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Approximate queueing models for analyzing harbor terminal operations

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  • Easa, Said M.

Abstract

The purpose of this paper is to present approximate queueing models to help assess the impacts of tug services on congested harbor terminals. The models are intended for harbors in which tug shortages are rare. A congested harbor terminal is modelled as a queueing system with m identical tugs (servers) and n identical berths (customers), and with general probability distributions of tug service time and berth cargo-handling time. The distribution of the number of berths in the system, a basic element to analyze the system performance, was established for two cases. For large m, the distribution was approximated by a binomial model and the respective accuracy tested. For small m, an approximate model for this distribution was developed. Particular emphasis was given to developing explicitly the probability of having one berth in queue and establishing the remaining probabilities of the distribution approximately. The model for small m was validated by means of simulation for various cases of harbor terminal operations exhibiting different ranges of the coefficient of variation of tug service time. The models were found to be reasonably accurate within a certain range covering situations in which tug shortages are in the order of 10% of the time or less.

Suggested Citation

  • Easa, Said M., 1987. "Approximate queueing models for analyzing harbor terminal operations," Transportation Research Part B: Methodological, Elsevier, vol. 21(4), pages 269-286, August.
    • Handle: RePEc:eee:transb:v:21:y:1987:i:4:p:269-286
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    Cited by:

    1. Vibhuti Dhingra & Debjit Roy & René B. M. Koster, 2017. "A cooperative quay crane-based stochastic model to estimate vessel handling time," Flexible Services and Manufacturing Journal, Springer, vol. 29(1), pages 97-124, March.
    2. Jia, Shuai & Li, Chung-Lun & Xu, Zhou, 2020. "A simulation optimization method for deep-sea vessel berth planning and feeder arrival scheduling at a container port," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 174-196.
    3. Milorad Vidovic & Kap Kim, 2006. "Estimating the cycle time of three-stage material handling systems," Annals of Operations Research, Springer, vol. 144(1), pages 181-200, April.
    4. Roy, D. & de Koster, M.B.M., 2014. "Modeling and Design of Container Terminal Operations," ERIM Report Series Research in Management ERS-2014-008-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Debjit Roy & René De Koster & René Bekker, 2020. "Modeling and Design of Container Terminal Operations," Operations Research, INFORMS, vol. 68(3), pages 686-715, May.
    6. Branislav Dragovic & Nenad Dj. Zrnic, 2011. "A Queuing Model Study of Port Performance Evolution," Analele Universitatii "Eftimie Murgu" Resita Fascicola de Inginerie, "Eftimie Murgu" University of Resita, vol. 2(XVIII), pages 65-76, December.
    7. Ebru K. Bish & Thin‐Yin Leong & Chung‐Lun Li & Jonathan W. C. Ng & David Simchi‐Levi, 2001. "Analysis of a new vehicle scheduling and location problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 48(5), pages 363-385, August.

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