Abstract
The planning and control of production processes is one of the main tasks in manufacturing systems. The consideration of inventory levels and availability of machines within production planning adds complexity and stochasticity. This challenge can be handled by means of a proper data exchange between the manufacturing system and the control system, allowing for the adaptation in to dynamic changes. In this context, this paper proposes a data-driven adaptive simulation-based optimization method that integrates inventory, production and maintenance control, optimizing job sequencing decisions according to the current system state in real-time. The new method achieved higher performance in real-world scenarios in comparison to available benchmarks, allowing for adaptive handling of changes in the manufacturing system.
Published Version
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