Peter Burggräf

Full Professor

Universität Siegen Naturwissenschaftlich-Technische Fakultät

Germany

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Peter Burggräf is a Full Professor at Universität Siegen Naturwissenschaftlich-Technische Fakultät in Germany. His research focuses on predictive methods in supply chains, machine learning applications in production management, and the integration of human expertise with artificial intelligence. Recent publications include studies on decision support systems, event-based vision in laser welding, and machine learning implementation in small and medium-sized enterprises.

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Collaborators (6)

Johannes Wagner

University of Siegen

GERMANY

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Ralf Mikut

Head of Research Area

Karlsruhe Institute of Technology

GERMANY

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Ognjen Radisic-Aberger

University of Siegen

GERMANY

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Fabian Steinberg

University of Siegen

GERMANY

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Till Moritz Saßmannshausen

University of Siegen

GERMANY

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Tobias Adlon

RWTH Aachen University

GERMANY

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Peter Burggräf

Research Interests

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About

Articles (23)

Event-based vision in laser welding: An approach for process monitoring

An analytical review of predictive methods for delivery delays in supply chains

Predicting schedule adherence of engineering changes – a case study on effectivity date adherence prediction using machine learning

A systematic review of machine learning for hybrid intelligence in production management

Hybrid intelligence – systematic approach and framework to determine the level of Human-AI collaboration for production management use cases

Bridging human expertise and machine learning in production management: a case study on ML-based decision support systems to prevent missing parts at assembly

Machine learning implementation in small and medium-sized enterprises: insights and recommendations from a quantitative study

AI-artifacts in engineering change management – a systematic literature review

gym-flp: A Python Package for Training Reinforcement Learning Algorithms on Facility Layout Problems

Enhancing brewery logistics with smart kegs: A simulation study

Application of stochastic risk simulation to increase depth of production planning

“Deep-Dive analysis of the latest Lithium-Ion battery safety testing standards and regulations in Germany and China”

Human trust in otherware – a systematic literature review bringing all antecedents together

Deciding on when to change – a benchmark of metaheuristic algorithms for timing engineering changes

Deep reinforcement learning for layout planning – An MDP-based approach for the facility layout problem

The Digital Factory Twin - An Empirical Study of Use Cases and Challenges,Der digitale Fabrikzwilling Eine empirische Studie zu Anwendungsfällen und Herausforderungen

Trust in artificial intelligence within production management – an exploration of antecedents

Achieving parametric transparency in model-based factory planning

Bibliometric Study on the Use of Machine Learning as Resolution Technique for Facility Layout Problems

Digital press shops: Data of an online survey among press shop experts

Fields of action towards automated facility layout design and optimization in factory planning - A systematic literature review

Machine Learning-Based Prediction of Missing Components for Assembly - a Case Study at an Engineer-to-Order Manufacturer

Uncovering the human evaluation of changeability for automated factory layout planning: an expert survey

Collaborators (6)

Johannes Wagner

Ralf Mikut

Ognjen Radisic-Aberger

Fabian Steinberg

Till Moritz Saßmannshausen

Tobias Adlon

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