Most of the current attempts for business process optimisation are manual without involving any formal automated methodology. This paper proposes a framework for multi-objective optimisation of business process designs. The framework uses a generic business process model that is formally defined and specifies process cost and duration as objective functions. The business process model is programmed and incorporated into a software platform where a selection of multi-objective optimisation algorithms is applied to a range of test designs including a real example. The test business process designs are of varying complexity and are optimised with three popular optimisation techniques (Non-Dominated Sorting Genetic Algorithm II (NSGA2), Strength Pareto Evolutionary Algorithm II (SPEA2) and Multi-Objective Particle Swarm Optimisation (MOPSO) algorithms). The results indicate that although business process optimisation is a highly constrained problem with fragmented search space; multi-objective optimisation algorithms such as NSGA2 and SPEA2 produce a satisfactory number of alternative optimised business process designs. However, the performance of the optimisation algorithms drops sharply as the complexity of the process designs increases. This paper also discusses the directions for future research in this particular area
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