scholarly journals A Three-Stage Hierarchical Model for An Earthquake Shelter Location-Allocation Problem: Case Study of Chaoyang District, Beijing, China

2019 ◽  
Vol 11 (17) ◽  
pp. 4561 ◽  
Author(s):  
Xiujuan Zhao ◽  
Jianguo Chen ◽  
Wei Xu ◽  
Shiyan Lou ◽  
Peng Du ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Hierarchical Model ◽  
Particle Swarm ◽  
Economic Loss ◽  
Swarm Optimization ◽  
Evacuation Time ◽  
Modified Particle Swarm Optimization ◽  
The Government ◽  
Beijing China

Earthquakes are one type of natural disaster that causes serious economic loss, deaths, and homelessness, and providing shelters is vital to evacuees who have been affected by an earthquake. Constructing shelters with reasonable capacity in the right locations and allocating evacuees to them in a reasonable time period is one disaster management method. This study proposes a multi-objective hierarchical model with three stages, i.e., an immediate shelter (IS) stage, a short-term shelter (STS) stage, and a long-term shelter (LTS) stage. According to the requirements of evacuees of IS, STS, and LTS, the objective of both the IS and STS stages is to minimize total evacuation time and the objectives of the LTS are to minimize total evacuation time and to minimize total shelter area. A modified particle swarm optimization (MPSO) algorithm is used to solve the IS and STS stages and an interleaved modified particle swarm optimization algorithm and genetic algorithm (MPSO-GA) is applied to solve the LTS stage. Taking Chaoyang District, Beijing, China as a case study, the results generated using the model present the government with a set of options. Thus, according to the preferences of the government, the determination can be made regarding where to construct ISs, STSs, and LTSs, and how to allocate the evacuees to them.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

Sign in / Sign up

Export Citation Format

Share Document