An emergency response system by dynamic simulation and enhanced particle swarm optimization and application for a marine oil spill accident

Abstract An emergency response system is important to protect the public and the environment and mitigate the negative effects from major accidents or natural disasters. It helps minimize time and losses by optimizing response operations and use of resources. Marine oil spills, as a typical accident, can cause significant, long-term and adverse impacts on ecological, social and economic systems. An efficient response system can significantly reduce both overall response time and cost especially when dealing with a large-scale spill. This paper proposed an emergency response system based on dynamic process simulation and system optimization modeling. This was achieved by the development of an enhanced particle swarm optimization (ME-PSO) algorithm with outstanding convergence performance and low computation cost characteristics which integrated multi-agent theory and evolutionary population dynamics. The performance was evaluated using various PSO algorithms with 13 benchmark functions under 48 parameter combinations. A case study on a representative marine oil spill was conducted to demonstrate the proposed methodology and its value in supporting emergency response decision-making. Allocation and deployment of responses from multiple response centers were optimized to save time and increase recovery efficiency with the process simulations of resource dispatch, oil weathering and oil removal. The research not only contributed to emergency decision making through an integrated, dynamic and simulation-optimization coupling approach but also provided emergency responders a powerful tool to improve their response capabilities in dealing with emergencies such as marine oil spills.

[1]  Mohamed Haouari,et al.  Review of optimization techniques applied for the integration of distributed generation from renewable energy sources , 2017 .

[2]  Zheng Wan,et al.  Oil spills from global tankers: Status review and future governance , 2019, Journal of Cleaner Production.

[3]  Chuangxin Guo,et al.  A multiagent-based particle swarm optimization approach for optimal reactive power dispatch , 2005 .

[4]  S. Talatahari,et al.  Developed Swarm Optimizer: A New Method for Sizing Optimization of Water Distribution Systems , 2016, J. Comput. Civ. Eng..

[5]  J. Harrison.  “Down Here We Rely on Fishing and Oil”: Work Identity and Fishers’ Responses to the BP Oil Spill Disaster , 2020 .

[6]  P. Zheng,et al.  Optimization on Emergency Materials Dispatching Considering the Characteristics of Integrated Emergency Response for Large-Scale Marine Oil Spills , 2019, Journal of Marine Science and Engineering.

[7]  T. Grubesic,et al.  Optimizing oil spill cleanup efforts: A tactical approach and evaluation framework. , 2017, Marine pollution bulletin.

[8]  M.H. Moradi,et al.  A combination of Genetic Algorithm and Particle Swarm Optimization for optimal DG location and sizing in distribution systems , 2010, 2010 Conference Proceedings IPEC.

[9]  Jihong Chen,et al.  Marine oil spill pollution causes and governance: A case study of Sanchi tanker collision and explosion , 2020 .

[10]  Seyedali Mirjalili,et al.  Optimisation Algorithms for Hand Posture Estimation , 2019 .

[11]  Jin Li,et al.  Multiple-resource and multiple-depot emergency response problem considering secondary disasters , 2012, Expert Syst. Appl..

[12]  Liang Guo,et al.  Emergency material allocation with time-varying supply-demand based on dynamic optimization method for river chemical spills , 2018, Environmental Science and Pollution Research.

[13]  M A Hannan,et al.  Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm. , 2018, Waste management.

[14]  Fritz Gehbauer,et al.  Optimized resource allocation for emergency response after earthquake disasters , 2000 .

[15]  Y. Wang,et al.  Comparison of using polyurethane foam passive samplers and tree bark samples from Western China to determine atmospheric organochlorine pesticide. , 2016, Journal of Environmental Science.

[16]  Rune Storesund,et al.  An integrated offshore oil spill response decision making approach by human factor analysis and fuzzy preference evaluation. , 2020, Environmental pollution.

[17]  Paraskevi S. Georgiadou,et al.  Multi-objective evolutionary emergency response optimization for major accidents. , 2010, Journal of hazardous materials.

[18]  Bing Chen,et al.  Monte Carlo simulation-based dynamic mixed integer nonlinear programming for supporting oil recovery and devices allocation during offshore oil spill responses , 2014 .

[19]  Bing Chen,et al.  Offshore oil spill response practices and emerging challenges. , 2016, Marine pollution bulletin.

[20]  A. Azevedo,et al.  A cross-scale numerical modeling system for management support of oil spill accidents. , 2014, Marine pollution bulletin.

[21]  Wen J. Li,et al.  An assertive reasoning method for emergency response management based on knowledge elements C4.5 decision tree , 2019, Expert Syst. Appl..

[22]  T. Grubesic,et al.  A strategic planning approach for protecting environmentally sensitive coastlines from oil spills: Allocating response resources on a limited budget , 2019, Marine Policy.

[23]  Martha Grabowski,et al.  Dynamic resource allocation to support oil spill response planning for energy exploration in the Arctic , 2017, Eur. J. Oper. Res..

[24]  Liang Zhang,et al.  Optimal design of Kaibel dividing wall columns based on improved particle swarm optimization methods , 2020 .

[25]  Pu Li,et al.  A simulation-based multi-agent particle swarm optimization approach for supporting dynamic decision making in marine oil spill responses , 2019, Ocean & Coastal Management.

[26]  Seyed Mohammad Mirjalili,et al.  Evolutionary population dynamics and grey wolf optimizer , 2015, Neural Computing and Applications.

[27]  B. E. Kirstein,et al.  Ocean-ice/oil-weathering computer program user's manual. Final report , 1987 .

[28]  Zheng Wan,et al.  Human errors are behind most oil-tanker spills , 2018, Nature.

[29]  Yaochu Jin,et al.  A social learning particle swarm optimization algorithm for scalable optimization , 2015, Inf. Sci..

[30]  Vincent Roberge,et al.  Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning , 2013, IEEE Transactions on Industrial Informatics.

[31]  Yong Liu,et al.  Multi-agent hybrid particle swarm optimization (MAHPSO) for wastewater treatment network planning. , 2019, Journal of environmental management.

[32]  Masoud Monjezi,et al.  Prediction of seismic slope stability through combination of particle swarm optimization and neural network , 2015, Engineering with Computers.

[33]  Reza Safabakhsh,et al.  A novel stability-based adaptive inertia weight for particle swarm optimization , 2016, Appl. Soft Comput..

[34]  Kaveh Khalili Damghani,et al.  Design of SCADA water resource management control center by a bi-objective redundancy allocation problem and particle swarm optimization , 2015, Reliab. Eng. Syst. Saf..

[35]  Junfei Qiao,et al.  Multiobjective design of fuzzy neural network controller for wastewater treatment process , 2018, Appl. Soft Comput..

[36]  Yufei Yuan,et al.  Modeling multiple humanitarian objectives in emergency response to large-scale disasters , 2015 .

[37]  Y. P. Li,et al.  Planning Regional Water Resources System Using an Interval Fuzzy Bi-Level Programming Method , 2010 .

[38]  Ibrahim Berkan Aydilek.  A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems , 2018, Appl. Soft Comput..

[39]  V. J. García-Garrido,et al.  A dynamical systems perspective for a real-time response to a marine oil spill. , 2016, Marine pollution bulletin.

[40]  Abdul-Lateef Balogun,et al.  DEVELOPING AN EMERGENCY RESPONSE MODEL FOR OFFSHORE OIL SPILL DISASTER MANAGEMENT USING SPATIAL DECISION SUPPORT SYSTEM (SDSS) , 2018 .