Global search algorithm for automated maintenance planning and scheduling of parts requests

The work content variability in a shop or field maintenance system is significant. The key to efficient maintenance operation is an effective diagnostic system that narrows the problem to a small ambiguity group, explicit mechanisms to leverage the knowledge of repairs and isolation tests that would be useful to fix the ambiguous fault condition and immediate communication with the parts store regarding availability of parts. Such scenario calls for the concept of dynamic optimization, which seeks optimal solutions to maintenance planning and scheduling problems subject to the dynamics such as outcomes of isolation tests or repair actions already execute to fix the ambiguous fault condition. This paper presents a global search algorithm to solve this dynamic optimization problem. An objective function is derived that takes into account the cost of parts that might be used for maintenance, the restocking fee for the parts that might be ordered and returned because they are not used, the labor cost of maintenance and the cost of waiting for ordered parts to be delivered. The global search algorithm was found to perform at satisfactory speeds for ambiguity groups containing up to five repairs. This covers majority of the cases in the field.