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NSGA II algorithm application within the dynamic programming approach to business process optimisation

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Języki publikacji
EN
Abstrakty
EN
Processes are defined as sets of tasks which transform inputs into outputs. Based on such a definition, processes are graphically modelled as tasks and the connections (dependencies) between them. Another popular modelling practice is not to model the whole process as one enormous diagram, but to split it into high level diagram, presenting whole value chain, and detailed diagrams of sub-processes’. Rather than joining these subprocesses back into one huge process, an optimisation algorithm can use this modelling approach as an advantage. This paper describes the NSGA II algorithm and its implementation which can be used within the scheduler to use a process model with sub-processes as an input and output.
Rocznik
Strony
195--207
Opis fizyczny
Bibliogr. 13 poz.
Twórcy
autor
  • CGI Polska, Energy and Resources, ul. Sienna 39, Warszawa 02-972
Bibliografia
  • [1] Koubrakis, M. and Plexousakis, D., A formal framework for business process modelling and design, Information Systems, Vol. 27, No. 5, 2002, pp. 299–319.
  • [2] Vergidis, K., Tiwari, A., and Majeed, B., Business process analysis and optimisation: beyond reengineering, IEEE Transactions on Systems, Man and Cybernetics Part C: Application and Reviews, Vol. 38, No. 1, 2008, pp. 69–82.
  • [3] van der Aalst, W., ter Hofstede, A., Kiepuszewski, B., and Barros, A., Advanced Workflow Patterns, Lecture Notes in Computer Science, Vol. 1901, 2000, pp. 18–29.
  • [4] Johansson, H. et al., Business Process Reengineering: BreakPoint Strategies for Market Dominance, John Wiley and Sons, 1993.
  • [5] Hammer, M. and Champy, J., Reengineering the Corporation: A Manifesto for Business Revolution, Harper Collins, 1993.
  • [6] Davenport, T., Process Innovation: Reengineering work through information technology, Harvard Business Press, 1993.
  • [7] Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T., A fast elitist nondominated sorting genetic algorithm for multi-objective optimisation: NSGAII, IEEE Transaction on Evolutionary Computations, Vol. 6, No. 2, 2002, pp. 182–197.
  • [8] Rudolph, G., Evolutionary search under partially ordered sets, Tech. Rep. CI-67 / 99, Dept. of Computer Science, University of Dortmund, 1999.
  • [9] Wibig, M., Optimisation of projects logistics processes using Petri Nets, Polish Journal of Environmental Studies, Vol. 17, No. 2A, 2008, pp. 95–98.
  • [10] Vergidis, K., Tiwari, A., and Majeed, B., Evolutionary multi-objective optimisation of business processes, In: Proceedings of IEEE Congress of Evolutionary Computations, 2006, pp. 3091–3097.
  • [11] Wibig, M., Dynamic programming and genetic algorithm for business process optimisation, International Journal Intelligent System and Applications, Vol. 5, No. 1, 2012, pp. 44–51.
  • [12] Kettinger, W., Teng, J., and Guha, S., Business Process Change: A study of methodologies techniques and tools, MIS Quartely, Vol. 21, No. 1, 1997, pp. 55–98.
  • [13] Melao, N. and Pidd, M., A conceptual framework for understanding business process modeling, Information Systems, Vol. 10, No. 1, 2000, pp. 105–130.
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Bibliografia
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