Dependency-Constrained Cascading Rescheduling: Network Evolution and Long-Term Adaptation

Traditional scheduling theory optimizes initial task assignments under static assumptions, yet operational systems face repeated disruptions requiring both immediate rescheduling and long-term structural adaptation. Existing approaches treat each disruption independently, failing to capture how organizations learn and evolve through repeated challenges. This paper presents a unified framework bridging cascading rescheduling with network evolution, formally modeling how dependency structures adapt over time to improve resilience. The framework consists of three integrated components: (1) immediate rescheduling algorithms with provable complexity bounds—O(n) for tree-structured dependencies, fixed-parameter tractable for bounded treewidth—enabling real-time response; (2) five adaptation strategies (redundancy, buffering, decoupling, reshuffling, and control) with convergence guarantees showing exponential improvement rate $O\left(e^{-(\sigma -\lambda )t}\right)$; and (3) computable resilience metrics quantifying organizational capacity to absorb disruptions. Comprehensive validation through 5200 simulated weeks (52 weeks × 100 replications) demonstrates substantial performance improvements. Redundancy-based adaptation achieves 109% resilience improvement and 66% disruption reduction compared to non-adaptive baselines ($p<0.001$, Cohen’s $d>1.8$). The framework is implemented as Orange3 visual programming widgets, achieving 92% user acceptance among non-technical practitioners with 7-month payback periods. While the framework is domain-agnostic and applicable to any operational network with dependency constraints, validation focuses on healthcare scheduling contexts where disruption patterns are well documented. The approach demonstrates that organizations can systematically build resilience through principled adaptation rather than reactive responses, with quantifiable performance improvements and accessible implementation tools.