Real-Time Scheduling of A Flexible Manufacturing System Using A Two-Phase Machine Learning Algorithm

The static and analytic scheduling approach is very difficult to follow and is not always applicable in real-time. most of the scheduling algorithms are designed to be established in offline environment. however, we are challenged with three characteristics in real cases: first, problem data of jobs are not known in advance. second, most of the shop’s parameters tend to be stochastic. third, thousands of jobs should be scheduled in a long planning horizon. in this work we designed an expert model for achieving better performance of real-time scheduling tasks in a flexible manufacturing system (fms). the proposed expert model is comprised of two sets of modules, namely fms simulator and decision (control) modules. information is translated from the first set of modules to the second in two phases. first, a feed-forward neural network as a supervised machine learning mechanism is set to capture the queueing attributes of the shop and train in initialization and pre-run mode. second, system states (in real run) are interpreted to the control module which is comprised of interconnected online learning activation function and a feed-forward neural net, and finally the best strategy is selected. therefore, an interactive discrete-event simulation model with control module is implemented in order to evaluate different scenarios and reduce the computational time and complexity. eventually, the presented procedure is benchmarked through simulation modeling of a triple-stage-triple-machine flexible flow shop with some embedded stochastic concept. results support our proposed methodology and follow our overall argument.