Data Driven Block Replacement Scheduling
Learning when to replace factory machines by watching what breaks
When machines fail unpredictably, operators must decide how often to replace all machines at once—a choice that dramatically affects costs. This paper develops algorithms that learn the best replacement schedule from real operational data, without needing to know in advance how long machines typically last, and proves these algorithms find the optimal strategy nearly as fast as theoretically possible.
Factories, power plants, and infrastructure systems lose money both when machines fail unexpectedly and when they replace equipment too often. These algorithms let operators automatically tune maintenance schedules to their specific equipment based on what actually happens in the field, rather than guessing or using generic rules—potentially cutting total maintenance costs by 10–20% depending on the equipment and failure patterns.