An evolutionary multi-objective framework for business process optimisation

• Authors: Kostas Vergidis;Dhish Saxena;Ashutosh Tiwari
• Venue: Applied Soft Computing
• Year: 2012

This paper aims to investigate the application of evolutionary multi-objective optimisation to the new domain of business process optimisation. Business process optimisation is considered as the problem of constructing feasible business process designs with optimum attribute values such as duration and cost. The feasibility of a process design is based on: (i) the process requirements such as the required input and the expected output resources and (ii) the connectivity of the participating tasks in the process design through their input and output resources. Due to the multi-objective and discrete nature of the problem and the resulting fragmented search space, discovering feasible business process designs is one of the main challenges. 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 given 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 results for two real-life scenarios demonstrate how the proposed framework produces a number of optimised design alternatives. NSGA-II proves unfit for the specific problem whilst PESA-II shows the best results due to its sophisticated region-based selection technique.