USING SIMULATED ANNEALING FOR TERRITORIAL OPTIMIZATION

 

María Beatriz Bernábe Loranca, David Eduardo Pinto Avendaño, Rogelio González Velázquez

 

Abstract

 

A particular method for solving the problem of territorial optimization requires a classification process based on clustering in which multiple comparisons must be performed in order to fulfill an objective function that minimizes the distances among objects with the purpose of achieving geographical compactness. The computational complexity of this problem is known to be in the NP-complete category, therefore, we have tackle the problem by means of heuristic methods. We have selected the simulated annealing technique to be applied inside the clustering algorithm, because of its capability of finding high quality suboptimal solutions. In general, in this paper we present a mathematical model and a geographical clustering algorithm for solving aggregation, a particular feature that exists in every problem of the territorial kind. The algorithm is combinatorial and, therefore, it obtains approximate solutions throughout the execution of the simulated annealing algorithm. The experiment carried out needed a factorial statistical analysis in order to model the parameters of the employed heuristic. The dataset are of the census type, with a well defined spatial component and a vector of descriptive variables.

 

Lecture Notes in Management Science (2011) Vol. 3: 335-350

3rd International Conference on Applied Operational Research, Proceedings

© Tadbir Operational Research Group Ltd. All rights reserved.

www.tadbir.ca

 

ISSN 2008-0050 (Print)

ISSN 1927-0097 (Online)

 

ARTICLE OUTLINE

 

·         Introduction

·         Related Works

·         Solving Geographical Clustering With PAM

·         Mathematical Formulation Of Geographical Clustering For Territorial Optimization

·         Problem Formulation

·         SA Heuristic To Obtain Sub-Optimal Solutions

·         Results Of Simulated Annealing For Territorial Optimization

·         Random Experiment

·         Statistical Experiment

·         Conclusions

·         References

 

Full Text PDF