Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.
The application of a self-sensing piezoelectric transducer included in a bridge readout network is here investigated taking into account the electromechanical interaction. The parametric uncertainties and the piezoelectric device losses as well as the tolerances of the electric components make the balancing of the bridge difficult to achieve in practice. A loss and uncertainties compensation, based on a real-time software implementation of the bridge reference arm is here presented and validated. The standard electrical and the proposed electromechanical balancing are both theoretically and experimentally compared in the case of a simple beam. The bridge output signal that is proportional to the strain rate is then fed back by means of a sharp phase second order low pass filter which is aimed at increasing the damping of the first bending mode of the beam.
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