[1]
J. Mula, D. Peidro, M. Díaz-Madroñero, E. Vicens, Mathematical programming models for supply chain production and transport planning, European Journal of Operational Research 204 (2010) 377-390.
DOI: 10.1016/j.ejor.2009.09.008
Google Scholar
[2]
J.T. Lin, C.M. Chen, Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing, Simulation Modelling Practice and Theory 51 (2015) 100-114.
DOI: 10.1016/j.simpat.2014.10.008
Google Scholar
[3]
J.H. Ge, H. Gao, Y.P. Wang, P.Q. Fu, C.T. Zhang, Research on Optimization Method of Real-time Available Resources for Dynamic Scheduling, International Journal of Database Theory & Application 7 (2014) 91-98.
DOI: 10.14257/ijdta.2014.7.2.09
Google Scholar
[4]
J. Lee, B. Bagheri, H.A. Kao, A Cyber-Physical Systems architecture for Industry 4. 0-based manufacturing systems, Manufacturing Letters 3 (2015) 18-23.
DOI: 10.1016/j.mfglet.2014.12.001
Google Scholar
[5]
J. Jungwattanakit, M. Reodecha, P. Chaovalitwongse, F. Werner, Algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria, The International Journal of Advanced Manufacturing Technology 37 (2008).
DOI: 10.1007/s00170-007-0977-0
Google Scholar
[6]
D. Quadt, H. Kuhn, A taxonomy of flexible flow line scheduling procedures, European Journal of Operational Research 178 (2007) 686-698.
DOI: 10.1016/j.ejor.2006.01.042
Google Scholar
[7]
C.H. Papadimitriou, Computational complexity, in: Encyclopedia of Computer Science, John Wiley and Sons Ltd., Chichester, 2003, pp.260-265.
Google Scholar
[8]
V. Gondek, Hybrid flow shop scheduling mit verschiedenen Restriktionen: Heuristische Lösung und LP-basierte untere Schranken, Dissertation, University of Duisburg-Essen, (2011).
Google Scholar
[9]
T.E. Morton, D.W. Pentico, Heuristic scheduling systems: with applications to production systems and project management, Wiley, New York, (1993).
Google Scholar
[10]
J. Hartmann, T. Makuschewitz, E.M. Frazzon, B. Scholz-Reiter, A genetic algorithm for the integrated scheduling of production and transport systems, in: S. Helber, M. Breitner, D. Rösch, C. Schön, J. -M. Graf von der Schulenburg, P. Sibbertsen, M. Steinbach, S. Weber, A. Wolter (Eds. ), Operations Research Proceedings 2012. Springer, Berlin/Heidelberg, 2013, pp.533-539.
DOI: 10.1007/978-3-319-00795-3
Google Scholar
[11]
M. Kück, B. Scholz-Reiter, A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands, Proceedings of the 12th International Conference on Machine Learning and Applications (ICMLA 2013), IEEE Press, 2013, pp.160-165.
DOI: 10.1109/icmla.2013.183
Google Scholar
[12]
M. Freitag, T. Hildebrandt, Automatic design of scheduling rules for complex manufacturing systems by multi-objective simulation-based optimization, submitted to CIRP-Annals - Manufacturing Technology, (2016).
DOI: 10.1016/j.cirp.2016.04.066
Google Scholar
[13]
S. Mehta, R. Uzsoy, Predictable scheduling of a single machine subject to breakdowns, International Journal of Computer Integrated Manufacturing, 12 (1999) 15–38.
DOI: 10.1080/095119299130443
Google Scholar
[14]
S. Janak, X. Lin, C. Floudas, A new robust optimization approach for scheduling under uncertainty II. Uncertainty with known probability distribution, Computers & Chemical Engineering, 31 (2007) 171-195.
DOI: 10.1016/j.compchemeng.2006.05.035
Google Scholar
[15]
S. Van de Vonder, F. Ballestin, E. Demeulemeester, W. Herroelen, Heuristic procedures for reactive project scheduling, Computers & Industrial Engineering, 52 (2007) 11-28.
DOI: 10.1016/j.cie.2006.10.002
Google Scholar
[16]
D. Ouelhadj, S. Petrovic, A survey of dynamic scheduling in manufacturing systems, Journal of scheduling, 12 (2008) 417-431.
DOI: 10.1007/s10951-008-0090-8
Google Scholar
[17]
F. Longo, Emergency simulation: state of the art and future research guidelines, SCS M&S Magazine, 1 (2010) 2010-04.
Google Scholar
[18]
J.F. O'kane, J.R. Spenceley, R. Taylor, Simulation as an essential tool for advanced manufacturing technology problems, Journal of Materials Processing Technology, 107 (2000) 412-424.
DOI: 10.1016/s0924-0136(00)00689-0
Google Scholar
[19]
J. Banks, J.S. Carson, B.L. Nelson, D.M. Nicol, Discrete-Event System Simulation, Prentice hall, New York, (2000).
Google Scholar
[20]
S.K. Jain, V.P. Singh, Water resources systems planning and management, Elsevier, (2003).
Google Scholar
[21]
F. Pirard, S. Iassinovski, F. Riane, A simulation based approach for supply network control, International Journal of Production Research, 49 (2011) 7205-7226.
DOI: 10.1080/00207543.2010.518726
Google Scholar
[22]
W. Krug, T. Wiedemann, J. Liebelt, B. Baumbach, Simulation and optimization in manufacturing, organization and logistics, in: Proceedings 14th European Simulation Symposium, SCS Europe BVBA, (2002).
Google Scholar
[23]
M.C. Fu, Optimization for simulation: Theory vs. practice. INFORMS Journal on Computing, 14 (2002) 192-215.
Google Scholar
[24]
L. März, W. Krug, O. Rose, G. Weigert, Simulation und Optimierung in Produktion und Logistik. Praxisorientierter Leitfaden mit Fallbeispielen, Springer, Heidelberg, (2011).
DOI: 10.1007/978-3-642-14536-0
Google Scholar
[25]
H.P. Wiendahl, J.W. Breithaupt, Modelling and controlling the dynamics of production systems, Production planning & control, 10 (1999) 389-401.
DOI: 10.1080/095372899233136
Google Scholar
[26]
V.V. Prabhu, N.A. Duffie, Nonlinear dynamics in distributed arrival time control of heterarchical manufacturing systems. IEEE Transactions on Control Systems Technology, 7 (1999) 724-730.
DOI: 10.1109/87.799673
Google Scholar