Time to Failure
Time to Failure defines how long a node operates before an interrupt occurs. It represents the active period between interruptions and is used to model events such as random equipment failures, scheduled downtime, wear-out conditions, or volume-based triggers.
In ReliaSim, Time to Failure is defined using a statistical distribution. Each time an interrupt is evaluated, a new value is sampled from this distribution and compared against either elapsed wall time, accumulated uptime, or processed volume—depending on the selected interrupt type. This allows failure behavior to reflect both predictable schedules and real-world variability.
Time to Failure can be driven by elapsed time, uptime, or throughput volume, and can operate in either competing or cumulative modes. Competing behavior is typically used for random failures where multiple potential causes may independently trigger an interrupt. Cumulative behavior is better suited for wear-based or consumption-driven scenarios, where progress toward failure builds gradually over repeated operating cycles.
By adjusting Time to Failure distributions, you can explore how reliability affects throughput, efficiency, and system stability. Even small changes in failure frequency can have significant downstream effects, making this parameter one of the most important levers for realistic modeling.
Time to Repair
Time to Repair defines how long a node remains unavailable once an interrupt has occurred. It represents recovery activities such as maintenance, resets, refilling, or operator intervention, and directly impacts how quickly normal operation resumes.
Like Time to Failure, Time to Repair is defined using a statistical distribution. Each interruption samples a repair duration, allowing downtime to vary from event to event. This makes it possible to model both consistent recovery times and highly variable repair processes.
While Time to Failure controls how often interruptions occur, Time to Repair determines how disruptive those interruptions are. Short repairs may have minimal impact on overall performance, while longer or more variable repairs can significantly increase upstream accumulation and downstream starvation.
Together, Time to Failure and Time to Repair define the reliability profile of constrained operations. By tuning both parameters, you can evaluate the impact of maintenance strategies, staffing levels, spare parts availability, and process improvements—helping prioritize actions that most effectively improve system performance.