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Performance Analysis of Preprocessing Algorithm in Container Terminal and Suggestion for Optimum Selection

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2018, v.16 no.12, pp.95-104
https://doi.org/https://doi.org/10.15722/jds.16.12.201812.95
Park, Young-Kyu
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Abstract

Purpose - In order to gain the upper hand in competition between container terminals, efforts to improve container terminal productivity continue. Export containers arrive randomly in the container terminal and are carried in the container terminal yard according to the arrival order. On the other hand, containers are carried out of the container terminal yard in order based on container weight, not in order of arrival. Because the carry-in order and the carry-out order are different, rehandling may occur, which reduces the performance of the container terminals. In order to reduce rehandling number, containers can be moved in advance when they arrive, which is called preprocessing. This paper proposes an effective preprocessing algorithm and analyzes the factors that affect the productivity of the container terminals. It also provides a way to choose the best factors for preprocessing for a variety of situations. Research design, data, and methodology - To analyze the impact of factors affecting the performance of preprocessing algorithms presented in this paper, simulations are performed. The simulations are performed for two types of bays, 12 stacks with 8 tiers, and 8 stacks with 6 tiers. Results - The results of the factor analysis that affects the performance of the preprocessing algorithm were as follows. (1) As the LMF increased, preprocessing number increases and rehandling number decreased. (2) The LML effect was greatest when the LML changed from 0 to 1, and that the effect decreased when it changed above 1. (3) The sum of preprocessing number and rehandling number was then shown to be increased after decrease, as the LMF increased. (4) In the case of NCI, a decrease in NCI showed that the containers would become more grouped and thus the performance was improved. (5) There was a positive effect in the case of EFS. Conclusion - In this paper, preprocessing algorithm was proposed and it was possible to choose the best factors for preprocessing for a variety of situations through simulations. Further research related to this study needs to be carried out in the following topic : a study on the improvement of container performance by connecting the preprocessing with remarshalling.

keywords
Preprocessing Algorithm, Rehandling, Remarshalling, Performance Factor, Container Terminal Yard

Reference

1.

Bae, J. W., Park, Y. M., & Kim, K. H. (2006). Assignment and Operation Sequencing for Remarshalling of a Vertical Yard Block in Automated Container Terminals. Journal of Korean Navigation and Port Research, 30(6), 457-464.

2.

Caserta, M., Voβ, S., & Sniedovich, M. (2011). Applying the Corridor Method to a Block Relocation Problem. OR Spectrum, 33, 915-929.

3.

Ha, B. H., & Kim, S. S. (2012). A* Algorithm for Optimal Intra-bay Container Pre-marshalling Plan, Journal of the Korean Institute of Industrial Engineers, 38(2), 157-172.

4.

Han, X., Lu, Z., & Xi, L. (2010). A Proactive Approach for Simultaneous Berth and Quay Crane Scheduling Problem with Stochastic Handling Time. European Journal of Operational Research, 207(3), 1327-1340.

5.

Hirashima, Y., Ishikawa, N., & Takeda, K. (2006). A New Reinforcement Learning for Group-Based Marshalling Plan considering Desired Layout of Containers in Port Terminals. Proc. IEEE Conf. Networking, Sensing and control, 670-675.

6.

Imai, A., Sasaki, K., Nishimura, E., & Papadimitriou, S. (2006). Multi objective Simultaneous Stowage and Load Planning for a Container Ship with Container Rehandle in Yard Stacks. European Journal of Operational Research, 171(2), 373-389.

7.

Jeong, Y. H., Kim, K. H., Woo, Y. J., & Seo, B. H. (2012). A Simulation Study on a Workload based Operation Planning Method in Container Terminal. Industrial Engineering and Management Systems, 11(1), 103-113.

8.

Kang, J. H., Oh, M. S., Ru, K. R., & Kim, K. H. (2004). Method of Inbound Container Positioning for Minimal Rehandling Considering Weight. Proceedings of The 2004 KIISS Fall Conference, 271-278.

