O. Battaïa and A. Dolgui, A taxonomy of line balancing problems and their solutionapproaches, International Journal of Production Economics, vol.142, issue.2, pp.259-277, 2013.
DOI : 10.1016/j.ijpe.2012.10.020

O. Battaïa, A. Dolgui, and N. Guschinsky, Integrated process planning and system configuration for mixed-model machining on rotary transfer machine, International Journal of Computer Integrated Manufacturing, vol.30, issue.9, 2015.
DOI : 10.1080/0951192X.2013.874585

O. Battaïa, A. Dolgui, and N. Guschinsky, Decision support for design of reconfigurable rotary machining systems for family part production, International Journal of Production Research, vol.29, issue.4, pp.1368-1385, 2017.
DOI : 10.1016/j.jmsy.2012.12.003

N. A. Boysen, M. Fliedner, and A. Scholl, A classification of assembly line balancing problems, European Journal of Operational Research, vol.183, issue.2, pp.674-693, 2007.
DOI : 10.1016/j.ejor.2006.10.010

J. Bukchin and J. Rubinovitz, A weighted approach for assembly line design with station paralleling and equipment selection, IIE Transactions, vol.8, issue.1, pp.73-85, 2003.
DOI : 10.1080/00207548908942531

A. Chube, L. Benyoucef, and M. K. Tiwari, An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system, Journal of Intelligent Manufacturing, vol.56, issue.2, pp.1141-1155, 2012.
DOI : 10.1007/3-540-29397-3_6

X. Delorme, A. Dolgui, and M. Y. Kovalyov, Combinatorial design of a minimum cost transfer line, Omega, vol.40, issue.1, pp.31-41, 2012.
DOI : 10.1016/j.omega.2011.03.004

URL : https://hal.archives-ouvertes.fr/emse-00710313

A. Dolgui, N. Guschinsky, and G. Levin, Graph approach for optimal design of transfer machine with rotary table, International Journal of Production Research, vol.2, issue.2, pp.321-341, 2009.
DOI : 10.1287/mnsc.32.4.430

URL : https://hal.archives-ouvertes.fr/hal-00387687

S. Erol, A. Jäger, P. Hold, K. Ott, and W. Sihn, Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production, Procedia CIRP, vol.54, pp.13-18, 2016.
DOI : 10.1016/j.procir.2016.03.162

D. Ivanov, B. Sokolov, and A. Dolgui, Applicability of optimal control theory to adaptive supply chain planning and scheduling, Annual Reviews in Control, vol.36, issue.1, pp.73-84, 2012.
DOI : 10.1016/j.arcontrol.2012.03.006

URL : https://hal.archives-ouvertes.fr/emse-00693526

D. Ivanov, B. Sokolov, A. Dolgui, F. Werner, and M. Ivanova, A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0, International Journal of Production Research, vol.11, issue.3, pp.386-402, 2016.
DOI : 10.1016/j.artint.2010.09.009

URL : https://hal.archives-ouvertes.fr/emse-01109312

M. Kumar, G. Graham, P. Hennelly, and J. Srai, How will smart city production systems transform supply chain design: a product-level investigation, International Journal of Production Research, vol.53, issue.1, pp.54-7181, 2016.
DOI : 10.1086/227496

G. J. Kyparisis and C. P. Koulamas, Flexible flow shop scheduling with uniform parallel machines, European Journal of Operational Research, vol.168, issue.3, pp.985-997, 2006.
DOI : 10.1016/j.ejor.2004.05.017

S. Lee, Y. Kang, and &. V. Prabhu, Smart logistics: distributed control of green crowdsourced parcel services, International Journal of Production Research, vol.54, issue.23, pp.6956-6968, 2016.
DOI : 10.1109/87.799673

E. B. Lee and L. Markus, Foundations of optimal control theory, 1967.

G. Levin, B. A. Rozin, and . Dolgui, Optimization of the Structure and Execution Modes of Intersecting Operation Sets. IFAC-PapersOnLine, pp.49-61, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01435894

N. N. Moiseev, Element of the optimal systems theory, Russian]. Moscow: Nauka, 1974.

L. Mönch, J. W. Fowler, and S. Mason, Production Planning and Control for Semiconductor Wafer Fabrication Facilities: Modeling, Analysis, and Systems, 2012.
DOI : 10.1007/978-1-4614-4472-5

A. Nayak, R. Reyes-levalle, S. Lee, &. Shimon, and Y. Nof, Resource sharing in cyber-physical systems: modelling framework and case studies, International Journal of Production Research, vol.2012, issue.1, pp.6969-6983, 2016.
DOI : 10.1016/j.ijpe.2010.10.002

T. D. Oesterreich and F. Teuteberg, Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry, Computers in Industry, vol.83, pp.121-139, 2016.
DOI : 10.1016/j.compind.2016.09.006

J. Otto, S. Henning, and O. Niggemann, Why Cyber-physical Production Systems Need a Descriptive Engineering Approach ??? A Case Study in Plug & Produce, Procedia Technology, vol.15, pp.295-302, 2014.
DOI : 10.1016/j.protcy.2014.09.083

D. N. Tahar, F. Yalaoui, C. Chu, and L. Amodeo, A linear programming approach for identical parallel machine scheduling with job splitting and sequence-dependent setup times, International Journal of Production Economics, vol.99, pp.1-2, 2006.

A. Theorin, K. Bengtsson, J. Provost, M. Lieder, C. Johnsson et al., An event-driven manufacturing information system architecture for Industry 4.0, International Journal of Production Research, vol.21, issue.99, pp.1297-1311, 2017.
DOI : 10.1109/MC.2011.56

S. Weyer, M. Schmitt, M. Ohmer, and D. Gorecky, Towards Industry 4.0 - Standardization as the crucial challenge for highly modular, multi-vendor production systems, IFAC-PapersOnLine, vol.48, issue.3, pp.579-584, 2015.
DOI : 10.1016/j.ifacol.2015.06.143

R. Y. Zhong, C. Xu, C. Chen, and G. Q. Huang, Big Data Analytics for Physical Internet-based intelligent manufacturing shop??floors, International Journal of Production Research, vol.30, issue.2, pp.2610-2621, 2017.
DOI : 10.1016/j.ijpe.2015.02.014