Global optimization for multi-stage construction of rescue units in disaster response. (November 2019)

• Record Type: Journal Article
• Authors: Xu, Ning; Zhang, Qiong; Zhang, Haoran; Hong, Minsung; Akerkar, Rajendra; Liang, Yongtu
• Is Part Of: Sustainable cities and society. Volume 51(2020)
• Publication Date: 2019-11
• Subjects: Multi-stage construction -- Global optimization -- Bi-level decomposition algorithm -- Accelerated solution; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2019.101768
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: This study conceptualizes the problem of developing a rescue plan when disaster strikes to guarantee the stable development of the city based on the collected information. The study deals with the dynamic change of disaster situation by introducing multi stages, the rescue plan of each stage should be based on that of the previous stages. It compares BLD algorithm with PSO algorithm and proves that the former has higher stability and better quality of results. It proposes two ways of accelerating solution for BLD algorithm and the ABLD algorithm obtained can determine the optimal rescue plan in less time. Abstract: Disasters pose a serious threat to people' lives and urban environment, affecting the sustainable development of society. Then it's crucial to quickly develop an efficient rescue plan for the disaster area. However, disaster rescue is rather difficult due to the requirement to develop the optimal rescue plan as quickly as possible according to the information of trapped people and rescue teams, and the amount of information will continue to increase as the rescue proceeds. At present, most of the rescue plans are manually made based on previous rescue experience. But obviously these plans might be the not optimal one. Considering the real-time location data of trapped people, this paper develops a Mixed Integer Non-linear Programming (MINLP) model to find the highest efficient rescue plan To solve the model accurately and efficiently, a bi-level decomposition (BLD) algorithm is presented to iteratively solve a discretized Mixed Integer Linear Programming (MILP) model and its nonconvex Non-linear Programming (NLP) model until a converged solution is obtained. In addition, since more trapped people could be found over time, the built rescue units should also be considered when making a rescue plan for a new stage. To further improve the solving efficiency, an accelerated bi-level decomposition (ABLD) algorithm is also proposed. Finally, a real-world disaster rescue is given to validate the superiority of the proposed ABLD algorithm relative to particle swarm optimization (PSO) algorithm and BLD algorithm.