was supported by the Natural Science Foundation of Jiangsu Province, China (No. BK20141517), the Science and Technology Support Program of Jiangsu Province (No.BY2016043-02), and the Suzhou Municipal Science and Technology Bureau (No. SS201704).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zailiang Chen.

Ethics declarations

Conflicts of interest

The authors declared that they have no conflicts of interest in this work.

Appendix A

Appendix A

See Table 9.

Table 9 Details of 20 processes

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Q., Chen, Z. & Yang, H. A Coscheduling Model of Automated Single-Beam Cranes and AGVs in Assembly Workshop. Arab J Sci Eng 46, 2815–2831 (2021). https://doi.org/10.1007/s13369-020-05134-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-020-05134-w

Keywords

Navigation