Cooperative control in production and logistics

Annual Reviews in Control

Volumn 39, Issue , 2015, Pages 12-29

  Monostori, László ^a,b   Valckenaers, Paul ^c   Dolgui, Alexandre ^d   Panetto, Hervé ^e,f   Brdys, Mietek ^g,h   Csáji, Balázs Csanád ^a  

^a INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

^b BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS   (Hungary)

^c Department of Health Care and Technology KHLeuven ^*  (Belgium)

^d ECOLE DES MINES DE SAINT ETIENNE   (France)

^e CNRS   (France)

^f UNIVERSITÉ DE LORRAINE   (France)

^g UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^h GDANSK UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONTROL THEORY; ENTERPRISE RESOURCE PLANNING; GAME THEORY; INTEROPERABILITY; LEARNING SYSTEMS; MANUFACTURE; MODEL PREDICTIVE CONTROL; MULTI AGENT SYSTEMS; POTABLE WATER; WATER DISTRIBUTION SYSTEMS; WATER SUPPLY SYSTEMS;

COOPERATIVE ENTERPRISE; COOPERATIVE GAME THEORY; DISTRIBUTED APPROACHES; DISTRIBUTED MACHINE LEARNING; INFORMATION AND COMMUNICATION TECHNOLOGIES; MANUFACTURING ENVIRONMENTS; RECONFIGURABLE SYSTEMS; THEORETICAL DEVELOPMENT;

NETWORKED CONTROL SYSTEMS;

EID: 84929048936     PISSN: 13675788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.arcontrol.2015.03.001     Document Type: Conference Paper

References (145)

