Automated model generation framework for material flow simulations of production systems

Abstract

Owing to increasingly important drivers for change, such as automation, digitisation, and dynamic demand patterns, simulation models of production systems become outdated rapidly. At the same time, building simulation models often requires much time, cost, and expertise, especially when dealing with complex job shop production systems. To address these challenges, an automated simulation model generation (ASMG) framework for material flow simulation of production systems is presented. This framework contains multiple approaches to infer routeing, control and temporal aspects from event-based data. To achieve this, methods from process mining (PM) and machine learning (ML) are applied. Additionally, the suitability of Coloured Petri Nets (CPNs) to serve as conceptual and operational simulation models is examined. The inferred simulation models have high validity when compared to the real system concerning the KPIs machine utilisation, throughput, and work in process. It is shown, that most model elements can be inferred very well, in particular process routes, processing times, and resource selection rules. This proof of concept serves as a foundation for research on detection approaches inferring further model elements such as setup times accurately.

Keywords:

• Simulation
• process mining
• Petri nets
• machine learning
• automated model generation framework

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article. Due to the nature of the research, due to legal restrictions on the use case data, further supporting data is not available.

Additional information

Notes on contributors

Marvin Carl May

Marvin Carl May is a researcher at the Institute for Production Science (wbk) at the Karlsruhe Institute of Technology (KIT). His research concerns production system optimisation on strategical, tactical and operational levels by means of intelligent algorithms. The focus lies on discrete manufacturing, in close cooperation with industry, e.g. semiconductor manufacturing, and aims at researching and implementing innovative and application-oriented solutions to technological problems.

Christian Nestroy

Christian Nestroy studied Industrial Engineering and Management at Karlsruhe Institute of Technology (KIT, Germany), University of Southern Denmark, and Ilmenau University of Technology (Germany).

Leonard Overbeck

Leonard Overbeck studied Industrial Engineering and Management at the Karlsruhe Institute of Technology (KIT), Universidad de Barcelona (Spain), and Virginia Tech (USA). He is a research assistant at wbk Institute of Production Science since 2019 and conducts his Ph.D. in the field of production system planning.

Gisela Lanza

Gisela Lanza is member of the management board at the Institute of Production Science (wbk) of the Karlsruhe Institute of Technology (KIT). She heads the Production Systems division dealing with the topics of global production strategies, production system planning, and quality assurance in research and industrial practice. Her research focus is on the holistic design and evaluation of production systems. The methodological approach includes the use of quantitative methods to increase efficiency. In addition, a special focus is placed on data-driven planning and control of production networks in order to translate corporate strategy into tactical and operative network design.