Solving the Social Golfers Problems by Constraint Programming in Sequential and Parallel

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.