1. D. Angeli, A. Casavola, and E. Mosca On feasible set-membership state estimators in constrained command governor control Automatica 37 1 2001 151 156
2. Arminski, K., & Brdys, M. A. (2013). Robust monitoring of water quality in drinking water distribution systems. In Procs 13th IFAC symposium on large scale complex systems: Theory and applications, Shanghai, China.
3. K. Arminski, T. Zubowicz, and M.A. Brdys Biochemical multi-specie quality model of drinking water distribution system for simulation and design International Journal of Applied Mathematics and Computer Science 23 3 2013 571 585
4. K.J. Åström, and R.M. Murray Feedback systems: An introduction for scientists and engineers 2008 Princeton University Press ISBN 0-691-13576-2
5. T. Aydinliyim, and G.L. Vairaktarakis Sequencing strategies and coordination issues in outsourcing and subcontracting operations Planning production and inventories in the extended enterprise Vol. 1 2011 Springer 269 319 ISBN 978-144-196-484-7
6. A.D. Baker A survey of factory control algorithms that can be implemented in a multi-agent heterarchy: Dispatching, scheduling, and pull Journal of Manufacturing Systems 17 4 1998 297 320
7. G. Barbarosoglu An integrated supplier-buyer model for improving supply chain coordination Production Planning and Control 11 8 2000 732 741
8. J.J. Bartholdi, and D.D. Eisenstein A production line that balances itself Operations Research 44 1 1996 21 34
9. R. Bekkerman, M. Bilenko, and J. Langford Scaling up machine learning: Parallel and distributed approaches 2012 Cambridge University Press
10. A. Bemporad, A. Casavola, and E. Mosca A predictive reference governor for constrained control systems Computers in Industry 36 1998 55 64
11. A. Bemporad, and E. Mosca Fulfilling hard constraints in uncertain linear systems by reference managing Automatica 34 4 1998 451 461
12. J. Berners-Lee, O. Hendler, and E. Lassila The semantic web Scientific American 284 5 2001 34 43
13. O.J. Blanchard The production and inventory behavior of the American automobile industry Journal of Political Economics 91 3 1983 365 400
14. Blondel, V. D., Hendrickx, J. M., Olshevsky, A., & Tsitsiklis J. (2005). Convergence in multiagent coordination, consensus, and flocking. In Procs of the 44th IEEE conference on decision and control (pp. 2996-3000).
15. D.L. Boccelli, M.E. Tryby, J.G. Uber, and R.S. Summers A reactive species model for chlorine decay and THM formation under rechlorination conditions Water Resources 37 11 2003 2654 2666
16. P.F. Boulos, K.E. Lansley, and B.W. Karney Comprehensive water distribution systems analysis handbook 2004 MWH Soft. Inc. Pasadena, USA
17. R. Branzei, D. Dimitrov, and S. Tijs Models in cooperative game theory 2nd ed. 2008 Springer
18. A. Bratcu, and Dolgui A Survey of the self-balancing production lines ("bucket brigades") Journal of Intelligent Manufacturing 16 2 2005 139 158
19. A. Bratcu, and Dolgui Some new results on the analysis and simulation of bucket brigades (self-balancing production lines) International Journal of Production Research 47 2 2009 369 388
20. Brdys, M. A., & Chang, T. (2002). Robust model predictive control under output constraints. In Procs 15th IFAC world congress, Barcelona, Spain.
21. Brdys, M. A., & Wang, J. (2005). Invariant set-based robust softly switched model predictive control. In Procs 16th IFAC world congress, Prague, Czech Republic.
22. M.A. Brdys, and T. Chang Robust model predictive control of chlorine residuals in water systems based on a state space modelling B. Ulanicki, B. Coulbeck, J. Rance, Water software systems: Theory and applications 2001 Research Studies Press Ltd Baldock, Hertfordshire, England
23. Brdys, M. A., Puta, H., Arnold, E., Chen, K., & Hopfgarten, S. (1995a). Operational control of integrated quality and quantity in water systems. In Procs IFAC/IFORS/IMACS symposium on large scale complex systems: Theory and applications, London, England.
24. Brdys, M. A., Chang, T., Duzinkiewicz, K., & Chotkowski, W. (2000). Hierarchical control of integrated quality and quantity in water distribution systems. In Procs A.S.C.E. 2000 joint conference on water resources engineering and water resources planning and management, Minneapolis, USA.
