바로가기메뉴

본문 바로가기 주메뉴 바로가기

ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

logo

Performance Analysis of a Combination of Carry-in and Remarshalling Algorithms

Performance Analysis of a Combination of Carry-in and Remarshalling Algorithms

The Journal of Industrial Distribution & Business(JIDB) / The Journal of Industrial Distribution & Business, (E)2233-5382
2020, v.11 no.10, pp.75-89
https://doi.org/https://doi.org/10.13106/jidb.2020.vol11.no10.75
PARK, Young-Kyu (Department of Social Welfare, Kaya University)
UM, Kyung-Ho (Department of Social Welfare, Kaya University)
  • 다운로드 수
  • 조회수

Abstract

Purpose: The container terminal is an area that plays an important role in the country's import and export. As the volume of containers increased worldwide, competition between terminals became fiercer, and increasing the productivity of terminals became more important. Re-handling is a serious obstacle that lowers the productivity of terminal. There are two ways to reduce re-handling in the terminal yard. The first method is to load containers in terminal yards using effective carry-in algorithms that reduce re-handling. The second method is to carry out effective remarshalling. In this paper, the performance of various carry-in algorithms and various remarshalling algorithms are reviewed. Next, we try to find the most effective combination of carry-in algorithm and remarshalling algorithm. Research design, data and methodology: In this paper, we analyze the performance of the four carry-in algorithms, AP, MDF, LVF, RP and the four remarshalling algorithms, ASI, ASI+, ASO, ASO+. And after making all the combinations of carry-in algorithms and remarshalling algorithms, we compare their performance to find the best combination. To that end, many experiments are conducted with eight types of 100 bays through simulation. Results: The results of experiments showed that AP was effective among the carry-in algorithms and ASO+ was effective among remarshalling algorithms. In the case of the LVF algorithm, the effect of carrying in was bad, but it was found to be effective in finding remarshalling solution. And we could see that ASI+ and ASO+, algorithms that carry out remarshalling even if they fail to find remarshalling solution, are also more effective than ASI and ASO. And among the combinations of carry-in algorithms and remarshalling algorithms, we could see that the combination of AP algorithm and ASO+ algorithm was the most effective combination. Conclusion: We compared the performance of the carry-in algorithms and the remarshalling algorithms and the performance of their combination. Since the performance of the container yard has a significant effect on the performance of the entire container terminal, it is believed that the results of this experiment will be effective in improving the performance of the container terminal when carrying-in or when remarshalling.

keywords
Carry-in, Preprocessing, Rehandling, Remarshalling Algorithm, Terminal Yard

참고문헌

1.

Cha, S. H., & Noh, C. K. (2014). A Study on the Application of Transfer Equipment Pooling Systems for Enhancing Productivity at Container Terminals, Journal of Navigation and Port Research , 38 (4), 399-407

2.

Cha, S. H., & Noh, C. K (2018) A Study on Application of Yard Transportation Equipment Automation System in the Container Terminal, Journal of Navigation and Port Research , 42 (3), 217-226

3.

Chung, C. Y., & Shin, J. Y. (2011) Efficient Yard Operation for the Dual Cycling in Container Terminal, Journal of Navigation and Port Research , 35(1), 71-76.

4.

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

5.

Ha, T. Y., & Choi, T. S. (2004) Analysis of Combined Productivity at Automated Container Terminal Using Simulation, The Korean Operations Research and Management Science Society , 643-646

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

10.

Kim, B. S., Kim, J. H., Fibrianto, H. Y., & Hong, S. D. (2018) A Remarshalling Buffer Location Model in a Rail based Container Terminal, Korean Journal of Logistics , 26 (3), 1-16.

11.

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

12.

Kim, K. H., & Park, Y. M. (1996). A slot assignment method in the container yard for export containers considering their weights, Journal of the Korean I nstitute of Industrial Engineers , 22 (4), 753-770.

13.

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.

14.

Kim, K. H., Won, S. H., Yang, C. H., Kim, Y. H., & Bae, J. U. (2001). Evaluation of Yard Layouts of Automated Container Terminals by Using the Simulation, International Journal of Management Science 1(1), 418-421.

15.

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

16.

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

17.

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

18.

Nilsson, N. J. (1998) Artificial Intelligence: A New Synthesis , San Francisco, USA Mo rgan Kaufmann Publishers, Inc.

19.

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

20.

Park, Y. K. (2016) Remarshalling Plan Using Neighboring Bay in Container Terminal, Journal of Korean Navigation and Port Research , 40(3), 113-120

21.

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

22.

Seo, J. H., Yi, S. H. & Kim, K. H., (2018) A Simulation Study on the Deadlock of a Rail Based Container Transport System, J. Navig. Port Res . 42(1), 47-56

23.

Zhang, C., Liu, J., Wan, Y . W., Murty, K. G., & Linn, R. J. (2003). Storage Space Allocation in Container Terminals, Transportation Research Part B Methodological , 37(10), 883-903

The Journal of Industrial Distribution & Business(JIDB)