Dr.-Ing. Pablo Oliveira Antonino

Dr. Pablo Oliveira Antonino is Head of the “Virtual Engineering” department of the Fraunhofer Institute for Experimental Software Engineering IESE in Kaiserslautern, Germany. He holds a PhD in Computer Science from Technische Universität Kaiserslautern and has experience with the design, evaluation, and integration of dependable embedded systems from various domains, such as automotive, avionics, agricultural and construction machines, medical devices, and smart industries. The Industry 4.0 middleware “BaSyx” is mainly being developed in the department managed by Dr. Antonino. --- Dr. Pablo Oliveira Antonino ist Leiter der Abteilung »Virtual Engineering« am Fraunhofer-Institut für Experimentelles Software Engineering IESE in Kaiserslautern. Er promovierte an der Technischen Universität Kaiserslautern in Informatik und hat Erfahrung im Bereich Entwurf, Evaluierung und Integration verlässlicher eingebetteter Systeme aus unterschiedlichen Domänen, wie Automotive, Avionik, Land- und Baumaschinen, medizinische Geräte und Smart Industries. Die Industrie-4.0-Middleware »BaSyx« wird hauptsächlich in der von Dr. Antonino geleiteten Abteilung entwickelt.

Simplifying Simulation Scenario Design and Execution: A Guide to Creating and Configuring FERAL Simulation Scenarios with YAML

Introduction Creating and configuring simulation scenarios is effort-intensive and time-consuming, mainly because each scenario requires a unique set of configurations, parameters, and settings, making the procedure time-consuming and error-prone. This complexity not only reduces productivity but also increases the learning…

Process Planning and Continuous Scheduling

Process Planning and Continuous Scheduling (Part 2)

In our last Fraunhofer IESE blog post, we introduced a holistic process planning and scheduling design called RL design, which addresses individualized production with small lot sizes. However, this design cannot deal with scheduling problems in the case of large…

Integrated Process Planning and Scheduling for Service-based Production with Deep Reinforcement Learning (Part 1)

Current industrial production scheduling approaches assume that process planning is performed before scheduling and that process plans are fully or at least partially available before scheduling starts. However, this is not the case in service-based production [5]. Service-based production provides…

Warum Software-defined Vehicles an Continuous Engineering in DevOps nicht vorbeikommen

Software-defined Cars oder auch Software-defined Vehicles charakterisieren, dass die Fahrzeugentwicklung – langjährigen Vorhersagen folgend – mittlerweile durch den Einsatz von Software dominiert ist. Die Begriffe beschreiben, dass sich andere Disziplinen nun an den Anforderungen der Software orientieren müssen und nicht…

Continuous Engineering (Continuous Planning and Continuous Budgeting)

Continuous Engineering for Industrie 4.0 (Part 2)

Future-proof decision making can be enabled by Continuous Planning and Continuous Budgeting with Continuous Integration of Digital Twins: Today we continue the series Continuous Engineering for Industrie 4.0, where we are exploring how continuous engineering practices should be instantiated to the automation domain to support Industrie 4.0 principles.

Continuous Engineering for Industrie 4.0

Continuous Engineering for Industrie 4.0 (Part 1)

Rolling out changes in complex systems is always a challenge. Regardless of whether a software component needs to be modified or whether a change in the communication network needs to be made, any change may lead to unexpected behavior. Continuous…