An expert system for assessing the relative importance of maintenance work requests
This paper presents the methods, procedures, and structure of a knowledge-based artificial intelligence (AI) program, called the Maintenance Priority Generator (MPG), for assessing the relative production importance of maintenance work requests in a large discrete parts manufacturing facility. The objective is to aid the allocation of limited maintenance resources in accordance with the plant mission and stated policies regarding environmental, safety, security, and other issues. The program takes input from the maintenance workorder database, applies expert system technology to generate relative importance numbers, and puts the numbers back into the database. Subsequently, and individual maintenance foreman's work pending list can be printed out in importance order. The program is not meant to be a scheduler because it does not have access to resource availability data, but could provide very useful input to a scheduler program. The MPG program was written for use in the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee, operated by Martin Marietta Energy Systems, Inc. for the Department of Energy. The Plant employs more than 6,000 workers in seven major divisions, scattered throughout more than 200 buildings. The maintenance workorder backlog typically runs to 25,000 or more entries, making close management and review imperative. The MPG program is highly specific to the Y-12 Plant, but the general approach is applicable to a wide range of manufacturing situations.
- Research Organization:
- Oak Ridge Y-12 Plant, TN (USA)
- Sponsoring Organization:
- USDOE; USDOE, Washington, DC (USA)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 5569039
- Report Number(s):
- Y/DW-884; ON: DE91013037
- Country of Publication:
- United States
- Language:
- English
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