The demand for maritime transportation has significantly increased over the past 20 years due to the rapid pace of globalization. With the increase in freight transport, terminal managers confront the challenge of establishing the appropriate seaside operations. Although the recent development in smart ports derived from emerging technologies such as the Internet of thing (IoT), machine learning (ML), data mining, etc. could enhance efficiency in port operations. These technologies enabled the implementation of a digital-twining port which allows port managers to simulate and predict the performance of operation schedules in real-time or near real-time. In fact, one of the key technologies behind them is the decision support system (DSS), which is derived from intelligent algorithms to effectively model and optimize port operations. The DSS system, which aims to assist port planners in the port operation planning phase, in practice, this system should be computationally efficient and friendly to use. In this study, we present a decision support system to assist the port planners in berth allocation and quay crane assignment problem (BAP+QCAP), this leads to a complex combinational problem and is known as NP-hard. Therefore, we adopted a metaheuristic approach to solve the problem in a reasonable time, called an elitist strategy genetic algorithm (ESGA). To verify our approach, a comparative study is made on a large random instance. It is found that the ESGA outperformed the well-known metaheuristic algorithms both in terms of solution quality and computational efficiency.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Elitist Strategy Genetic Algorithm-Based Planning Optimization Deriving for Smart Port Decision Support System


    Additional title:

    Lect. Notes on Data Eng. and Comms.Technol.


    Contributors:

    Conference:

    International Conference on Intelligence of Things ; 2024 ; Da Nang, Vietnam September 12, 2024 - September 14, 2024



    Publication date :

    2024-12-24


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Nonlinear predictive controller based on elitist preserved genetic algorithm (EGA)

    Wang, H.-y. / Zhu, J. | British Library Online Contents | 2002


    Using an elitist Pareto genetic algorithm for aerodynamic data extraction

    Anderson, Murray / Lawrence, William / Gebert, Glenn | AIAA | 1996


    Elitist Non-dominated Sorting Genetic Algorithm-Based Heuristic for Optimizing Rail Freight Transportation

    Panicker, Vinay V. / Aryadutt, C. S. / Anoop, K. P. | Springer Verlag | 2018