25. Brdys, M. A., Tran, V. N., & Kurek, W. (2011). Safety zones based robustly feasible model predictive control for nonlinear network systems. In Invited session on advances in intelligent monitoring, control and security of critical infrastructure systems, Procs 18th IFAC world congress, Milano, Italy.
26. Brdys, M. A., Huang, X., & Lei, Y. (2013). Two times-scale control of integrated quantity and quality in drinking water distribution systems. In Procs 13th IFAC symposium on large scale complex systems: Theory and applications, Shanghai, China.
27. M.A. Brdys, M. Grochowski, T. Gminski, K. Konarczak, and M. Drewa Hierarchical predictive control of integrated wastewater treatment systems Control Engineering Practice 16 2008 751 767
28. M.A. Brdys, and P. Tatjewski Iterative algorithms for multilayer optimizing control 2005 World Scientific Publishing; Imperial College Press New York, USA; London, UK
29. M.A. Brdys, and B. Ulanicki Operational control of water systems: Structures, algorithms and applications 1995 Prentice Hall International
30. S. Bussmann, N.R. Jennings, and M. Wooldridge Multiagent systems for manufacturing control: A design methodology 2004 Springer
31. E. Camponogara, D. Jia, B.H. Krogh, and S. Talukdar IEEE control systems aaaa Vol. 22 2002 IEEE Press 44 52
32. Chang, T., Brdys, M. A., & Duzinkiewicz, K. (2003). Decentralised robust model predictive control of chlorine residuals in drinking water distribution systems. In Procs world water & environmental resources congress - EWRI2003, Philadelphia, USA.
33. F. Chauvet, E. Levner, L. Meyzin, and J.-M. Proth On-line scheduling in a surface treatment system European Journal of Operational Research 120 2000 382 392
34. L. Chisci, J.A. Rossiter, and G. Zappa Systems with persistent disturbances: Predictive control with restricted constraints Automatica 37 7 2001 1019 1028
35. G. Chryssolouris Manufacturing systems: Theory and practice 2nd ed. 2006 Springer
36. W.W.C. Chung, and S.W.F. Leung Collaborative planning, forecasting and replenishment: A case study in cooper clad laminate industry Production Planning and Control 16 6 2005 563 574
37. P.I. Cowling, and M. Johansson Using real time information for effective dynamic scheduling European Journal of Operational Research 139 2 2002 230 244
38. B.C. Csáji, and L. Monostori Adaptive stochastic resource control: A machine learning approach Journal of artificial intelligence research (JAIR) Vol. 32 2008 AAAI Press p. 453-486
39. B.C. Ding, L.H. Xie, and W.J. Cai Distributed model predictive control for constrained linear systems International Journal of Robust and Nonlinear Control 2 11 2010 1285 1298
40. A. Dolgui, and J.M. Proth Supply chain engineering: Useful methods and techniques 2010 Springer
41. Drewa, M., Brdys, M. A., & Ciminski, A. (2007). Model predictive control of integrated quantity and quality in drinking water distribution systems. In Procs 8th international IFAC symposium on dynamics and control of process systems, Cancun, Mexico.
42. W.B. Dunbar Distributed receding horizon control of dynamically coupled nonlinear systems IEEE Transactions on Automatic Control 52 7 2007 1249 1263
43. A.B. Dunbar, and R.M. Murray Distributed receding horizon control for multi-vehicle formation stabilization Automatica 12 1 2006 1249 1263
44. K. Duzinkiewicz, M.A. Brdys, and T. Chang Hierarchical model predictive control of integrated quality and quantity in drinking water distribution systems Urban Water Journal 2 2 2005 125 137
45. W. ElMaraghy, H. ElMaraghy, T. Tomiyama, and L. Monostori Complexity in engineering design and manufacturing CIRP Annals - Manufacturing Technology 61 2 2012 793 814
46. G. Ewald, W. Kurek, and M.A. Brdys Grid implementation of parallel multi-objective genetic algorithm for optimized allocation of chlorination stations in drinking water distribution systems: Chojnice case study EEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38 4 2008 497 509
47. A.E. Fallah-Seghrouchni, and A. Suna CLAIM: A computational language for autonomous, intelligent and mobile agents Lecture notes in computer science Vol. 3067 2004 Springer 90 110
