Development of machine learning‐based real time scheduling systems: using ensemble based on wrapper feature selection approach

Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments

“…The roots of this work are in the models developed by Priore et al (2001Priore et al ( , 2003Priore et al ( , 2006Priore et al ( , 2010, which use different machine learning techniques for automatically modifying the dispatching rules of flexible manufacturing systems over time. Shiue, Guh, and Lee (2012) review similar approaches in the literature. These works show that this dynamic approach is able to produce breakthrough improvements in performance over the same rules applied statically.…”

A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment

“…A dynamic scheduling KB can provide fast and acceptable solutions, allowing the shop floor to make immediate decisions and the system to meet the operational characteristics required of a dynamic production system [16]. Three major machine learning classification methods for establishing a dynamic scheduling KB were used in earlier research [22]: neural networks [24], C4.5 decision trees [25], and support vector machines [26]. Many studies [27][28][29][30] apply the above machine learning classification methods to develop dynamic scheduling in flexible manufacturing systems (FMSs) and wafer fabrication (FAB) environments.…”

“…In a dynamic scheduling mechanism, various scheduling rules are applied in a dynamic and multi-pass manner to select the nearest-to-optimal dispatching (i.e., scheduling) strategy among the feasible alternatives at each scheduling decision point and thereby satisfy shop floor performance criteria [14,15]. Previous studies [16,17] have indicated that dynamic scheduling includes two main approaches: the multi-pass simulation technique [18,19] and the machine learning technique [20][21][22][23]. Multi-pass simulations examine candidate scheduling rules and select the best strategy according to simulation information, such as the current state of the system and performance criteria for each scheduling interval.…”

Learning-based scheduling of flexible manufacturing systems using ensemble methods

“…The mathematical formulation of a learning-based scheduling system can be found in Park, Raman, and Shaw (1997) and Shiue et al (2012).…”

“…It should be highlighted that to date sets of classifiers have been hardly employed in FMSs. One of the few studies is that by Shiue, Guh, and Lee (2012), who use the bagging method. These authors justify that this method fits better with the nature of the scheduling problem in real time than the boosting method, given that there is a substantial classification noise.…”