Maritime Fleet Schedule Optimization and Disruption of System Order 102 views

Schedule optimization models often overlook disruptions to system order, including the prioritization of missions, assets, and infrastructure. This thesis introduces the Kendall–Tau (K–T) statistic as a measure of order sensitivity within maritime fleet scheduling. Within a mixed-integer linear programming (MILP) framework, K–T quantifies how disruption scenarios alter both mission allocations and the relative ordering of priorities. The demonstration assigns nine U.S. Coast Guard vessels to eight patrol types with distinct duration and coverage requirements. Ten disruption scenarios—representing backlogged maintenance, crew constraints, mission surges, and external stressors—test system resilience. Each case undergoes re-optimization, and comparisons with the baseline use K–T statistics to capture changes in both mission coverage and rank order. The findings show that extended maintenance and mission shifts disrupt system order most severely, while certain vessels and missions consistently display higher sensitivity to disruption. These insights pinpoint where fragility exists in the scheduling framework and identify opportunities for planners to implement resiliency measures. This work extends systems reliability theory by explicitly accounting for order disruption and offering a transparent decision-support tool for resource-constrained environments. The framework applies broadly to transportation, defense, and infrastructure systems that demand resilience and interpretable models in scheduling.

MS (Master of Science)

Systems Reliability ; Risk Analysis; Resilience Analysis; Sensitivity Analysis; Requirements Analysis; Logistics Systems; Kendall’s Tau Statistic

English

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2025-11-09