48. W. Findeisen, F.N. Bailey, M.A. Brdys, K. Malinowski, P. Tatjewski, and A. Wozniak Control and coordination in hierarchical systems 1980 J. Wiley & Sons London-Chichester-New York
49. K. Fregene, D. Kennedy, and D. Wang Towards a system - and control - oriented agent framework IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 36 2005 989 999
50. T. Glad, and L. Ljung Control theory: Multivariable and nonlinear methods 2000 Taylor and Francis
51. Grieder, P., Parrilo, P. A., & Morari, M. (2003). Robust receding horizon control - analysis and synthesis. In Procs 42th IEEE conference on decision and control, Hawaii, USA.
52. D. Grundel, R. Murphey, P. Pardalos, and O. Prokopye Cooperative systems: Control and optimization Lecture notes in mathematics and mathematical systems 2007 Springer
53. Hadeli, P. Valckenaers, M. Kollingbaum, and H.V. Brussel Multi-agent coordination and control using stigmergy Computers in industry Vol. 53 2004 Elsevier 75 96
54. Haugen, O., Runde R. K. (2008). Enhancing UML to formalize the FIPA agent interaction protocol. In Procs agent-based technologies and applications for enterprise interoperability (ATOP). pp. 154-173.
55. J. Holland Complex adaptive systems Daedalus 121 1992 17 30
56. J. Holland Hidden order: How adaptation builds complexity 1995 Helix Book, Addison-Wesley New York
57. T. Holvoet, D. Weyns, and P. Valckenaers Delegate MAS patterns for large-scale distributed coordination and control applications EuroPLoP 2010 2010 25
58. J.L. Hougaard An introduction to allocation rules 2009 Springer
59. IEC 62264 (2002). IEC 62264. Enterprise-control system integration, Part 1. Models and terminology, Part 2: Model object attributes. ISO/IEC, Geneva.
60. H. Ito, Z.-P. Jiang, S. Dashkovskiy, and B. Rüffer Robust stability of networks of iISS systems: Construction of sum-type Lyapunov functions IEEE Transactions on Automatic Control 58 2013 1192 1207
61. D. Ivanov, A. Dolgui, and B. Sokolov Applicability of optimal control theory for adaptive supply chain planning and scheduling Annual Reviews in Control 36 1 2012 73 84
62. K. Iwata, M. Onosato, and M. Koike Random manufacturing systems: A new concept of manufacturing systems for production to order CIRP Annals - Manufacturing Technology 43 1 1994 379 383
63. F. Jovane, Y. Koren, and C.R. Boer Present and future of flexible automation: Towards new paradigms CIRP Annals - Manufacturing Technology 52 2 2003 453 560
64. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Procs IEEE international conference on neural networks (Vol. 4, pp. 1942-1948).
65. Kerrigan, E. C., & Maciejowski, J. M. (2001). Robust feasibility in model predictive control: Necessary and sufficient conditions. In Procs 40th IEEE conference on decision and control - CDC 20001, Florida, USA.
66. V.B. Kreng, and F.-T. Chen Three echelon buyer-supplier delivery policy - a supply chain collaboration approach Production Planning and Control 18 4 2007 338 349
67. Kurek, W., & Brdys, M. A. (2010). Adaptive multiobjective model predictive control with application to DWDS. In Procs 12th IFAC symposium on large scale complex systems: Theory and applications, Lille, France.
68. R. Langowski, and M.A. Brdys Monitoring of chlorine concentration in drinking water distribution systems using an interval estimator International Journal of Applied Mathematics and Computer Science 17 2 2007 199 216
69. W. Langson, I. Chryssochoos, S.V. Rakovic, and D.Q. Mayne Robust model predictive control using tubes Automatica 40 1 2004 125 133
70. P. Leitão, and F. Restivo ADACOR: A holonic architecture for agile and adaptive manufacturing control Computers in Industry 57 2 2006 121 130
71. Lentini, P. L., Rao, G. P., Thies J. N., & Kay, J. (1998). In Procs AAAI workshop on software tools for developing agents, July 1998.
72. D. Liberzon Switching in systems and control 2003 Birkhuser Boston, USA
73. L. Ljung System identification: Theory for the user 2nd ed. 1999 Prentice-Hall Upper Saddle River
74. B.L. MacCarthy, and J. Liu Addressing the gap in scheduling research: A review of optimization and heuristic methods in production scheduling International Journal of Production Research 31 1 1993 59 79
