During the recent years, development of information technology caused to develop a
new industrial system which is called e-Manufacturing system. Thanks to the webenabled
manufacturing technologies, the lead times are being minimized to their
extreme level, and the minimum amount of inventory is kept, though the products are
being made-to order. Under these circumstances, achieving near-zero downtime of the
plant floor’s equipments is a crucial factor which mitigates the risk of facing unmet
demands. Many researches carried out to schedule maintenance actions in short term,
but none of them have utilized all of planning horizon to spread maintenance actions
along available time. In this research a method of enhanced maintenance scheduling of
multi-component e-Manufacturing systems has been developed. In this multi-component
system, importance of all machines is considered and the benefit of the entire system in
term of produced parts is taken into account (versus benefits of single machine). In
proposed system, the predicted machines degradation information, online information
about work in process (WIP) inventory (at inventory buffer of each work station) as well as production line’s dynamism are taken into account. All of makespans of planning
horizon have been utilized to improve scheduling efficiency and operational
productivity by maximizing the system throughputs. A state-of-the-art method which is
called simulation optimization has been utilized to implement the proposed scheduling
method. The production system is simulated by ProModel software. It plays the role of
objective function of the maintenance scheduling optimization problem. Using a
production related heuristic method which is called system value method, the value of
each workstation is determined. These values are used to define the objective function’s
parameters. Then, using genetic algorithm-based software which is called SimRunner
and has been embedded by ProModel, the scheduling optimization procedure is run to
find optimum maintenance schedule. This process is carried out for nine generated
scenarios. At the end, the results are benchmarked by two commonly used maintenance
scheduling methods to magnify the importance of proposed intelligent maintenance
scheduling in the multi-component e-Manufacturing systems. The results demonstrate
that the proposed optimal maintenance scheduling method yields much better system
value rather than sequencing methods. Furthermore, it indicates that when the mean time
to repairs are longer, this method is more efficient. The results in the simulated testbed
indicate that the developed scheduling method using simulation optimization functions
properly and can be applied in other cases
