Abstract: This chapter presents a procedure for automatic creation of self-adaptive artificial supervisors of multi-task real-time object-oriented systems (MT RT OOS). The procedure is based on developmental genetic programming. Early UML diagrams describing a MT RT OOS are used as input data to the procedure. Next, an artificial supervisor which optimizes the system use is automatically generated. The supervisor is self-adaptive what means that it is capable of keeping optimality of the system in spite of disruptions that may occur dynamically in time of the system work. A representative example of creation of a supervisor of building a house illustrates the procedure. Efficiency of the procedure from the point of view of self-adaptivity of the supervisor is investigated.
B I B L I O G R A F I A1.C. Wei, P. Liu, Y. Tsai, Resource-constrained project management using enhanced theory of constraint. Int. J. Proj. Manag. 20(7), 561–567 (2002). http://dx.doi.org/10.1016/S0263-7863(01)00063-1
2.
J. Blazewicz, J.K. Lenstra, A.H.G. Rinnooy Kan, Scheduling subject to resource constraints: classification and complexity. Discret. Appl. Math. 5, 11–24 (1983). http://dx.doi.org/10.1016/0166-218X(83)90012-4
3.
G. Pawiński, K. Sapiecha, Cost-efficient project management based on distributed processing model, in Proceedings of The 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Belfast (2013). http://dx.doi.org/10.1109/PDP.2013.30
4.
R.H. Möhring, A.S. Schulz, F. Stork, M. Uetz, Solving project scheduling problems by minimum cut computations. Manag. Sci. 49(3), 330–350 (2003). http://dx.doi.org/10.1287/mnsc.49.3.330.12737
5.
R.E. Keller, W. Banzhaf, The evolution of genetic code in genetic programming, in Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1077–1082 (1999)
6.
L.C. Briand, Y. Labiche, A UML-based approach to system testing. Softw. Syst. Model. 1(1), 10–42 (2002). http://dx.doi.org/10.1007/s10270-002-0004-8
7.
J.L.M. Pasaje, M.G. Harbour, J.M. Drake, MAST real-time view: a graphic UML tool for modeling object-oriented real-time systems, in Proceeding of: IEEE 22nd Real-Time Systems Symposium (RTSS 2001) (2001). http://dx.doi.org/10.1109/REAL.2001.990618
8.
H. Gomaa, Designing Concurrent, Distributed, and Real-Time Applications with UML (Addison-Wesley, Boston, 2000)
9.
R. Jigorea, S. Manolache, P. Eles, Z. Peng, Modelling of real-time embedded systems in an object-oriented design environment with UML, in Proceedings. Third IEEE International Symposium on Object-Oriented Real-Time Distributed Computing, pp. 210–213 (2000). http://dx.doi.org/10.1109/ISORC.2000.839532
10.
J.H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, (University of Michigan Press, Ann Arbor) (reprinted, MIT Press, Cambridge, 1992)
11.
J. Koza, F.H. Bennett III, D. Andre, M.A. Keane, Evolutionary design of analog electrical circuits using genetic programming, in Adaptive Computing in Design and Manufacture, ed. by I.C. Parmee (1998). http://dx.doi.org/10.1007/978-3-540-85857-7_8
12.
S. Deniziak, A. Górski, Hardware/software co-synthesis of distributed embedded systems using genetic programming, in Lecture Notes in Computer Science (Springer, Berlin, 2008), pp. 83–93. http://dx.doi.org/10.1007/978-3-540-85857-7_8
13.
J.R. Koza, R. Poli, Genetic programming, in Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Chapter 5, ed. by E. Burke, G. Kendal (Springer, 2005). http://dx.doi.org/10.1007/0-387-28356-0_5
14.
J. Alcaraz, C. Maroto, A robust genetic algorithm for resource allocation in project scheduling. Ann. Oper. Res. 102, 83–109 (2001). http://dx.doi.org/10.1023/A:1010949931021
15.
S. Hartmann, An competitive genetic algorithm for resource-constrained project scheduling. Nav. Res. Logist. 45(7), 733–750 (1998). http://dx.doi.org/10.1002/(SICI)1520-6750(199810)45:7%3C733::AID-NAV5%3E3.3.CO
2-7
16.
H. Zhang, H. Xu, W. Peng, A genetic algorithm for solving RCPSP, in International Symposium on Computer Science and Computational Technology (2008)
17.
S. Hartmann, A self-adapting genetic algorithm for project scheduling under resource constraints. Wiley Period. Inc. Nav. Res. Logist. 49, 433448 (2002)
18.
X. Li, L. Kang, W. Tan, Optimized research of resource constrained project scheduling problem based on genetic algorithms. Lect. Notes Comput. Sci. 4683, 177–186 (2007). http://dx.doi.org/10.1007/978-3-540-74581-5_19
19.
H. Zoulfaghari, J. Nematian, N. Mahmoudi, M. Khodabandeh, A new genetic algorithm for the RCPSP in large scale. Int. J. Appl. Evol. Comput. 4(2), 29–40 (2013). http://dx.doi.org/10.4018/jaec.2013040103
20.
S. Hartmann, D. Briskorn, A survey of variants and extensions of the resource-constrained project scheduling problem, Eur. J. Oper. Res.: EJOR 207(1)(16.11), 1–15 (Elsevier, Amsterdam, 2010). http://dx.doi.org/10.1016/j.ejor.2009.11.005
21.
M. Al-Fawzan, M. Haouari, A bi-objective model for robust resourceconstrained project scheduling. Int. J. Prod. Econ. 96, 175–187 (2005)CrossRef
22.
K.M. Calhoun, R.F. Deckro, J.T. Moore, J.W. Chrissis, J.C.V. Hove, Planning and re-planning in project and production scheduling. Omega Int. J. Manag. Sci. 30(3), 155170 (2002)CrossRef
23.
K. Sapiecha, L. Ciopiński, S. Deniziak, An application of developmental genetic programming for automatic creation of supervisors of multi-task real-time object-oriented systems, in Proceedings. Federated Conference on Computer Science and Information Systems (FedCSIS, Warsaw, 2014). http://dx.doi.org/10.15439/2014F208
24.
R.V. Binder, Testing Object-Oriented Systems—Models, Patterns, and Tools (Addison-Wesley, Reading, 1999)
25.
Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs (Springer, Berlin, 1996) 
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