Non-Model-Based Search Guidance for Set Partitioning Problems

Instead, we cluster Search is an integral part of solution approaches for NPhard training instances according to their features and determine combinatorial optimization and decision problems. Once the an assignment of branching heuristics to clusters that results ability to reason deterministically is exhausted, state-of-theart in the best performance when the branching heuristic is dynamically solvers try out different alternatives which may lead to chosen based on the current subproblem's nearest an improved (in case of optimization) or feasible (in case cluster. We test our approach on the MIP-solver Cplex that of satisfaction) solution. This consideration of alternatives we use to tackle set partitioning problems. Our experiments may take place highly opportunistically as in local search approaches, show that this approach can effectively boost search performance or systematically as in backtracking-based methods.