Control Schemes in a Generalized Utility for Parallel Branch-and-Bound Algorithms
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
Branch, Cut, and Price: Sequential and Parallel
Computational Combinatorial Optimization, Optimal or Provably Near-Optimal Solutions [based on a Spring School]
Parallel branch and cut for capacitated vehicle routing
Parallel Computing - Special issue: Parallel computing in logistics
A Library Hierarchy for Implementing Scalable Parallel Search Algorithms
The Journal of Supercomputing
Computational Experience with a Software Framework for Parallel Integer Programming
INFORMS Journal on Computing
Design of sequence family subsets using a branch and bound technique
IEEE Transactions on Information Theory
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Branch and bound algorithms are general methods applied to various combinatorial optimization problems. Recently, parallelizations of these algorithms have been proposed. In spite of the generality of these methods, many of the parallelizations have been set up for a specific problem and a specific parallel computer. A generalized utility PUBB (Parallelization Utility for Branch and Bound algorithms) is presented. It can be used on a network of workstations and enables us to easily apply parallelized branch and bound algorithms on any specific combinatorial optimization problem. A new selection rule (hybrid selection rule) was implemented during this study. Several branch and bound algorithms were experimentally parallelized with PUBB, using up to 111 networked workstations. The results of these experiments show that superlinear speedup in solving time may be achieved when the number of processing elements is increased and also indicate that the hybrid selection rule has an advantage over other selection rules.