A filtering genetic programming framework for stochastic resource constrained multi-project scheduling problem under new project insertions

Multi-project management and uncertain environment are very common factors, they bring greater challenges to scheduling due the increase of problem complexity response efficiency requirements. In this paper, a novel hyper-heuristic based filtering genetic programming (HH-FGP) framework is proposed for evolving priority rules (PRs) deal with multi-project considering stochastic activity duration new project insertion together, namely Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI), within heuristic computation time. HH-FGP designed divide traditional evolution into sampling simultaneously two kinds parameters constituting PRs, depth range attribute, obtain more effective PRs. Based on this, existing search local improved meet constraints, multi-objective evaluation mechanism achieve filtering. Under benchmark, compared analysed methods verify its effectiveness.