18th European Symposium on Computer Aided Process Engineering – ESCAPE 18 Bertrand Braunschweig and Xavier Joulia (Editors) © 2008 Elsevier B.V./Ltd. All rights reserved.
Optimization of Preventive Maintenance Scheduling in Processing Plants DuyQuang Nguyen and Miguel Bagajewicz
The University of Oklahoma, R. T-335 SEC, 100 E. Boyd, Norman, OK 73019, USA
A new methodology designed to optimize both the planning 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 Montecarlo simulation to evaluate the expected cost of maintenance as well as the expected economic loss, an economical indicator for maintenance performance. The Montecarlo simulation describes different failure modes of equipment and uses the prioritization of maintenance supplied, the availability of labour and spare parts. A Genetic algorithm is used for optimisation. The well-known Tennessee Eastman Plant problem is used to illustrate the results. Keywords: Preventive maintenance, Maintenance optimization, Montecarlo simulation
Maintenance can be defined as all actions appropriate for retaining an item/part/equipment in, or restoring it to a given condition (Dhillon, 2002). More specifically, maintenance is used to repair broken equipments, preserve equipment conditions and prevent their failure, which ultimately reduces production loss and downtime as well as the environmental and the associated safety hazards. It is estimated that a typical refinery experiences about 10 days downtime per year due to equipment failures, with an estimated economic lost of $20,000-$30,000 per hour (Tan and Kramer, 1997). In the age of high competition and stringent environmental and safety regulations, the perception for maintenance has been shifted from a “necessary evil” to an effective tool to increase profit, from a supporting part to an integrated part of the production process. Effective and optimum maintenance has been the subject of research both in academy and in industry for a long time. There is a very large literature on maintenance methods, philosophies and strategies. In addition, there is a large number of Computerized Maintenance Management Systems (CMMS) software packages devoted to help managing / organizing the maintenance activities. Despite this abundance, the optimization of decision variables in maintenance planning like preventive maintenance frequency or spare parts inventory policy, is usually not discussed in textbooks nor included as a capability of the software packages. Nonetheless, it has been extensively studied in academic research: Many models were discussed and summarized in the excellent textbook by Wang and Pham (2006)] and various review papers, e.g. Wang (2002). Most of the models are deterministic models obtained by making use of simplified assumptions, which allow the use of mathematical programming techniques to solve. The most common optimization criterion is minimum cost and the constraints are requirements on system reliability measures: availability, average uptime or downtime. More complex maintenance models that consider simultaneously many decision variables like preventive maintenance (PM) time interval,
Nguyen & Bagajewicz
labor workforce size, resources allocation are usually solved by Genetic algorithm (e.g. Sum and Gong, 2006; Saranga, 2004). Monte Carlo simulation is usually used to estimate reliability parameters in the model. Tan and Kramer (1997) utilized both Monte Carlo simulation and GA. None of preventive maintenance planning models considers constraints on resources available in process plants, which include labor and materials (spare parts). For example, the maintenance work force, which is usually limited, cannot perform scheduled PM tasks for some equipments at scheduled PM time because of the need to repair other failed equipments. Such dynamic situations can...
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