A simulation-based multi-agent particle swarm optimization approach for supporting dynamic decision making in marine oil spill responses

Ye, Xudong; Chen, Bing; Li, Pu; Jing, Liang; Zeng, Ganning

doi:10.1016/j.ocecoaman.2019.02.003

Cited by 57 publications

(21 citation statements)

References 42 publications

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“…Many existing models usually separately consider response operations and spilled oil's transport and weathering processes, but they have significant interactions [68]. Some novel decision support systems are developed to provide more comprehensive support for oil spill response [68,73,74]. For instance, Ye et al [73] developed a simulation-based multi-agent particle swarm optimization (SA-PSO) approach that integrates oil transport and weathering simulation, cleanup, recovery response simulation, and optimization approaches (as shown in Figure 3).…”

Section: Oil Spill Modeling and Decision Support Approachmentioning

confidence: 99%

“…Some novel decision support systems are developed to provide more comprehensive support for oil spill response [68,73,74]. For instance, Ye et al [73] developed a simulation-based multi-agent particle swarm optimization (SA-PSO) approach that integrates oil transport and weathering simulation, cleanup, recovery response simulation, and optimization approaches (as shown in Figure 3). Agent-based modeling (ABM) is first applied for the oil spill response operation and oil weathering simulation, which comprises multiple types of agents and given specific rules for behavior simulation.…”

Section: Oil Spill Modeling and Decision Support Approachmentioning

confidence: 99%

“…For over two decades, the oil industry mainly focused on mitigation plans regarding greenhouse gas emissions reductions instead of the guidelines for avoiding the damage and losses to the oil sector caused by climate change [73][74][75]. Katopodis and Sfetsos [8] summarized the most recognized prevention and mitigation measures for climate change damage and losses.…”

Section: Oil Spill Modeling and Decision Support Approachmentioning

confidence: 99%

See 2 more Smart Citations

Climate Change Impacts on Coastal and Offshore Petroleum Infrastructure and the Associated Oil Spill Risk: A Review

Dong

Asif

Shi

et al. 2022

JMSE

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Climate change has been observed worldwide in recent decades, posing challenges to the coastal and offshore oil and gas infrastructure. It is crucial to identify how climate change affects these infrastructures and the associated oil spill risk. This paper provides an analysis of the vulnerability of coastal and offshore oil and gas infrastructure in response to climate change. The paper examines oil spill incidents worldwide and addresses climate change’s possible influences on oil spill risk. Moreover, available oil spill modeling and decision support tools for oil spill response are reviewed considering climate change. The paper signals the need for emerging decision and modeling tools considering climate change effects, which can help decision-makers to evaluate the risk on time and provide early warnings to adapt or prevent the unforeseen impacts on the oil industry partially resulting from global warming, including oil spill accidents.

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Section: Oil Spill Modeling and Decision Support Approachmentioning

confidence: 99%

Section: Oil Spill Modeling and Decision Support Approachmentioning

confidence: 99%

Section: Oil Spill Modeling and Decision Support Approachmentioning

confidence: 99%

See 1 more Smart Citation

Climate Change Impacts on Coastal and Offshore Petroleum Infrastructure and the Associated Oil Spill Risk: A Review

Dong

Asif

Shi

et al. 2022

JMSE

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“…Due to the dependence and high consumption of this fuel, problems such as global warming, increased pollution of the oceans caused by constant oil spills (ASHOK et al, 2019;AMIR-HEIDARI et al, 2019;YE et al ., 2019) and even air pollution in large cities, because the burning of oil products through their combustion in automotive engines, bring up discussions about solutions to reduce their use (SANTOS;SILVA, 2016).…”

Section: Introductionmentioning

confidence: 99%

Computational simulation and evaluation of the use of cooking oil for biodiesel generation in municipalities of southern Brazil

Rodrigues

Simonetto

Barros

et al. 2021

Exacta

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“…Based on PSO algorithm, Liu et al [14], developed the continuousdiscrete PSO (CDPSO) algorithm to generate multi-agent shape formation, and illustrated the application of the algorithm in conjunction with the MH370 search and rescue process. Ye et al [15], provided a simulation-based multi-agent PSO (SA-PSO) algorithm, which supported marine oil spill decision-making by integrated simulation and optimization of response device allocation and process control. Sánchez-García et al [16], proposed a distributed and dynamic PSO algorithm for UAV networks (dPSO-U), which took the victims movements and the communication among UAVs into consideration, to generate trajectories for drone formations in large-scale disaster scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Particle Swarm Optimization Algorithm Based on Time-Space Weight for Helicopter Maritime Search and Rescue Decision-Making

et al. 2020

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One of the important problems to be solved in maritime search and rescue (MSAR) is decisionmaking, and the premise of it is determining the mission area for search and rescue unit. To solve the problem that classical cellular iterative search (CIS) algorithm is easy to fall into local optimal solution when determining the mission area, the particle swarm optimization algorithm based on time-space weight (TS-PSO) is proposed in this paper. This algorithm summarizes the optimization objectives and constraint conditions of the MSAR mission area planning according to search theory, carries out the parametric modeling of mission area legitimately and obtains the global optimal solution by continuous exploration in the parameter definition domain. On this basis, by analyzing the time-space weight of drift prediction data, the optimization results are further improved. Finally, through the case simulation analysis, it can be seen that the TS-PSO algorithm can effectively make up for the deficiency of the CIS algorithm and further improve the success probability of optimal MSAR mission area.INDEX TERMS Global optimal solution, maritime search and rescue, mission area planning, particle swarm optimization algorithm, time-space weight.

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A simulation-based multi-agent particle swarm optimization approach for supporting dynamic decision making in marine oil spill responses

Cited by 57 publications

References 42 publications

Climate Change Impacts on Coastal and Offshore Petroleum Infrastructure and the Associated Oil Spill Risk: A Review

Climate Change Impacts on Coastal and Offshore Petroleum Infrastructure and the Associated Oil Spill Risk: A Review

Computational simulation and evaluation of the use of cooking oil for biodiesel generation in municipalities of southern Brazil

A Particle Swarm Optimization Algorithm Based on Time-Space Weight for Helicopter Maritime Search and Rescue Decision-Making

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