GIS and Optimisation: Potential Benefits for Emergency Facility Location in Humanitarian Logistics

Abstract

Floods are one of the most dangerous and common disasters worldwide, and these disasters are closely linked to the geography of the affected area. As a result, several papers in the academic field of humanitarian logistics have incorporated the use of Geographical Information Systems (GIS) for disaster management. However, most of the contributions in the literature are using these systems for network analysis and display, with just a few papers exploiting the capabilities of GIS to improve planning and preparedness. To show the capabilities of GIS for disaster management, this paper uses raster GIS to analyse potential flooding scenarios and provide input to an optimisation model. The combination is applied to two real-world floods in Mexico to evaluate the value of incorporating GIS for disaster planning. The results provide evidence that including GIS analysis for a decision-making tool in disaster management can improve the outcome of disaster operations by reducing the number of facilities used at risk of flooding. Empirical results imply the importance of the integration of advanced remote sensing images and GIS for future systems in humanitarian logistics.

Dynamic formulation for humanitarian response operations incorporating multiple organisations

“…Disaster-specific parameters were determined based on information from authorities and a flood analysis in a GIS. The analysis in the GIS included the creation of flood maps to assess the situation and provide information for the model based on the process explained by Rodríguez-Espíndola et al (2016).…”

Healthcare Logistics System Planning for Facility Location of Aging Society

“…If the data has an unstable variance, it must first be adjusted to have stabilization properties by finding the differences and comparing the characteristics of the correlation function for the Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF; 2) Model parameter estimation is the procedure for estimating the value of the ARIMA model to obtain the smallest discrepancy; 3) Model verification aims to verify that the characteristics are consistent with the accuracy of the assumptions. If not consistent, the model must be adjusted [17]. Therefore, ARIMA does ex-plain the values of data better than regression.…”

Beyond mapping: a case for geospatial analytics in humanitarian health

“…Humanitarian logistics –whether focused on supply chains, resourcing or distribution, leads the incorporation of geoanalytics into decision-support tools for humanitarian operations [8]. Multistep models, informed by operations research methodology, integrate remotely-sensed (either aerial or satellite) imagery, land use, road maps, land elevations and pre-existing infrastructure to determine cost surfaces and risk profiles.…”