Scheduling tasks in production facilities are usually
hybrid optimization
problems of a large combinatorial nature. They involve solving, in
near-real time, the integration of the operation of several batch
units of continuous dynamics with the discrete manufacture of items
in processing lines. Moreover, one has to deal with uncertainty (process
delays, unexpected stops) and the management of shared resources (energy,
water, etc.) including decisions made by plant operators: still, some
tasks in the scheduling layers are done manually. Manufacturing Execution
Systems (MESs) are intended to support plant personnel at this level.
However, there is still much work to do in terms of performing automatic
scheduling, computed in real time, that guides managers to achieve
an optimal operation of such complex cyber-physical systems. This
work proposes a closed-loop approach to handle the uncertainty arising
when facing the online scheduling of supply lines and parallel batch
units. These units often share some resources, so effects due to concurrent
resource consumption on the system dynamics are explicitly considered
in the presented formulation. The proposed decision support system
is tested onsite in a tuna cannery, to handle short-term online scheduling
of sterilization processes that deal with limited steam, carts, and
operators as shared resources.