Multi-objective optimisation of web business processes

Authors:
Ashutosh Tiwari;Christopher Turner;Peter Ball;Kostas Vergidis
Affiliations:
Decision Engineering Centre, Cranfield University, Cranfield, Bedfordshire, UK;Decision Engineering Centre, Cranfield University, Cranfield, Bedfordshire, UK;Decision Engineering Centre, Cranfield University, Cranfield, Bedfordshire, UK;Decision Engineering Centre, Cranfield University, Cranfield, Bedfordshire, UK
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

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Abstract

This paper proposes an approach for the optimisation of web business processes using multi-objective evolutionary computing. Business process optimisation is considered as the problem of constructing feasible business process designs with optimum attribute values such as duration and cost. This optimisation framework 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 given requirements. The optimisation framework is tested to validate the framework's capability in capturing, composing and optimising business process designs constituted of web services. The results from the web business process optimisation scenario, featured in this paper, demonstrate that the framework can identify business process designs with optimised attribute values.