Univ.-Prof. Dr.-Ing. Peter Burggräf

Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques

Optimize existing structures and workflows: Using a real-time locating system (RTLS) to enhance cleanliness in modern production areas

Uncovering the behaviour of facility layout problem solutions in relation to factory design applications

_Enhancing Resource Efficiency And Monetization In Metal Recycling Through Supply Chain-Wide Digitalization: An Approach For Single-Variety Metal Stream Optimization And CO2 Reduction

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

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

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

Towards Digital-Twin-Driven Factory Planning - A Systematic Review

Automatic Generation of Assembly Instructions by Analyzing Process Recordings - A Concept Overview

Plaftforms for factory planning,Plattformen für die fabrikplanung

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

Classification Of Flow-Based Assembly Structures For The Planning Of Flexible Mixed-Model Assembly