Browsing by Author "Vergidis, K."
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Item Open Access Business process improvement using multi-objective optimisation(Springer Verlag, 2006) Vergidis, K.; Tiwari, Ashutosh; Majeed, BasimBusiness process redesign and improvement has become an increasingly attractive subject in the wider area of business process intelligence. Although there have been many attempts to establish a business process redesign framework, there is little work on the actual optimisation of business processes with given objectives. Furthermore, most of the attempts to optimise a business process are manual and do not involve a formal automated methodology. This paper proposes a process improvement approach for automated multi-objective optimisation of business processes. The proposed framework uses a generic business process model that is formally defined. The formal definition of business processes is necessary to ensure that the optimisation will take place in a clearly defined, repeatable and verifiable way. Multi-objectivity is expressed in terms of process cost and duration as two key objectives for any business process. The business process model is programmed and incorporated into a software optimisation platform where a selection of multi-objective optimisation algorithms can be applied to a business process design. This paper outlines a case study of business process design that is optimised by the state-of-the-art multi-objective optimisation algorithm NSGA2. The results indicate that, although business process optimisation is a highly constrained problem with fragmented search space, a number of alternative optimised business processes that meet the optimisation criteria can be produced. The paper also provides directions for future research in this area.Item Open Access Business process optimisation using an evolutionary multi-objective framework(Cranfield University, 2008-11) Vergidis, K.; Tiwari, AshutoshIn response to the increasingly volatile and competitive environment, organisations are examining how their core business processes may be redesigned in order to improve business performance and market responsiveness. However, there is a lack of holistic approaches towards business process redesign through optimisation. The aim of this research is to develop an evolutionary multi-objective optimisation framework for business processes capable of: (i) representing business process designs in a quantitative way, (ii) algorithmically composing designs based on specific process requirements and (iii) identifying the optimal processes utilising evolutionary algorithms. A literature survey of business process definitions, modelling, analysis and optimisation techniques provides an overview of the current state of research and highlights the gap in business process optimisation. An industry survey within the service sector grounds the research within the industrial context and compares the real-life issues related to business processes with the literature findings. This research proposes a representation technique for business process designs using both a visual and a quantitative perspective. It also proposes the Process Composition Algorithm (PCA) – an algorithm for composing new business process designs. The proposed business process optimisation framework (bpoF) lies at the heart of this research and employs the representation technique, PCA and a series of state-of-the-art evolutionary optimisation algorithms. The framework is capable of generating a series of alternative optimised business process designs based on given requirements. A strategy for creating experimental business process scenarios is also proposed by this research. The proposed strategy provides the opportunity of assessing both the capability of the framework in optimising challenging business process scenarios and the performance of the evolutionary algorithms. Finally, a set of real-life business process scenarios is prepared using the proposed representation in order to validate the optimisation framework. Also, a workshop with a series of business process experts assesses the capability of the framework in dealing with these real-life scenarios. In this way, this research proposes a fully tested and validated methodology for capturing, representing and optimising business process designs.Item Open Access Optimisation of business process designs: An algorithmic approach with multiple objectives.(Elsevier, 2007-09) Vergidis, K.; Tiwari, Ashutosh; Majeed, Basim; Roy, RajkumarMost 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.