Kostas Vergidis

Business Process Analysis and Optimization: Beyond Reengineering

There is an abundance of business process modeling techniques that capture and address different aspects of a business process. A limited number of these process models allow further quantitative analysis, and only a few enable structured process improvement. This paper reviews and classifies the main techniques for business process modeling with regard to their analysis and optimization capabilities. Three primary groups are identified, and a selection of representative business process modeling techniques is classified based on these. Similar classification is also presented for the analysis and optimization approaches for business processes that were identified in relevant literature. The main contribution of the paper is that it identifies which types of business process models are suitable for analysis and optimization, and also highlights the lack of such approaches. This paper offers a state-of-the-art review in the areas of business process modeling, analysis, and optimization-underlining that the latter two have not received enough coverage and support in the literature.

Evolutionary Multi-objective Optimization of Business Processes

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 processes. 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 five test problems. The test problems are business process designs of varying complexities and are optimised with three popular optimisation techniques (NSGA2, SPEA2 and MOPSO algorithms). The results indicate that although the 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 processes. However, the performance of the optimisation algorithms drops sharply with even a slight increase in problem complexity. This paper also discusses the directions for future research in this area.

An evolutionary multi-objective framework for business process optimisation

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.

Evolutionary Multi-objective Optimisation of Business Processes

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.

Business process design and attribute optimization within an evolutionary framework

This paper discusses the problem of business process design and attribute optimization within a multi-objective evolutionary framework. Business process design and attribute optimization 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. The proposed approach involves the application of the evolutionary multi objective optimization algorithm (EMOOA) non-dominated sorting genetic algorithm II (NSGA2) in an attempt to generate a series of diverse optimized business process designs for the same process requirements. The proposed optimization 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 (Le. process design). The results demonstrate that for a variety of experimental problems NSGA2 produces a satisfactory number of optimized design alternatives considering the problem complexity and high rate of infeasibility.

Automated inspection using database technology within the aerospace industry

Many manufacturing processes have become fully automated resulting in high production volumes. However, this is not the case for inspection. Shortening the inspection times in manufacturing industry using the available information resources can result in the reduction of production lead-time and overall costs. Rapid advances in machine tool technology have resulted in fast processing computer numerical control (CNC) machines that are capable of manufacturing parts at high speeds, turning their manual inspection process into a bottleneck. However, most CNC machines record the operations that they perform as realization logs. This paper proposes an approach that utilizes these realization logs for automating the inspection process. The automation occurs with the implementation of a software tool that imports and compares the realization logs with the manufacturing instructions for a manufactured part. The output of the tool is an inspection report that lists all the identified skipped or mishandled operations for that part. The proposed inspection approach is compared with the manual practice within an aerospace manufacturer. The results demonstrate drastic reduction in production lead-time while producing accurate and reliable inspection reports in an automated manner.

Composite business processes: An evolutionary multi-objective optimization approach

Business process optimization has received little coverage compared to business process modeling and analysis techniques. This paper introduces composite business process models, i.e. conceptual business processes with tasks that each has its own library of alternatives. This paper formulates an optimization problem based on this concept. A series of experiments is designed to address processes of various sizes in terms of participating tasks and libraries of alternatives. Evolutionary algorithms such as NSGA2, SPEA2 and MOPSO attempt to generate optimum solutions to a bi-objective and tri- objective problem formulation. The results show that SPEA2 performs better in the bi-objective problem, while NSGA2 has a clear advantage in the tri-objective problem, although both provide good solutions in all instances. This paper attempts to establish a viewpoint regarding business processes that will provoke and encourage further optimization attempts in this area.

An automated optimisation framework for the development of re-configurable business processes: a web services approach

The practice of optimising business processes has, until recently, been undertaken mainly as a manual task. This article provides insights into an automated business process optimisation framework by using web services for the development of re-configurable business processes. The research presented here extends the optimisation framework by introducing additional web services as a mechanism for facilitating process interactions, identifying enhancements to support business processes and undertaking three case studies to evaluate the proposed enhancements. The featured case studies demonstrate that an increase in the amount of available web services gives rise to improvements in the business processes generated. This research highlights an increase in the efficiency of the algorithm and the quality of the business process designs that result from the enhancements. Future research directions are proposed for the further improvement of the framework.

The evaluation line: A posteriori preference articulation approach

This paper presents the concept of the evaluation line - a posteriori preference articulation approach for evaluating Pareto-optimal solutions using high level preference criteria. The evaluation line is an approach based on analytical geometry. It is sketched using a weighted function that prioritizes the different objectives by assigning unique weights. It then evaluates each Pareto-optimal solution based on its point-line distance from the line. Based on its function, the evaluation line leans appropriately to demonstrate preference towards one or more objectives and its function is extended to the n-objective space. The evaluation line is compared with the classical weighted sum approach. The comparison demonstrates the relativity of the two approaches but also highlights the strength of the evaluation line in the cases of non convex Pareto-optimal fronts.

Multi-objective optimisation of web business processes

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 frameworks 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.