75. A. Márkus, T. Kis, J. Váncza, and L. Monostori A market approach to Holonic manufacturing Annals of the CIRP - Manufacturing Technology 45 1 1996 433 436
76. D.Q. Mayne, M.M. Seron, and S.V. Rakovic Robust model predictive control of constrained linear systems with bounded disturbances Automatica 41 2 2005 219 224
77. P. McCullen, and D. Towill Achieving lean supply through agile manufacturing Integrated Manufacturing Systems 12 6-7 2001 524 533
78. R. Mejía, A. López, and A. Molina Experiences in developing collaborative engineering environments: An action research approach Computers in industry Vol. 58/4 2007 Elsevier pp. 329-346
79. S. Meyn Control techniques for complex networks 2007 Cambridge University Press
80. L. Monostori, J. Váncza, and S.R.T. Kumara Agent-based systems for manufacturing Annals of the CIRP - Manufacturing Technology 55 2 2006 697 720
81. G. Morel, H. Panetto, M-.B. Zaremba, and F. Mayer Manufacturing enterprise control and management system engineering: Paradigms and open issues Annual reviews in control Vol. 27/2 2003 Elsevier 199 209
82. Moyson, F., & Manderick, B. (1988). The collective behaviour of ants: An example of self-organization in massive parallelism. In Procs AAAI spring symposium on parallel models of intelligence, Stanford, California.
83. S.Y. Nof Springer handbook of automation 2009 Springer-Verlag Berlin, Heidelberg
84. S.Y. Nof, G. Morel, L. Monostori, A. Molina, and F. Filip From plant and logistics control to multi-enterprise: Milestone report of the manufacturing & logistics systems coordinating committee Annual reviews of control Vol. 30/1 2006 Elsevier 55 68
85. Novas, J., Bahtiar, R., Van Belle, J., & Valckenaers, P. (2012). An approach for the integration of a scheduling system and a multi-agent manufacturing execution system. Towards a collaborative framework. In 14th IFAC symposium on information control problems in manufacturing (INCOM 2012) (pp. W258-W263).
86. R. Olfati-Saber Design of behavior of swarms: From flocking to data fusion using microfilter networks Chapter of the book: Cooperative control of distributed multi-agent systems 2007 Wiley
87. Olfati-Saber, R., & Murray, R. M. (2003). Consensus protocols for networks of dynamic agents. In Procs of the American control conference (ACC) (pp. 951-956).
88. Akyol, S. D., & Bayhan, G. M. (2011). A particle swarm optimization algorithm for maximizing production rate and workload smoothness. Third world congress on nature and biologically inspired computing (NaBIC).
89. Ostfeld, A., Salomons, E., & Shamir, U. (2002). Optimal operation of water distribution systems under water quality unsteady conditions. In Procs 1st annual environmental & water resources systems analysis (EWRSA) symposium, A.S.C.E. environmental & water resources institute (EWRI) annual conference, Roanoke, Virginia, USA.
90. C. Pach, T. Berger, Y. Sallez, T. Bonte, E. Adam, and D. Trentesaux Reactive and energy-aware scheduling of flexible manufacturing systems using potential fields Computers in Industry 2014 10
91. H. Panetto Towards a classification framework for interoperability of enterprise applications In International journal of CIM Vol. 20/8 2007 Taylor & Francis pp. 727-740, ISSN 0951-192X
92. H. Panetto, M. Dassisti, and A. Tursi ONTO-PDM: Product-driven ONTOlogy for product data management interoperability within manufacturing process environment Advanced Engineering Informatics 26 2 2012 334 348
93. H. Panetto, and A. Molina Enterprise integration and interoperability in manufacturing systems: Trends and issues Computers in Industry 59 7 2008 641 646
94. B. Peleg, and P. Sudhölter Introduction to the theory of cooperative games 2004 Kluwer
95. J.R. Perkins, C. Humes, and P.R. Kumar Distributed scheduling of flexible manufacturing systems: Stability and performance IEEE Transactions on Robotics and Automation 10 1994 133 141
96. M. Pinedo Scheduling theory algorithms and systems 2nd ed. 2002 Prentice Hall
97. M. Polycarpou, J. Uber, Z. Wang, F. Shang, and M.A. Brdys Feedback control of water quality IEEE Control Systems Magazine 2001 68 87
