An Evolutionary Simulation-Optimization Approach to Product-Driven Manufacturing Control

The presently reported research proposes an adaptive manufacturing scheduling and control framework that exploits the challenging combination of the main capabilities of product-driven control paradigm and online simulation-optimization approaches. Mainly, the proposed approach employs a scheduling rule-based evolutionary simulation-optimization strategy to dynamically select the most appropriate local decision policies to be used by the agentified manufacturing system components. In addition, this approach addresses products and machines agents' local decisional efficiency issues by dynamically adapting their behaviour to the fluctuations of the manufacturing system state. The main motivation of the developed hybrid intelligent system framework is the realization of an effective and efficient distributed dynamic scheduling and control strategy, that enhances manufacturing system reactivity, flexibility and fault-tolerance, as well as maintaining global behavioural stability and optimality. In order to assess the significance of the proposed approach, a proof of proposal prototype implementation is presented and a series of numerical benchmarking experiments are discussed.