Advertisement

OCBLA: A Storage Space Allocation Method for Outbound Containers

  • Xinyu ChenEmail author
  • Wenbin HuEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 41)

Abstract

In container yard, the way of allocating space for outbound containers determines the transport efficiency of containers and the management cost. The existing space allocation methods usually have problems such as low computational efficiency, large number of shifts etc. This paper proposes an outbound containers’ block-location allocation (OCBLA) method to solve this problem, it has two steps, including allocating a block for containers which arrive at the same time and transported by the same vessel and allocating the specific location for each container. The first step aims to balance the load among blocks and reduce the cost of transportation, and last step uses ITO algorithm to reduce the number of shifts. The ITO algorithm regards every optional place as a moving particle and uses drift operator to control particle moving in the direction of the optimal place, wave operator to control particle exploring around. Having a tendency to explore makes the algorithm to find the optimal result in a limited number of tests, thus improving the computational efficiency. Through experiments it can be seen that this method can reduce the number of shifts during the process of container stacking, and get better results while having good real-time performance.

Keywords

OCBLA ITO algorithm Shifts Container stacking 

Notes

Acknowledgement

This work was supported in part by the Key Projects of Guangdong Natural Science Foundation (No. 2018B030311003).

References

  1. 1.
    Exposito-Izquierdo, C., Melian-Batista, B., Moreno-Vega, M.: Pre-marshalling problem: heuristic solution method and instances generator. Expert Syst. Appl. 39(9), 8337–8349 (2012)CrossRefGoogle Scholar
  2. 2.
    Lee, D.H., Cao, J.X., Shi, Q., et al.: A heuristic algorithm for yard truck scheduling and storage allocation problems. Transp. Res. Part E Logistics Transp. Rev. 45(5), 810–820 (2009)CrossRefGoogle Scholar
  3. 3.
    Dekker, R., Voogd, P.: Container Terminals and Cargo Systems, pp. 131–154. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Hu, W., Wang, H., Min, Z.: A storage allocation algorithm for outbound containers based on the outer-inner cellular automaton. Inf. Sci. 281, 147–171 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Chang, D., Jiang, Z., Yan, W., et al.: Developing a dynamic rolling-horizon decision strategy for yard crane scheduling. Adv. Eng. Inform. 25(3), 485–494 (2011)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Boysen, N.: Determining crane areas in intermodal transshipment yards: the yard partition problem. Eur. J. Oper. Res. 204(2), 336–342 (2010)CrossRefGoogle Scholar
  9. 9.
    Peng, G., Cheng, W., Yi, W., et al.: Gantry crane scheduling in intermodal rail-road container terminals. Int. J. Prod. Res. 1–18 (2018)Google Scholar
  10. 10.
    Peng, G., Cheng, W., Zhang, Z., et al.: Gantry crane scheduling with interference constraints in railway container terminals. Int. J. Comput. Intell. Syst. 6(2), 244–260 (2013)CrossRefGoogle Scholar
  11. 11.
    Jiang, H.C.: A note on: a flexible crane scheduling methodology for container terminals. Flex. Serv. Manufact. J. 1–7 (2018)Google Scholar
  12. 12.
    Chu, Y., Zhang, X., Yang, Z.: Multiple quay cranes scheduling for double cycling in container terminals. PLoS ONE 12(7), e0180370 (2017)CrossRefGoogle Scholar
  13. 13.
    Zheng, H., Liu, B., Dong, Y., et al.: Multi-yard cranes scheduling optimization of inbound full container block with timely relocation. Syst. Eng. Theory Pract. 37(10), 2700–2714 (2017)Google Scholar
  14. 14.
    Li, Y., Zhu, X., Li, W., et al.: Stowage plan based slot optimal allocation in rail-water container terminal. J. Control Sci. Eng. 2, 1–10 (2017)zbMATHGoogle Scholar
  15. 15.
    Wu, Y., Luo, J., Zhang, D., et al.: An integrated programming model for storage management and vehicle scheduling at container terminals. Res. Transp. Econ. 42(1), 13–27 (2013)CrossRefGoogle Scholar
  16. 16.
    Lee, B.K., Kim, K.H.: Optimizing the block size in container yards. Transp. Res. Part E Logistics Transp. Rev. 46(1), 120–135 (2010)CrossRefGoogle Scholar
  17. 17.
    Yu, M., Qi, X.: Storage space allocation models for inbound containers in an automatic container terminal. Eur. J. Oper. Res. 226(1), 32–45 (2013)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Bazzazi, M., Safaei, N., Javadian, N.: A genetic algorithm to solve the storage space allocation problem in a container terminal. Comput. Ind. Eng. 56(1), 44–52 (2009)CrossRefGoogle Scholar
  19. 19.
    Sharif, O., Huynh, N.: Storage space allocation at marine container terminals using ant-based control. Expert Syst. Appl. 40(6), 2323–2330 (2013)CrossRefGoogle Scholar
  20. 20.
    Dong, W., Zhang, W., Yu, R.: Convergence and runtime analysis of ITO algorithm for one class of combinatorial optimization. Chin. J. Comput. 34(4), 636–646 (2011)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer ScienceWuhan UniversityWuhanChina
  2. 2.Shenzhen Research InstituteWuhan UniversityWuhanChina

Personalised recommendations