A GIS supported ant algorithm for the linear feature covering problem with distance constraints

Authors:
Bo Huang;Nan Liu;Magesh Chandramouli
Affiliations:
Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada;Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC;GIS Research Center, Feng Chia University, Taichung, Taiwan
Venue:
Decision Support Systems
Year:
2006

Citing 3
Cited 6

Practical genetic algorithms

Practical genetic algorithms
Geographical information systems and location science

Computers and Operations Research - Location analysis
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation

Investigation of Ant System parameter interactions by using design of experiments for job-shop scheduling problems

Computers and Industrial Engineering
Spatial point process models for location-allocation problems

Computational Statistics & Data Analysis
Ant intelligence for solving optimal path-covering problems with multi-objectives

International Journal of Geographical Information Science
A study on analysis support system of energy and environmental system for sustainable development based on MFM and GIS

Proceedings of the 2007 conference on Human interface: Part II
Survey: Covering problems in facility location: A review

Computers and Industrial Engineering
Design-based spatial sampling: Theory and implementation

Environmental Modelling & Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper analyzes a linear feature covering problem (LFCP) with distance constraints, and characterizes the problem by a fuzzy multi-objective (MO) optimization model. An integrated approach combining an Ant algorithm (LFCP-Ant) and a Geographic Information System (GIS) has been devised to solve the LFCP problem in large scale. The efficacy of the proposed approach is demonstrated using a case study of locating new fire stations in Singapore. A GIS has been used to transform the continuous problem into a discrete one, which is then solved using the LFCP-Ant. This algorithm employs a two-phase local search to improve both search efficiency and precision.