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Discrete-event systems modeling and model predictive allocation algorithm for integrated berth and quay crane allocation

Tri Cahyono, R., Flonk, E. & Jayawardhana, B., 2019, In : Ieee transactions on intelligent transportation systems. p. 1-11

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  • BCAP_paper. 20190312

    Submitted manuscript, 585 KB, PDF document

  • Discrete-event systems modeling and model predictive allocationalgorithm for integrated berth and quay crane allocation

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  • Discrete-Event Systems Modeling and the ModelPredictive Allocation Algorithm for IntegratedBerth and Quay Crane Allocation

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  • Supplemental material: Discrete-event systems modeling and model predictive allocation algorithm

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DOI

In this paper, we study the problem of integrated berth and quay crane allocation (I-BCAP) in general seaport container terminals and propose model predictive allocation (MPA) algorithm and preconditioning methods for solving I-BCAP. Firstly, we propose a dynamical modeling framework based on discrete-event systems (DES) that describes the operation of berthing process with multiple discrete berthing positions and multiple quay cranes. Secondly, based on the discrete-event model, we propose a MPA algorithm for solving I-BCAP using model predictive control (MPC) principle with a rolling event horizon. The validation and performance evaluation of the proposed modeling framework and allocation method are done using: (i). extensive Monte-Carlo simulations with realistically-generated datasets; (ii). real dataset from a container terminal in Tanjung Priuk port, located in Jakarta, Indonesia; and (iii). real life field experiment at the aforementioned container terminal. The numerical simulation results show that our proposed MPA algorithm can improve the efficiency of the process where the total handling and waiting cost is reduced by approximately 6 - 9% in comparison to the commonly adapted method of first-come first-served (FCFS) (for the berthing process) combined with the density-based quay cranes allocation (DBQA) strategy. Moreover, the proposed method outperforms the state-of-the-art HPSO-based and GA-based method proposed in recent literature. The real life field experiment shows an improvement of about 6% in comparison to the existing allocation method used in the terminal.
Original languageEnglish
Pages (from-to)1-11
JournalIeee transactions on intelligent transportation systems
Publication statusPublished - 2019

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