Citation

Zhao M, Liu X. Safety Sci. 2018; 102: 110-117.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.ssci.2017.10.007

PMID

unavailable

Abstract

Emergency rescue facility is an essential component of urban emergency logistics system, and selection of their locations is significant for urban public safety. Urban emergency rescue facility locations (UERFLs) problem is essentially a geospatial multi-objective optimization problem (Geospatial-MOP), which presents a challenge for both researchers and managers. In this study, a user-friendly decision support tool was designed and developed for facilitating the process of optimizing UERFLs in large-scale urban areas. We described the design, architecture and implementation of the tool and its core optimization component. Based on a hypothetical case study, we introduced its functionalities as well as the decision making workflow. The results provide evidences that the tool can successfully generate Pareto-optimal frontier and capture a pool of alternative solutions to the decision maker for trade-off. This work offers new insights on promoting future urban emergency logistics management with the use of GIS and emerging artificial intelligence technologies, and makes contributions in integrating multi-objective optimization algorithm with GIS for solving geospatial multi-objective optimization problem.

Language: en

Keywords

GIS; Emergency facility locations; Geospatial multi-objective optimization; Humanitarian logistics management; Urban public safety