98. T.D. Prasad, G.A. Walters, and D.A. Savic Booster disinfection of water supply networks: Multiobjective approach Journal of Water Resources, Planning and Management 130 5 2004
99. S.J. Qin, and T.A. Badgwell A survey of industrial model predictive control technology Control engineering practice Vol. 11 2003 Elsevier 733 764
100. J.B. Rawlings, and D.Q. Mayne Model predictive control: Theory and design 2009 Nob Hill Publishing LLC Maddison, USA
101. W.C. Regli, I. Mayk, C.J. Dugan, J.B. Kopena, R.N. Lass, P.J. Modi, W.M. Mongan, J.K. Salvage, and E.A. Sultanik Development and specification of a reference model for agent-based systems IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39 5 2009 572 596
102. A. Sakarya, and L.W. Mays Optimal operation of water distribution system pumps with water quality considerations ASCE Journal of Water Resources Planning and Management 126 4 2000 210 220
103. Y. Sallez, T. Berger, and D. Trentesaux A stigmergic approach for dynamic routing of active products in FMS Computers in Industry 60 3 2009 204 216
104. R. Scattolini Architectures for distributed and hierarchical model predictive control - A review Journal of Process Control 19 5 2009 723 731
105. B. Scholz-Reiter, and M. Freitag Autonomous processes in assembly systems CIRP Annals - Manufacturing Technology 56 2 2007 712 729
106. G. Schuh, L. Monostori, B.C. Csáji, and S. Döring Complexity-based modeling of reconfigurable collaborations in production industry Annals of the CIRP - Manufacturing Technology 57 1 2008 445 450
107. J. Shamma Dimensions of cooperative control Chapter of the book: Cooperative control of distributed multi-agent systems 2007 Wiley
108. Shamma, J., (2013). From distributed control systems to game theory: There and back again. In Procs 52th IEEE conference on decision and control, Florence, Italy.
109. H.A. Simon The sciences of the artificial 1969 MIT Press Cambridge, Massachusetts
110. D.-P. Song Optimal control and optimization of stochastic supply chain systems 2013 Springer
111. J.D. Sterman Modeling managerial behaviour: Misperception of feedback in a dynamic decision making experiment Management Science 35 1989 321 339
112. P.P.M. Stoop, and V.C.S. Weirs The complexity of scheduling in practice International Journal of Operations and Production Management 16 10 1996 37 53
113. R.S. Sutton, and A.G. Barto Reinforcement learning: An introduction 1998 The MIT Press
114. A. Tharumarajah, J. Wells, and L. Nemes Comparison of the bionic, fractal and holonic manufacturing systems concepts International Journal of Computer Integrated Manufacturing 9 3 1996 217 226
115. Tran, V. N., & Brdys, M. A. (2013). Softly switched robustly feasible model predictive control for nonlinear network systems. In Procs 13th IFAC symp on large scale complex systems: Theory and applications, Shanghai, China.
116. Trawicki, D., Duzinkiewicz, K., & Brdys, M. A. (2003). Hybrid GA-MIL algorithm for optimisation of integrated quality and quantity in water distribution systems. In Procs world water & environmental resources congress - EWRI2003, Philadelphia, USA.
117. I. Trencansky, and R. Cervenka Agent modeling language (AML): A comprehensive approach to modeling MAS Informatica 29 2005 391 400
118. K. Ueda, A. Márkus, L. Monostori, H.J.J. Kals, and T. Arai Emergent synthesis methodologies for manufacturing Annals of the CIRP - Manufacturing Technology 50 2 2001 535 551
119. K. Ueda, J. Vaario, and K.H. Ohkura Modelling of biological manufacturing systems for dynamic reconfiguration CIRP Annals - Manufacturing Technology 46 1 1997 343 346
120. P. Valckenaers, and H. Van Brussel Holonic manufacturing execution systems CIRP Annals - Manufacturing Technology 54 1 2005 427 432
121. P. Valckenaers, H. Van Brussel, H. Bruyninckx, B. Saint Germain, J. Van Belle, and J. Philips Predicting the unexpected Computers in industry Vol. 62 2011 Elsevier 623 637
122. Valencia, F., Espinosa, J., De Schutter, B., & Stankova, K. (2011). Feasible cooperation distributed model predictive control scheme based on game theory. Invited session on hierarchical and distributed model predictive control - I. Fundamentals. In S. Bittani, A. Cenedese, & S. Zampieri (Eds.), Procs 18th IFAC world congress, Milano, Italy.
