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SCOPUS 학술저널

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

컨테이너 터미널의 선처리 알고리즘 성능분석과 최적선택 제안

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

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2. 이론적 고찰

3. 연구방법론

4. 연구 결과

5. 연구결과 토론 및 시사점

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