(411a) A New Tool for the Evaluation and Optimization of Preventive Maintenance Scheduling in Processing Plants

A new methodology designed to optimize both the scheduling of preventive maintenance and the amount of resources needed to perform maintenance in a process plant is presented. The methodology is based on the use of a genetic algorithm to determine what schedule is most appropriate, while evaluating each of these using a Monte Carlo simulation. The overall goal of this method is to facilitate improvements in plant safety, reduce equipment replacement costs, and reduce economic losses due to downtime or reduced production. The simulation accurately describes equipment failure types, takes into account mean time between failures, maintenance cost, resources limitations, labor cost, repair downtime, failed but un-repaired equipment performance due to lack of resources, and other maintenance rules. In addition, inventory management decisions are factored in. The model divides the time horizon into specific intervals (usually weeks) and allows a maintenance schedule to be created based on tasks priorities. Optimization of the well-known Tennessee Eastman Plant Problem is used to illustrate the method. Optimal labor as well as optimal inventory policies as well as preventive maintenance frequencies for different equipments are identified. Extensions to safety will be discussed.