Improving Maintenance Scheduling through Knowledge Based Simulation (KBS
Improve)
Context
Maintenance is rapidly
becoming the largest indirect operating cost for manufacturing organisations.
The trend is set to continue as 'low cost' manufacturing methods place ever
greater emphasis on automation and reduced work-in-progress. A key element of
the maintenance process is the scheduling of maintenance tasks, which if
performed effectively reduces the loss of throughput caused by machine failures
and reduces the need for work-in-progress (WIP) to buffer the process from such
interruptions. Maintenance scheduling, however, proves to be an extremely
complex task that continues to be largely within the remit of a maintenance
supervisor. A means for improving the performance of supervisors in maintenance
scheduling would undoubtedly lead to reduced manufacturing costs and improved
throughput.
Aims
and Objectives
The objectives are:
to develop a mechanism for determining
the scheduling strategies of maintenance supervisors
to develop a means for determining the
effect of alternative decision making strategies on key performance measures of
the manufacturing system, particularly maintenance costs, WIP and throughput
to develop a means for improving
maintenance scheduling by comparing alternative decision making strategies
Method
The research is to be based
on a simulation modelling approach linked to artificial intelligence (AI)
systems that represent the maintenance supervisors' decision making strategies.
Initially a detailed model of a specific Ford facility is to be developed,
through which maintenance supervisors are to be presented with typical
maintenance scenarios. These scenarios and the resulting decisions will be
recorded and used to train the AI systems (neural networks and rule based
expert systems). The AI systems will then be used to develop an understanding
of the supervisors' decision making strategies, and in
co-operation with the simulation used to determine the effectiveness of the
various strategies and to look for improvements in those strategies.
Benefits
The
development of a methodology for improving maintenance supervisor decisions,
leading to increased throughput and reduced maintenance costs as well as a
reduced need for work-in-progress. A key advantage of the proposed approach is
that simulation models can be developed prior to the commissioning of a new
manufacturing facility and, therefore, maintenance supervisors could be trained
prior to start-up. The approach also has benefits for the wider community by
providing a means for modelling human decision-makers and a method for
improving their decision making strategies.
Partners
Ford Motor Company
Lanner Group
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