Simulation Library for Resource Constrained Project Scheduling with Uncertain Activity Durations

Stochastic Resource Constrained Project Scheduling (SRCPS) is among the hardest combinatorial problems. Exact calculations of interesting measures, such as expected project duration and the probability of satisfying the deadline, using known probabilities are in #P even for relaxed instances of the problem where resource constraints are ignored. The most common approach is to use substantial simulation to evaluate candidate solutions. All of the work so far uses ad-hoc developed simulation environments with prevalent use of a priori generated activity duration scenarios. This paper describes the discrete-time simulation library aimed to support the creation of simulation-based algorithms for solving SRCPS problems with known probability distributions of activity durations. The library is designed, in the first instance, with shared memory parallelization of simulation, using OpenMP. Runtime parallelized generation of random activity duration scenarios is supported and we deal with "inconveniences" that are otherwise elegantly avoided using a priori generated activity duration scenarios. However, in some approaches that is not feasible and runtime scenario generation has to be used. We propose modular organization of simulators that enables better reuse of basic intrinsic project scheduling functionality.