Optimal scheduling of emergency resources for major maritime oil spills considering time-varying demand and transportation networks

Zhang, Lingye; Lu, Jing; Yang, Zaili

doi:10.1016/j.ejor.2020.12.040

Cited by 21 publications

(7 citation statements)

References 49 publications

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“…Many different approaches have been developed to address maritime transportation safety and emergency management problems. Recently, new methods that have appeared in maritime safety and emergency management research include STPA, cognitive reliability error analysis method (CREAM), DBN, emergency assessment-based simulation [88], probabilistic risk assessment-based simulation [89], resilience assessment-based simulation [90], and mathematical modeling and optimization methods, such as non-linear optimization and enhanced particle swarm optimization (EPSO) models [91], multi-objective particle swarm algorithm [47], and dynamic multi-objective optimization model [92]. At present, machine learning is introduced to improve maritime safety and management.…”

Section: Overview Of the Research Methodsmentioning

confidence: 99%

“…According to the query used in the WOS database and collected dataset, Lv Jing and Fu Shanshan had a four-year gap between their articles related to maritime safety and emergency management. Meanwhile, we found that these core authors frequently used the BN and CN methods to study maritime transportation safety issues [43][44][45][46] and mainly focused on oil spill problems [47]. In addition, a few authors were instrumental in studying maritime resilience and vulnerability assessment [48,49].…”

Section: Influential Scholar Analysismentioning

confidence: 99%

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A Systematic Literature Review of Maritime Transportation Safety Management

Xu,

Ma,

Zhao

et al. 2023

JMSE

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Maritime transportation plays a critical role in global trade, and studies on maritime transportation safety management are of great significance to the sustainable development of the maritime industry. Consequently, there has been an increasing trend recently in studies on maritime transportation safety management, especially in terms of safety risk analysis and emergency management. Therefore, the general idea of this article is to provide a detailed literature review of maritime transportation safety management based on 186 articles in the Web of Science (WOS) database published from 2011 to 2022. The purposes of this article are as follows: (1) to provide a statistics-based description and conduct a network-based bibliometric analysis on the basis of the collected articles; (2) to summarize the methodologies/technologies employed in maritime transportation safety management spatiotemporally; and (3) to propose four potential research perspectives in terms of maritime transportation safety management. Based on the findings and insights obtained from the bibliometric and systematic review, the development of a resilient maritime transportation system could be facilitated by means of data- or intelligence-driven technologies, such as scenario representation, digital twinning, and data simulation. In addition, the issues facing intelligent maritime shipping greatly challenge the current maritime safety management system due to the co-existence of intelligent and non-intelligent maritime operation.

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Section: Overview Of the Research Methodsmentioning

confidence: 99%

Section: Influential Scholar Analysismentioning

confidence: 99%

A Systematic Literature Review of Maritime Transportation Safety Management

Xu,

Ma,

Zhao

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…The selection operation adopts an elite retention strategy, whereby the population individuals are sorted in descending order according to the size of their fitness as well as numbered, the 2% of the individuals with the highest fitness are selected as the elite individuals, and the indexes of the 2% of the individuals with the highest fitness are extracted and saved, In subsequent genetic algorithm operations, these elite individuals can be indexed to ensure that better parent genes are passed on to the next generation, thus facilitating algorithm convergence and improved optimization results [9] .…”

Section: Improved Selection Operationsmentioning

confidence: 99%

Research on energy storage charging piles based on improved genetic algorithm

Sun

2024

J. Phys.: Conf. Ser.

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Aiming at the charging demand of electric vehicles, an improved genetic algorithm is proposed to optimize the energy storage charging piles optimization scheme. Firstly, the characteristics of electric load are analyzed, the model of energy storage charging piles is established, the charging volume, power and charging/discharging timing constraints in the charging process are considered, and the optimization is carried out by the improved genetic algorithm, and the profit of charging piles and user charging electricity charges are calculated, so as to obtain the possible optimal parameters, as well as the impact of the energy storage system on the stability of the power grid. Secondly, the analysis of the results shows that the energy storage charging piles can not only improve the profit to reduce the user’s electricity cost, but also reduce the impact of electric vehicle charging on the power grid load.

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“…In training of depth convolution neural network, the problems of network deepening, difficult training, and slow convergence arise [27]. BN and GN are shown in Figure 6.…”

Section: Normalization Adjustmentmentioning

confidence: 99%

A Dynamic Multitarget Detection Algorithm in front of Vehicle Based on Embedded System and Internet of Things

Dou

Wang

2022

Scientific Programming

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There are few studies for the classification detection and dynamic multitarget detection of the targets in front of vehicles. In order to solve this problem, a dynamic multitarget detection algorithm is proposed. First, a dynamic multitarget detection with displacement at any time is suggested; secondly, a multitarget detection algorithm based on improved You Only Look Once version 3 (YOLOv3) is proposed for the detection of multitarget high probability risk events in front of the vehicle. The YOLOv3 algorithm model is a lightweight backbone network that uses embedded real-time detection technologies. In this paper, we use a lightweight Mobilenetv2 to replace Darknet-53 for feature extraction. Moreover, an optimizer is used for multiobjective feature extraction, group normalization, and multiobjective feature extraction. The results show that in comparison with the original YOLOv3 algorithm, the detection leakage rate of the improved YOLOv3 multitarget detection algorithm is less than 5%, and the amount of model parameters in this paper is reduced by 95% as compared to the traditional data and CPU intertime is reduced to 78%.

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Optimal scheduling of emergency resources for major maritime oil spills considering time-varying demand and transportation networks

Cited by 21 publications

References 49 publications

A Systematic Literature Review of Maritime Transportation Safety Management

A Systematic Literature Review of Maritime Transportation Safety Management

Research on energy storage charging piles based on improved genetic algorithm

A Dynamic Multitarget Detection Algorithm in front of Vehicle Based on Embedded System and Internet of Things

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