A decision support method for design and operationalization of search and rescue in maritime emergency. (1st July 2020)

• Record Type: Journal Article
• Authors: Xiong, Weitao; van Gelder, P.H.A.J.M.; Yang, Kewei
• Is Part Of: Ocean engineering. Volume 207(2020)
• Publication Date: 2020-07-01
• Subjects: Search and rescue -- Decision support -- Multi-objective optimization -- Differential evolution -- Maritime emergency response; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2020.107399
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Design and operationalization for Search and Rescue (SAR) activities are unstructured and complex multi-criteria decision-making problems, especially for maritime emergency scenario. There is a lack of decision support methods based on intelligent algorithms to shorten the response time and to reduce the loss of life and property. The primary purpose of this paper is to develop a three-stage decision support method to optimize the type and number of resources when making SAR schemes so as to formulate emergency response more efficiently and effectively. First, the main influential factors are identified in Stage 1, including the particulars of environmental indices, search objects and SAR resources. Next, in Stage 2, important variables are defined for generating probability distribution maps, identifying the search areas, and evaluating the objective function in Stage 3. Two intelligent algorithms, the Differential Evolution (DE) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), are used to find appropriate SAR schemes and help resources scheduling. Finally, the feasibility and validity of the model are verified by a ship collision example. From the simulation of the SAR task assignment and decision preference analysis, the proposed method can be used for further improvement of SAR design and operationalization. Highlights: Comprehensive review on decision support of maritime emergencies. Three-stage decision support method based on two intelligent algorithms. Increasing resources do not lead to a proportional improvement in success rate. Decision-making process can be adjusted by specific preferences.