A fleet of revenue-generating assets, a fixed annual repair budget, and assets that keep failing at random — this tool computes the optimal repair-or-skip rule for every day of the year and every level of budget remaining, so cheap fixes that pay back beat expensive ones that barely do.
Each equipment type carries its own repair cost and unit margin (net earnings per unit of output). Repairing a failed asset recovers output × margin × days remaining of earnings, but with a fixed cash budget the several cheap repairs you could fund instead of one expensive one usually recover more. The optimizer ranks the fleet by earnings per dollar of repair, not by raw output, and solves the resulting cash-budget allocation exactly by backward induction on a discretized budget grid, then validates the realized policy with Monte-Carlo simulation.
A static priority list (always fix the highest-output asset first) ignores the fact that a fixed budget is exhausted faster by expensive equipment. The optimizer instead computes a shadow price for each remaining budget dollar — how much recoverable earnings it is worth — and derives, per equipment type, an output cutoff: repair only if the failed asset's output is at least that cutoff. The cutoff rises as budget runs low or the year runs out, since a repair has less time or cash to earn back its cost.
A fleet of 260 assets across six equipment types shares a $1.8M annual repair budget against 60 expected failures. The optimizer typically deploys the full budget, recovers roughly ten times the earnings it spends, and captures over 90% of at-risk earnings — while the decision table shows exactly which output level justifies a repair for each equipment type, in each month, at each level of budget remaining.
For: reliability engineers, maintenance planners, and asset managers allocating a constrained repair or capex budget across a fleet with heterogeneous repair economics.