Ant colony optimization algorithm for the Euclidean location-allocation problem with unknown number of facilities

Authors:
Jean-Paul Arnaout
Affiliations:
Industrial & Mechanical Engineering Department, Lebanese American University, Byblos, Lebanon 101-F
Venue:
Journal of Intelligent Manufacturing
Year:
2013

Citing 6
Cited 0

Comparison of genetic algorithms, random restart and two-opt switching for solving large location-allocation problems

Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Improvement and Comparison of Heuristics for Solving the Uncapacitated Multisource Weber Problem

Operations Research
Ant Colony Optimization

Ant Colony Optimization
The multi-facility location-allocation problem with polyhedral barriers

Computers and Operations Research
A study on facility location---allocation problem in mixed environment of randomness and fuzziness

Journal of Intelligent Manufacturing
Genetic application in a facility location problem with random demand within queuing framework

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper addresses the Euclidean location-allocation problem with an unknown number of facilities, and an objective of minimizing the fixed and transportation costs. This is a NP-hard problem and in this paper, a three-stage ant colony optimization (ACO) algorithm is introduced and its performance is evaluated by comparing its solutions to the solutions of genetic algorithms (GA). The results show that ACO outperformed GA and reached better solutions in a faster computational time. Furthermore, ACO was tested on the relaxed version of the problem where the number of facilities is known, and compared to existing methods in the literature. The results again confirmed the superiority of the proposed algorithm.