Please use this identifier to cite or link to this item: https://doi.org/10.3390/app11199156
Title: Resource capacity requirement for multi?terminal cooperation in container ports
Authors: Lee, Byung Kwon 
Low, Joyce M. W.
Keywords: Capacity requirement planning
Multiterminal operations
Resource profiles
Transshipment hub
Workload distributions
Issue Date: 1-Oct-2021
Publisher: MDPI
Citation: Lee, Byung Kwon, Low, Joyce M. W. (2021-10-01). Resource capacity requirement for multi?terminal cooperation in container ports. Applied Sciences (Switzerland) 11 (19) : 9156. ScholarBank@NUS Repository. https://doi.org/10.3390/app11199156
Rights: Attribution 4.0 International
Abstract: Capacity sharing among neighboring terminals offer a means to meet increasing or unexpected demand for cargo?handling without additional capital investment. This study proposes a model for capacity requirement planning of major resources, such as quay cranes (QCs), storage space, and gate, in multiterminal port operations where demand is time dependent. A resource profile simulation is run to generate random events across the terminals and estimate the capacity requirement in the form of workload distributions on port resources over time?shifts. The effects on workload requirement, arising from multiterminal cooperation, are subsequently evaluated in consideration of different container flows among terminals. Experimental results suggest that higher transferring rate between terminals will reduce the QC intensity and storage space requirements but increase gate congestion. Variabilities in the QC intensity and storage space requirements also increase due to shorter stays and more movements in container inventory at the yard. The interaction effect between transferring and trans?shipment rates further shows that the average resource requirements for a terminal can be greatly reduced when the interterminal transferring of containers contributes positively to a more even workload redistribution across terminals. The most significant improvements occur when trans?shipment rate is 85% and transferring rate is 75% for QC intensity; trans?shipment rate is 90% and transferring rate is 60% for storage capacity; and trans?shipment rate is 80% and transferring rate is 75% for gate congestion. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: Applied Sciences (Switzerland)
URI: https://scholarbank.nus.edu.sg/handle/10635/233639
ISSN: 2076-3417
DOI: 10.3390/app11199156
Rights: Attribution 4.0 International
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