|
Kostas Kolomvatsos, Kakia Panagidi, & Stathes Hadjiefthymiades. (2013). Optimal spatial partitioning for resource allocation. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 747–757). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Spatial partitioning consists of the problem of finding the best segmentation of an area under specific conditions. The final goal is to identify parts of the area where a number of resources could be allocated. Such cases are common in disaster management scenarios. In this paper, we consider such a scenario and propose a methodology for the resource allocation for emergency response. We utilize an intelligent technique that is based on the Particle Swarm Optimization algorithm. We define the problem by giving specific formulations and describe the proposed algorithm. Moreover, we provide a method for separating the area into cells and describe a technique for calculating cell weights based on the underlying spatial data. Finally, we present a case study for allocating a number of ambulances and give numerical results concerning the run time and the total coverage of the examined area.
|
|
|
Lida Huang, Tao Chen, Yan Wang, & Hongyong Yuan. (2015). Forecasting Daily Pedestrian Flows in the Tiananmen Square Based on Historical Data and Weather Conditions. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: It is important to forecast the pedestrian flows for organizing crowd activities and making risk assessments. In this article, the daily pedestrian flows in the Tiananmen Square are forecasted based on historical data, the distribution of holidays and weather conditions including rain, wind, temperature, relative humidity, and AQI (Air Quality Index). Three different methods have been discussed and the Support Vector Regression based on the Adaptive Particle Swarm Optimization (APSO-SVR) has been proved the most reliable and accurate model to forecast the daily pedestrian flows. The results of this paper can help to conduct security pre-warning system and enhance emergency preparedness and management for crowd activities.
|
|
|
Xiujuan Zhao, Graham Coates, & Wei Xu. (2017). Solving the earthquake disaster shelter location-allocation problem using optimization heuristics. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 50–62). Albi, France: Iscram.
Abstract: Earthquakes can cause significant disruption and devastation to populations of communities. Thus, in the event of an earthquake, it is necessary to have the right number of disaster shelters, with the appropriate capacity, in the right location in order to accommodate local communities. Mathematical models, allied with suitable optimization algorithms, have been used to determine the locations at which to construct disaster shelters and allocate the population to them. This paper compares the use of two optimization algorithms, namely a genetic algorithm and a modified particle swarm optimization, both of which have advantages and disadvantages when solving the disaster shelter location-allocation problem.
|
|
|
Xiujuan Zhao, Jianguo Chen, Peng Du, Wei Xu, Ran Liu, & Hongyong Yuan. (2019). Location-allocation model for earthquake shelter solved using MPSO algorithm. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Constructing shelters in suitable quantities, with adequate capacities and at the right locations is essential for evacuees under earthquake disasters. As one of the disaster management methods, constructing shelters can help to significantly reduce disruption and devastation to affected population. Mathematical models have been used to solve this problem allied with a heuristic optimization algorithm. The optimization of evacuation efficiency, as one of the most important objectives, has many expressive forms, such as minimizing evacuation distance and evacuation time. This paper proposes a new model that aims to minimize evacuation time with a new calculation method and to maximize total evacuees? comfort level. The modified particle swarm optimization (MPSO) algorithm is employed to solve the model and the result is compared with a model that calculated evacuation time differently and a model without distance constraint, respectively.
|
|