A hybrid simulation-optimization approach for the robust Discrete Time/Cost Trade-off Problem

We investigate a stochastic Discrete Time/Cost Trade-off Problem. This multimode project scheduling problem, requires assigning modes to activities, and scheduling the activities so that the sum of the activities cost and the expected tardiness cost is minimized. Furthermore, the derived solution should be stable. This stability property renders the schedule less sensitive to uncontrollable factors that may disrupt the project execution. To solve this problem, we propose a two-stage solution strategy. In the first stage, we implemented a simulation–optimization strategy that yields a cost-effective schedule. Next, a mixed-integer programming formulation is used to derive a schedule that is both stable and cost-effective. We report the results of computational experiments that attest to the efficacy of the proposed approach.