Flexible job-shop problem with predictive maintenance planning using genetic algorithm

The most common disruptions and emerging challenges that
manufacturing systems frequently encounter include the arrival of new
orders, last-minute order cancellations, unforeseen machine breakdowns,
and alterations in due dates. To effectively respond to these challenges,
production schedules are continually adjusted by implementing real-time
rescheduling mechanisms that rely on up-to-the-minute data from the
shop floor. This remarkable level of connectivity and the exchange of
real-time information are made possible through advanced initiatives
in manufacturing technologies and industrial informatics, supported by
the paradigm of Industry 4.0. In this paper we propose a genetic algo rithm approach that enables dynamic scheduling of production tasks and
maintenance activities considering the evolution degradation of machine
health status.