123. T. Vámos Co-operative systems - An evolutionary perspective IEEE continuous systems magazine Vol. 3/2 1983 IEEE Press 9 14
124. Van Belle, J., Saint Germain, B., Philips, J., Valckenaers, P., & Cattrysse, D. (2013). Cooperation between a holonic logistics execution system and a vehicle routing scheduling system. In Proceedings 11th IFAC workshop on intelligent manufacturing systems (pp. 184-189).
125. H. Van Brussel, J. Wyns, P. Valckenaers, L. Bongaerts, and P. Peeters Reference architecture for holonic manufacturing systems: PROSA Computers in industry Vol. 37 1998 Elsevier 255 274
126. J. Váncza, L. Monostori, D. Lutters, S.R. Kumara, M. Tseng, P. Valckenaers, and H. Van Brussel Cooperative and responsive manufacturing enterprises CIRP annals - manufacturing technology Vol. 60/2 2011 Elsevier 797 820 10.1016/j.cirp.2011.05.009
127. A. Venkat, S. Rawlings, and S. Wright Distributed model predictive control 2007 Springer
128. P. Verstraete, P. Valckenaers, H. Van Brussel, B. Saint Germain, Hadeli, and J. Van Belle Towards robust and efficient planning execution Engineering Applications of Artificial Intelligence 21 3 2008 304 314
129. G.E. Vieira, J.W. Hermann, and E. Lin Rescheduling manufacturing systems: A framework of strategies, policies and methods Journal of Scheduling 6 2003 36 92
130. J. von Neumann, and O. Morgenstern Theory of games and economic behavior 3rd ed. 1953 Princeton University Press Princeton, New Jersey
131. M.M. Waldrop Complexity: The emerging science at the edge of order and chaos 1992 Simon and Schuster Inc.
132. Wang, J., & Brdys, M. A. (2006a). Supervised robustly feasible soft switching model predictive control with bounded disturbances. In Procs 6th IEEE biennial world congress on intelligent control and automation - WCICA'06, Dalian, China.
133. Wang, J., & Brdys, M. A. (2006b). Softly switched hybrid predictive control. In Procs applications of large scale industrial systems - ALSIS2006, Helsinki, Finland.
134. Wang, J., Grochowski, M., & Brdys, M. A. (2005). Analysis and design of softly switched model predictive control. In Procs 16th IFAC world congress, Prague, Czech Republic.
135. H.J. Warnecke Revolution der Unternehmenskultur - Das fraktale Unternehmen 1993 Springer Verlag Berlin, Germany
136. V.C.S. Weirs A review of the applicability of OR and AI scheduling techniques in practice Omega. The International Journal of Management Science 25 2 1997 145 153
137. L. Whitman, and H. Panetto The missing link: Culture and language barriers to interoperability Annual Reviews in Control 30 2 2006 233 241
138. H-P. Wiendahl, and P. Scholtissek Management and control of complexity in manufacturing CIRP Annals - Manufacturing Technology 43 2 1994 533 540
139. T. Wu, N. Ye, and D. Zhang Comparison of distributed methods for resource allocation International Journal of Production Research 43 2005 515 536
140. Yen, J., Yan, Y. H., Wang, B. J., Sin, P. K. H., & Wu, F. F. (1998). Multi-agent coalition formation in power transmission planning. In Proceedings of the 31st Hawaii international conference on system sciences (Vol. 4. pp. 433-443).
141. G. Zambrano Rey, C. Pach, N. Aissani, A. Bekrar, T. Berger, and D. Trentesaux The control of myopic behavior in semi-heterarchical production systems: A holonic framework Engineering Applications of Artificial Intelligence 26 2 2013 800 817
142. Zheng, Y., Li, S. (2013). The stabilisation of coordinated distributed model predictive control. In Procs. 13th IFAC symposium on large scale systems: Theory and applications, Shanghai, China.
143. Y. Zheng, S. Li, and N. Li Distributed model predictive control over network information exchange for large scale systems Control Engineering Practice 19 2011 757 769
144. Y. Zheng, S. Li, and H. Qiu Network coordination - Based distributed model predictive control for large scale systems IEEE Transitions on Control Systems Technology 21 2013 991 1012
145. K. Zimmer Supply chain coordination with uncertain just-in-time delivery International Journal of Production Economics 77 2002 1 15