2025
From Scenario Farming to Learning: A Modular Low-Code Framework for Decision Support in Scheduling
Madlene Leißau, Christoph Laroque • Winter Simulation Conference 2025, Seattle (USA)
Modern manufacturing environments, such as semiconductor manufacturing, require agile, data-driven decision support to cope with increasing system complexity. Discrete event simulation (DES) is a key method for evaluating scheduling strategies under uncertainty. Building on a previously introduced low-code framework for scenario farming, this paper presents an extended and evolving approach that integrates machine learning (ML) to enhance scheduling decision support. The framework automates model generation, distributed experimentation, and systematic scenario data collection, providing the basis for training data-driven decision models. Using the Semiconductor Manufacturing Testbed 2020 as a reference, initial experiments demonstrate how simulation-based insights can be transformed into intelligent scheduling aids. Key features include model structure synchronization across simulation tools, automated experiment design, and modular integration of learning components, providing a first step towards adaptive, simulation-driven decision support systems.
At Odds by Design: How Integration Barriers Shape Immersive Digital Twin Systems
Adrian Rössl, Madlene Leißau, Christoph Laroque • ASIM SPL 2025, Dresden
This work explores the development of a Digital Twin for an automated sorting system, combining real-time data integration via cloud services with immersive visualization in a virtual environment. The use case demonstrates how physical system behavior can be mirrored and monitored digitally, enabling interaction, remote insights, and enhanced user experiene. This work aims to highlight the integration challenges for different technologies. Key challenges included ensuring smooth communication, low latency, and consistent system representation across platforms.
Fast-Track Your Decisions: Leveraging Low Code to Speed Up Simulation-Driven Insights in Semiconductor Industry
Madlene Leißau, Adrian Rössl, Christoph Laroque • CIRP CMS 2025, Enschede (Niederlande)
Low-code approaches can accelerate decision-making in the semiconductor industry by streamlining simulation-driven insights. This supports the paradigm shift to Industry 4.0 and Industry 5.0 by enabling rapid development and optimized workflows. However, existing simulation methods often require extensive coding expertise, limiting accessibility and slowing down model development. This paper presents a simulation template that streamlines the development of discrete event simulation models in semiconductor manufacturing. Thus, the simulation template implements reusable components to simplify model creation and reduce development time. The approach encourages collaboration between technical and nontechnical stakeholders. Combined with a low-code data farming framework, the simulation template increases agility, accelerates experimentation, and supports efficient, data-driven production planning decisions.