Optimization of physical flows in an automotive manufacturing plant: some experiments and issues

Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges

“…Intelligent tools such as expert systems, fuzzy systems and neural networks, were developed to support the design process of the flexible manufacturing system. (Muhl et al 2003) proposed for the automotive industry a way to optimize, in a centralized way, the schedule of the car assembly line according to a unique performance indicator and the determination of the pertinent parameters which were periodically recalculated to assure the best synchronization between the real shop-floor state and the new schedule. Another way to design this indicators system could be found using learning mechanisms as neural networks, fuzzy approaches or Choquet integrals usage .…”

Supervisory synthesis for product-driven automation and its application to a flexible assembly cell

“…In this case, reconfiguration requires the tuning or the selection of already designed control rules. The main benefit is the flexibility of control policies but these approaches often suffer from an inevitable state explosion problem related to the number of machines and product variability (Muhl et al 2003).…”

Hybrid Job Shop and Parallel Machine Scheduling Problems: Minimization of Total Tardiness Criterion

“…That is why we have decided to use the simulation based optimization technique as it has been proved to be effective for such kind of applications. Indeed, simulation is more and more used in today's industries with the aim of assessing their systems or to study the impact of changing system design parameters, Muhl et al, (2003) and Sahlin et al, (2004). ARENA®, developed by Systems Modelling Corporation, is Hybrid Job Shop and parallel machine scheduling problems: minimization of total tardiness criterion 285…”