9.

Kang, J. H., Oh, M. S., Ru, K. R., & Kim, K. H. (2005). Sequencing Container Moves for Intra-Block Remarshalling in a Container Terminal Yard. Journal of the Korean Institute of Industrial Engineers, 29(1), 83-90

10.

Kang, J., Ryu, K. R., & Kim, K. H. (2006). Deriving Stacking Strategies for Export Containers with Uncertain Weight Information. Journal of Intelligent Manufacturing, 17, 399-410.

11.

Kim, J. E., Park, K. Y., Park, T. J., & Ryu, K. R. (2009). Container Selecting Methods for Remarshalling Considering Restricted Idle Time of Crane in an Automated Container Terminal. Journal of Korean Navigation and Port Research, 33(10), 715-722.

12.

Kim, K. H. (2007). Decision making Problems for the Operation of Container Terminal. Journal of the Korean Institute of Industrial Engineers, 33(3), 290-302.

13.

Kim, K. H., Kim, K. Y., & Ko, C. S. (1997). Load Scheduling Using a Genetic Algorithm in Port Container Terminals. Journal of the Korean Institute of Industrial Engineers, 23(4), 645-660.

14.

Kim, K. H., Park, Y. M., & Ryu, K.-R. (2000). Deriving Decision Rules to Locate Export Containers in Container Yards. European Journal of Operational Research, 124, 89-101.

15.

Kim, K. H., & Hong, G. P. (2006). A Heuristic Rule for Relocating Blocks. Computers and Operations Research, 33, 940-954.

16.

Kim, K. H., & Park, K. T. (2003). A Note on a Dynamic Space-allocation Method for Outbound Containers. European Journal of Operational Research, 148, 92-101.

17.

Kim T. K., Yang. Y. J., Bae. A. K., & Ryu, K. R. (2014). Optimization of Dispatching Strategies for Stacking Cranes Including Remarshalling Jobs. Journal of Korean Navigation and Port Research, 38(2), 155-162.

18.

Lee, S. W. (2011). A Genetic Algorithm for the Container Pick-Up Problem. IE Interface, 24(4), 362-372.

19.

Lee, Y., & Lee Y.-J. (2010). A Heuristic for Retrieving Containers from a Yard. Computers and Operations Research, 37, 1139-1147.

20.

Mohammad B. A., Nima Safaei, B., Nikbakhsh, J. (2009). A genetic algorithm to solve the storage space allocation problem in a container terminal. Computers & Industrial Engineering, 56(1), 44-52.

21.

Nilsson, N. J. (1998). Artificial Intelligence: A New Synthesis (pp. 142-160). San Francisco, CA: Morgan Kaufmann Publishers, Inc.

22.

Oh, M. S., Kwang, J. H., Yu, K. R., & Kim, K. H. (2005). A Heuristic Approach to Scheduling Multiple Cranes for Intra-Block Remarshalling. Journal of Korean Navigation and Port Research, 29(5), 447-455.

23.

Park, K. Y., Park, T. J., & Ryu, K. R. (2010). Iterative Container Reselection Methods for Remarshaling in a Container Terminal. Journal of Korean Navigation and Port Research, 34(6), 503-509.

24.

Park, Y. K., & Kwak, K. S. (2011). Export container preprocessing method to decrease the number of rehandling in container terminal. Journal of Korean Navigation and Port Research, 35(1), 77-82.

25.

Park, Y. K. (2016). Comparison fo Algorithm based on the Container Remarshalling Efficiency Factor in Port Distribution. Journal of Distribution Science, 14(5), 107-114.

26.

Wan, Y. W., Liu, J., & Tsai, P. C. (2009). The Assignment of Storage Locations to Containers for a Container Stack. Naval Research Logistics, 56, 699-713.

27.

Zhang, C., Liu, J., Wan, Y. W., Murty, K. G., & Linn, R. J. (2003). Storage Space-Allocation in Container Terminals. Transportation Research Part B, 37, 883-903.

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