Emergency Rescue Vehicle Dispatch Planning Using a Hybrid Algorithm

Jie Cao (), He Han (), Yi-Ping Jiang () and Ya-Jing Wang ()
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Jie Cao: School of Public Administration and Collaborative Innovation, Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing Jiangsu 210044, P. R. China
He Han: #x2020;School of Information & Control, Nanjing University of Information Science & Technology, Nanjing Jiangsu 210044, P. R. China
Yi-Ping Jiang: #x2021;College of Engineering, Nanjing Agricultural University, Nanjing Jiangsu 210031, P. R. China
Ya-Jing Wang: #xA7;Shuozhou Meteorological Bureau of Shanxi Province, Shanxi Shuozhou 038500, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2018, vol. 17, issue 06, 1865-1890

Abstract: This paper describes the emergency rescue vehicle transportation network within the entire rescue period, and imitates rescue vehicle to select rescue route and to allocate emergency resource. The presented emergency rescue vehicle dispatch model seeks to minimize rescue time as the first objective function, minimize delay cost as the second objective function and maximize lifesaving utility as the last objective function in disaster response operations. To solve the proposed multiple objective model, a hybrid algorithm named nondominated sorting genetic algorithm (NSGA-II) with ant colony algorithm and a NSGA-II with random crossover and mutation, which can find better initial solution, are presented. In order to further prove the validity of the model and algorithm, a more complicated case is cited. Computational results are reported to illustrate the performance of the proposed model and algorithm. Statistical analysis confirms that the proposed random crossover and mutation operator outperforms the original crossover and mutation operator. The sensitivity analysis proves which parameter is more important for objective function values.

Keywords: Emergency rescue; vehicle dispatch; NSGA-II; hybrid algorithm; delay cost (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S0219622018500414

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