The scheduling of offshore wind farm (OWF) installations is a complex, weather-driven, and resource-constrained problem. Mixed-integer linear programming (MILP) models excel at cost and makespan optimization, but remain difficult to verify and validate beyond feasibility checks. Similarly, Petri nets (PNs) provide behavioral transparency and simulation fidelity, yet lack prescriptive optimality. This article presents frameworks that utilize complementary application strategies to address two key needs, thereby combining the strengths of both approaches. First, an iterative verification–validation framework is employed that derives local PN models from MILP solutions and compares them with knowledge-based PN representations using conformance and reachability checks. Implemented in a simulation environment for the installation of OWF, the framework enables consistent model verification, cross-model validation, and context-aware scheduling. Second, a cascading decision-support framework that selects or blends MILP, heuristic, and PN-based scheduling methods through standardized descriptors and context signals. Numerical experiments demonstrate that optimization minimizes offshore time and cost, but requires high computational efforts. In contrast, Heuristics deliver short-term plans rapidly, while PN-based schedules offer intermediate costs and longer planning horizons, demonstrating that hybrid, context-sensitive orchestration outperforms any single method.
Peng et al. (Tue,) studied this question.