Iterative Flattening Search on RCPSP/max Problems: Recent Developments

This paper proposes an iterative improvement algorithm for solving instances of the Resource Constraint Project Scheduling Problem with Time-Windows (RCPSP/max). The algorithm is based on Iterative Flattening Search (ifs), an effective meta-heuristic strategy proposed over the past years for solving multi-capacity optimization scheduling problems. Given an initial solution, ifs iteratively applies two steps: (1) a subset of solving decisions are randomly retracted from a current solution (relaxation-step); (2) a new solution is incrementally recomputed (flattening-step). At the end, the best solution found is returned. To the best of our knowledge this is the first paper which proposes a version of ifs for solving RCPSP/max instances. The main contribution of this paper is threefold: (1) we succeed in improving 15 out of 90 solutions with respect to the officially published current best, thus demonstrating the general efficacy of ifs; (2) we highlight an intrisic limitation of the original ifs strategy in solving RCPSP/max, such that under certain circumstances the two-step improvement loop can get stuck in a status where no solving decision can be retracted; (3) we propose two different escaping strategies which extend the original ifs procedure. An experimental evaluation ends the paper, comparing the performances of the proposed escaping strategies against the original ifs procedure.