P2P B&B and GA for the flow-shop scheduling problem

International audienceSolving exactly Combinatorial Optimization Problems (COPs) using a Branch-and-Bound algorithm (B&B) requires a huge amount of computational resources. The efficiency of such algorithm can be improved by its hybridization with meta-heuristics such as Genetic Algorithms (GA) which proved their effectiveness, since they generate acceptable solutions in a reasonable time. Moreover, distributing at large scale the computation, using for instance Peer-to-Peer (P2P) Computing, provides an efficient way to reach high computing performance. In this chapter, we propose ParallelBB and ParallelGA, which are P2P-based parallelization of the B&B and GA algorithms for the computational Grid. The two algorithms have been implemented using the ProActive distributed object Grid middleware. The algorithms have been applied to a mono-criterion permutation flow-shop scheduling problem and promisingly experimented on the Grid5000 computational Grid