Optimization of Predictive Maintenance Schedules under Uncertainty: A Scenario-Based Theoretical Framework

Abstract

This paper proposes a scenario-based framework for predictive maintenance scheduling under uncertainty in a finite planning horizon. The considered setting involves multiple assets for which maintenance decisions are informed by three heterogeneous sources of information: calendar-based overhaul intervals, usage-based limits driven by uncertain future operating cycles, and condition-monitoring outputs represented through remaining useful life (RUL) estimates with uncertainty. While these elements have been studied extensively in the maintenance literature, they are often treated separately or only partially integrated. In contrast, the proposed formulation evaluates complete maintenance schedules under simulated future scenarios and compares them using expected-cost and tail-risk criteria. The contribution is primarily conceptual and methodological: we define a unified finite-horizon decision framework that combines calendar-, usage-, and prognostics-based information within a common scheduling problem. A small synthetic computational example is used as a proof of concept. The results show that integrated scenario-based policies can substantially outperform simpler single-trigger rules, while the difference between risk-neutral and risk-aware integrated policies remains modest under the present calibration.