Authors:
Ke Liu
;
Sven Löffler
and
Petra Hofstedt
Affiliation:
Brandenburg University of Technology Cottbus-Senftenberg, Germany Department of Mathematics and Computer Science, MINT, Konrad-Wachsmann-Allee 5, 03044 Cottbus and Germany
Keyword(s):
Constraint Programming, Constraint Satisfaction, Parallel Constraint Solving, Sports Scheduling, Social Golfer Problem.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Constraint Satisfaction
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
The social golfer problem (SGP) has received plenty of attention in constraint satisfaction problem (CSP)
research as a standard benchmark for symmetry breaking. However, the constraint satisfaction approach has
stagnated for solving larger SGP instances over the last decade. We improve the existing model of the SGP
by introducing more constraints that effectively reduce the search space, particularly for instances of special
form. Furthermore, we present a search space splitting method to solve the SGP in parallel through data-level
parallelism. Our implementation of the presented techniques allows us to attain solutions for eight instances
with maximized weeks, in which six of them were open instances for the constraint satisfaction approach, and
two of them are computed for the first time. Besides, super-linear speedups are observed for all the instances
solved in parallel.