TY - CHAP
T1 - A computational geometry-based local search algorithm for planar location problems
AU - Cambazard, Hadrien
AU - Mehta, Deepak
AU - O'Sullivan, Barry
AU - Quesada, Luis
PY - 2012
Y1 - 2012
N2 - Constraint-based local search is an important paradigm in the field of constraint programming, particularly when considering very large optimisation problems. We are motivated by applications in areas such as telecommunications network design, warehouse location and other problems in which we wish to select an optimal set of locations from a two dimensional plane. The problems we are interested in are so large that they are ideal candidates for constraint-based local search methods. Maintaining the objective function incrementally is often a key element for efficient local search algorithms. In the case of two dimensional plane problems, we can often achieve incrementality by exploiting computational geometry. In this paper we present a novel approach to solving a class of placement problems for which Voronoi cell computation can provide an efficient form of incrementality. We present empirical results demonstrating the utility of our approach against the current state of the art.
AB - Constraint-based local search is an important paradigm in the field of constraint programming, particularly when considering very large optimisation problems. We are motivated by applications in areas such as telecommunications network design, warehouse location and other problems in which we wish to select an optimal set of locations from a two dimensional plane. The problems we are interested in are so large that they are ideal candidates for constraint-based local search methods. Maintaining the objective function incrementally is often a key element for efficient local search algorithms. In the case of two dimensional plane problems, we can often achieve incrementality by exploiting computational geometry. In this paper we present a novel approach to solving a class of placement problems for which Voronoi cell computation can provide an efficient form of incrementality. We present empirical results demonstrating the utility of our approach against the current state of the art.
UR - https://www.scopus.com/pages/publications/84861423366
U2 - 10.1007/978-3-642-29828-8_7
DO - 10.1007/978-3-642-29828-8_7
M3 - Chapter
AN - SCOPUS:84861423366
SN - 9783642298271
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 97
EP - 112
BT - Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems - 9th International Conference, CPAIOR 2012, Proceedings
T2 - 9th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming, CPAIOR 2012
Y2 - 28 May 2012 through 1 June 2012
ER -