Opportunistic inspection policy for groundwater well-heads addressing default

Abstract

This dissertation presents an optimized maintenance policy framework for critical groundwater systems, with a specific focus on groundwater well-heads and submersible pumps. The model introduces default probabilities to account for scenarios where maintenance actions cannot be executed due to external impediments, such as logistical constraints or operational failures. These default probabilities are integrated into an opportunistic inspection model, allowing for a more flexible and resilient approach to system maintenance. Through optimization modeling and comprehensive sensitivity analyses, the study investigates how decision variables such as time to scheduled inspection and the start of the window of opportunity interact to influence the overall cost rate and system reliability. Results demonstrate that increasing default probabilities and higher replacement costs are associated with a rise in total cost rates, reflecting the increased financial burden of system failures and missed maintenance opportunities. However, raising the frequency of inspections and allowing for longer defect detection times significantly reduce the cost rate, highlighting the importance of efficient scheduling and the effective use of inspection windows. Additionally, the findings suggest that decreasing opportunity costs widens the opportunity window for maintenance actions, leading to more flexible and cost-effective maintenance schedules. This work emphasizes the need for proactive strategies that prioritize inspection and timely interventions, particularly in environments where defaults and unforeseen failures are more likely. The research contributes practical insights into optimizing maintenance schedules for single-component systems and has applications for industries relying on such systems subject to default, where reliability and cost management are critical. The optimized maintenance policies proposed in this study provide a robust foundation for improving the long-term sustainability and operational efficiency of essential infrastructure systems.