Categorized stacking models for import containers in port container terminals

Shih-Chan Ting, Jaw-Shen Wang, Sheng-Long Kao and Flor Melina Pitty
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Shih-Chan Ting: Department of Transportation and Navigation Science, National Taiwan Ocean University, No 2, Pei-Ning Road, Keelung, Taiwan
Jaw-Shen Wang: Yang Ming Marine Transport Corp., No.217, Ming-Der 1st Road, Keelung, Taiwan
Sheng-Long Kao: Department of Transportation and Navigation Science, National Taiwan Ocean University, No 2, Pei-Ning Road, Keelung, Taiwan
Flor Melina Pitty: Panama Maritime Authority, Diablo Heights, 5534 Building, Panama City, Republic Of Panama

Maritime Economics & Logistics, 2010, vol. 12, issue 2, 162-177

Abstract: Container terminals have been playing an important role in global transportation and serve as multi-modal interfaces between sea and land transportation. Import containers are those containers that arrive at container terminals from overseas and transship to their destination through inland transport, feeders or wait to be picked up by consignees. As trucks assigned by consignees arrive more or less randomly to pick up specific containers, there is uncertainty about which container will be picked up first, before another one. This uncertainty may result in a great number of unproductive moves. The purpose of this article is to provide terminal operators with a good solution to improve yard crane operation efficiency in container terminals. A booking system for picking up import containers is designed to collect information in advance and to reduce the uncertainty of container pick-up time. The article also analyzes the historical data provided by one container terminal operator in the Port of Kaohsiung, in order to obtain several critical attributes and categorize containers into several groups, according to the predicted pick-up time priority. The results from the application of the booking system and proposed model to categorize import containers into three stacking priorities, integrated with advanced booking time, show that the models are effective and applicable.

Date: 2010
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