Abstract
This paper discusses the problem of business process optimisation within a multi-objective evolutionary framework. Business process optimisation is considered as the problem of constructing feasible business process designs with optimum attribute values such as duration and cost. The proposed approach involves the application of a series of Evolutionary Multi-objective Optimisation Algorithms (EMOAs) in an attempt to generate a series of diverse optimised business process designs for the same process requirements. The proposed optimisation framework introduces a quantitative representation of business processes involving two matrices one for capturing the process design and one for calculating and evaluating the process attributes. It also introduces an algorithm that checks the feasibility of each candidate solution (i.e. process design). The experimental results demonstrate that the proposed optimisation framework is capable of producing a satisfactory number of optimised design alternatives considering the problem complexity and high rate of infeasibility.
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References
Hofacker, I., Vetschera, R.: Algorithmical approaches to business process design. Computers & Operations Research 28, 1253–1275 (2001)
Tiwari, A., Vergidis, K., Majeed, B.: Evolutionary Multi-objective Optimization of Business Processes. In: Proceedings of IEEE Congress on Evolutionary Computation 2006, pp. 3091–3097 (2006)
Vergidis, K., Tiwari, A.: Business Process Design and Attribute Optimization within an Evolutionary Framework. In: Proceedings of the Congress on Evolutionary Computing (CEC 2008), pp. 668–675 (2008)
Vergidis, K., Tiwari, A., Majeed, B.: Business process improvement using multi-objective optimization. BT Technology Journal 24(2), 229–235 (2006)
Vergidis, K., Tiwari, A., Majeed, B.: Composite business processes: An evolutionary multi-objective optimization approach. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, pp. 2672–2678 (2007)
Wang, K., Salhi, A., Fraga, E.S.: Process design optimization using embedded hybrid visualization and data analysis techniques within a genetic algorithm optimization framework. Chemical Engineering and Processing 43, 663–675 (2004)
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Tiwari, A., Vergidis, K., Turner, C. (2010). Evolutionary Multi-objective Optimisation of Business Processes. In: Gao, XZ., Gaspar-Cunha, A., Köppen, M., Schaefer, G., Wang, J. (eds) Soft Computing in Industrial Applications. Advances in Intelligent and Soft Computing, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11282-9_31
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DOI: https://doi.org/10.1007/978-3-642-11282-9_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11281-2
Online ISBN: 978-3-642-11282-9
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