Dávid Gyulai
Dávid Gyulai
Fraunhofer PMI, MTA SZTAKI
Verified email at sztaki.mta.hu
TitleCited byYear
Capacity management for assembly systems with dedicated and reconfigurable resources
D Gyulai, B Kádár, A Kovács, L Monostori
CIRP Annals 63 (1), 457-460, 2014
372014
Milkrun vehicle routing approach for shop-floor logistics
D Gyulai, A Pfeiffer, T Sobottka, J Váncza
Procedia CIRP 7, 127-132, 2013
362013
Matching demand and system structure in reconfigurable assembly systems
D Gyulai, Z Vén, A Pfeiffer, J Váncza, L Monostori
Procedia CIRP 3, 579-584, 2012
202012
Robust production planning and capacity control for flexible assembly lines
D Gyulai, B Kádár, L Monosotori
IFAC-PapersOnLine 48 (3), 2312-2317, 2015
182015
Robust production planning and control for multi-stage systems with flexible final assembly lines
D Gyulai, A Pfeiffer, L Monostori
International Journal of Production Research 55 (13), 3657-3673, 2017
172017
Design and management of reconfigurable assembly lines in the automotive industry
M Colledani, D Gyulai, L Monostori, M Urgo, J Unglert, F Van Houten
CIRP Annals 65 (1), 441-446, 2016
162016
Capacity planning and resource allocation in assembly systems consisting of dedicated and reconfigurable lines
D Gyulai, B Kádár, L Monostori
Procedia Cirp 25, 185-191, 2014
162014
Manufacturing lead time estimation with the combination of simulation and statistical learning methods
A Pfeiffer, D Gyulai, B Kádár, L Monostori
Procedia CIRP 41, 75-80, 2016
132016
An integrated framework for design, management and operation of reconfigurable assembly systems
M Manzini, J Unglert, D Gyulai, M Colledani, JM Jauregui-Becker, ...
Omega 78, 69-84, 2018
92018
Capacity management of modular assembly systems
D Gyulai, L Monostori
Journal of Manufacturing Systems 43, 88-99, 2017
92017
Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer
L Lingitz, V Gallina, F Ansari, D Gyulai, A Pfeiffer, L Monostori
PROCEDIA CIRP 72, 1051-1056, 2018
82018
Simulation-based production planning and execution control for reconfigurable assembly cells
D Gyulai, A Pfeiffer, B Kádár, L Monostori
Procedia CIRP 57, 445-450, 2016
82016
Lead time prediction in a flow-shop environment with analytical and machine learning approaches
D Gyulai, A Pfeiffer, G Nick, V Gallina, W Sihn, L Monostori
IFAC-PapersOnLine 51 (11), 1029-1034, 2018
72018
Improving the Accuracy of Cycle Time Estimation for Simulation in Volatile Manufacturing Execution Environments
S Wenzel, T Peter
Simulation in Produktion und Logistik 2017, 413, 2017
52017
Simulation-based flexible layout planning considering stochastic effects
D Gyulai, Á Szaller, ZJ Viharos
Procedia CIRP 57, 177-182, 2016
52016
Scheduling and operator control in reconfigurable assembly systems
D Gyulai, B Kádár, L Monostori
Procedia Cirp 63, 459-464, 2017
42017
Industry 4.0: mining physical defects in production of surface-mount devices
F Tavakolizadeh, JÁC Soto, D Gyulai, C Beecks
ibai-publishing, 2017
32017
Order-stream-oriented system design for reconfigurable assembly systems
D Gyulai, Z Vén
University of Pannonia, 2012
32012
An online machine learning framework for early detection of product failures in an Industry 4.0 context
JA Carvajal Soto, F Tavakolizadeh, D Gyulai
International Journal of Computer Integrated Manufacturing 32 (4-5), 452-465, 2019
22019
Towards joint optimization of product design, process planning and production planning in multi-product assembly
D Tsutsumi, D Gyulai, A Kovács, B Tipary, Y Ueno, Y Nonaka, L Monostori
CIRP Annals 67 (1), 441-446, 2018
22018
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