Constraint programming models for integrated container terminal operations

Kızılay, Damla; Hentenryck, Pascal Van; Eliiyi, Deniz Türsel

doi:10.1016/j.ejor.2020.04.025

Cited by 45 publications

(12 citation statements)

References 22 publications

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“…We would like to compare the solutions from the dynamical modeling approach in this research with the static approach as presented in these two benchmark methods. These two papers use heuristic methods to find the solution and similar recent method can also be found in [18]. In [16], LP problems are defined for determining the allocation and scheduling of QC, YC, and IT.…”

Section: A Simulation Set-upmentioning

confidence: 99%

Simultaneous Allocation and Scheduling of Quay Cranes, Yard Cranes, and Trucks in Dynamical Integrated Container Terminal Operations

Cahyono

Kenaka

Jayawardhana

2022

IEEE Trans. Intell. Transport. Syst.

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We present a dynamical modeling of integrated (end-to-end) container terminal operations using finite state machine (FSM) framework where each state machine is represented by a discrete-event system (DES) formulation. The hybrid model incorporates the operations of quay cranes (QC), internal trucks (IT), and yard cranes (YC) and also the selection of storage positions in container yard (CY) and vessel bays. The QC and YC are connected by the IT in our models. As opposed to the commonly adapted modeling in container terminal operations, in which the entire information/inputs to the systems are known for a defined planning horizon, in this research we use realtime trucks, crane, and container storage operations information, which are always updated as the time evolves. The dynamical model shows that the predicted state variables closely follow the actual field data from a container terminal in Tanjung Priuk, Jakarta, Indonesia. Subsequently, using the integrated container terminal hybrid model, we proposed a model predictive algorithm (MPA) to obtain the near-optimal solution of the integrated terminal operations problem, namely the simultaneous allocation and scheduling of QC, IT, and YC, as well as selecting the storage location for the inbound and outbound containers in the CY and vessel. The numerical experiment based on the extensive Monte Carlo simulation and real dataset show that the MPA outperforms by 3-6% both of the policies currently implemented by the terminal operator and the state-of-the-art method from the current literature.

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Section: A Simulation Set-upmentioning

confidence: 99%

Simultaneous Allocation and Scheduling of Quay Cranes, Yard Cranes, and Trucks in Dynamical Integrated Container Terminal Operations

Cahyono

Kenaka

Jayawardhana

2022

IEEE Trans. Intell. Transport. Syst.

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“…While the objective for the berth allocation is to minimize the deviation from the vessel expected turnaround time window, the objective for the yard space allocation is to minimize the container internal transportation time. Kizilay et al (2020) develop multiple constraint programming models to optimize the integrated equipment assignment and scheduling problem at the seaside. The objective is to minimize the turnover times of the vessels and maximize terminal throughput.…”

Section: Integrated Operationsmentioning

confidence: 99%

Modeling landside container terminal queues: Exact analysis and approximations

Roy

Ommeren

Koster

et al. 2022

Transportation Research Part B: Methodological

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“…From the above definition, it is concluded that the computational logistics is significantly different from the traditional methods for CLS that cover mathematic programming, system simulation, intelligent optimization, and simulation-based optimization [13][14][15]. e theoretical origins of computational logistics mainly include computational thinking, computational lens, theory of computation, and great principles of computing [16], which are collectively referred to as 4CTTLP.…”

Section: Computational Logistics and Practical Philosophymentioning

confidence: 99%

Computational Logistics for Container Terminal Handling Systems with Deep Learning

2021

Computational Intelligence and Neuroscience

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Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonlinearity, coupling, and complexity. Given that, a combination of computational logistics and deep learning, which is just about container terminal-oriented neural-physical fusion computation (CTO-NPFC), is proposed to discuss and explore the pattern recognition and regression analysis of CTHS. Because the liner berthing time (LBT) is the central index of terminal logistics service and carbon efficiency conditions and it is also the important foundation and guidance to task scheduling and resource allocation in CTHS, a deep learning model core computing architecture (DLM-CCA) for LBT prediction is presented to practice CTO-NPFC. Based on the quayside running data for the past five years at a typical container terminal in China, the deep neural networks model of the DLM-CCA is designed, implemented, executed, and evaluated with TensorFlow 2.3 and the specific feature extraction package of tsfresh. The DLM-CCA shows agile, efficient, flexible, and excellent forecasting performances for LBT with the low consuming costs on a common hardware platform. It interprets and demonstrates the feasibility and credibility of the philosophy, paradigm, architecture, and algorithm of CTO-NPFC preliminarily.

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Constraint programming models for integrated container terminal operations

Cited by 45 publications

References 22 publications

Simultaneous Allocation and Scheduling of Quay Cranes, Yard Cranes, and Trucks in Dynamical Integrated Container Terminal Operations

Simultaneous Allocation and Scheduling of Quay Cranes, Yard Cranes, and Trucks in Dynamical Integrated Container Terminal Operations

Modeling landside container terminal queues: Exact analysis and approximations

Computational Logistics for Container Terminal Handling Systems with Deep Learning

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