Reinforcement learning (RL) usage in the industrial domain is on the rise since the shift towards Industry4.0 systems. The need for faster adaptive systems is encouraging manufacturers to invest more in artificial intelligence (AI) technologies. Our aim is to add better intelligence into simulation tools by embedding RL capabilities. Discrete event simulations have been used for decision support in manufacturing systems for decades. New simulation tools such as AnyLogic have improved significantly in the past few years. In this paper, we built a RL library for AnyLogic simulation models. The RL library is developed for model designers, who may not be experts in the field of RL. We applied our RL library on a real-world use case model for truck dispatching problem. The results show the benefits of using RL in real-world problems to find better dispatching policies. Additionally, the visualization capabilities of AnyLogic enabled us to explain the RL agent's reaction to changes in the system.

Digital equivalence is the main objective of Digital Twin (DT)s, and simulation is an integral part. DTs reach beyond traditional simulation with the help of real-time synchronization through Industrial Internet of Things (IIoT) technologies. Simulation supports off-line experimentations and planning, while DTs offer synchronous execution and modification. DTs help to understand “what may happen”. Also, they present “what is happening” and its management methodologies. In this paper, we present building aspects and an integration approach for a Digital Twin based Discrete-Event Simulation model. Our approach utilizes a data-driven agent-based simulation within a DT framework. It presents an integration layer that provides two essential features: reconfiguration and state initialization. It gives simulation models configurability and integrity that are required for operating within a DT. Our proposed approach is presented through a use case of a semiconductor manufacturing system. The proposed integration layer extends the usability of current Discrete-Event Simulation (DES) for a DT within a Cyber-Physical Production System.

Modern digital systems require one or more frequency shaping modules that realize certain transfer functions, usually in the form of digital filters. Breaking up the design into multiple stages results in reducing the implementation area and power consumption. However, there is no well-defined automation methodology to design an optimum multistage multirate filter. This article proposes a new design flow automation tool using a hybrid sampling strategy-based differential evolution (HSS-DE). The user specifies the inputs and outputs of the system, including the input spectrum, the sample rate change, the magnitude and phase responses, and the required signal-to-noise ratio. The tool uses this information to generate a solution indicating the number of stages and the filters' specifications for each stage such that the generated architecture satisfies the user requirements. We compared the proposed tool to subsystems designed by experts in the ASIC industry. Several case studies are presented to demonstrate the proposed tool's efficiency in different design problems. The case studies include decimation filters for sigma-delta (ΣΔ) analog-to-digital converters (ADCs) and interpolation filters for wideband transmitters such as wideband code division multiple access (WCDMA). The case studies' results indicate that the proposed design automation flow reduces the design time remarkably, in addition to reducing the implementation in all cases.