A Metaheuristic Approach for the Configuration of Business Process Families

Abstract

Business process families provide an over-arching representation of the possible business processes of a target domain. They are defined by capturing the similarities and differences among the possible business processes of the target domain. To realize a business process family into a concrete business process model, the variability points of the business process family need to be bounded. The decision on how to bind these variation points boils down to the stakeholders' requirements and needs. Given specific requirements from the stakeholders, the business process family can be configured. This paper formally introduces and empirically evaluates a framework called ConfBPFM that utilizes standard techniques for identifying stakeholders' quality requirements and employs a metaheuristic search algorithm (i.e., Genetic Algorithms) to optimally configure a business process family.

Search based software engineering for software product line engineering

“…Ognjanović et al extract a business process model from a set of business process instances and then apply a genetic algorithm to search for business process configurations based on stakeholder requirements [126]. The contributions target the business community rather than the SPL community.…”

Multi-Criteria Decision Making for Optimal Configuration of Business Process Model Families

“…).In the approach that is used through the paper, the variation points are managed and configured by means of both models, i.e. feature models and business process models, respectively [21].…”

“…Given that more and more services with identical or overlapping functionality are available [5], consideration of non-functional preferences is essential for selecting an ideal service among alternatives [34]. However, finding the most appropriate selection of services based on different kinds of stakeholders' preferences presents a great challenge, since: (i) there is no general framework for eliciting and analysing different kinds of preferences [19]; and (ii) automatic service selection, defined as a maximization of some quality measure of configured BPM regarding to the stakeholder's preferences, is an NP-hard problem [21].…”

“…The second aim was to develop fuzzified measurements of service quality on the basis of CS-AHP results, which was then integrated into a fitness function and GA search technique was used for evaluation of sets of available services. The third aim was to analyse the accuracy of the proposed approach, followed by the assessment of the impact of different variability and uncertainty elements of the whole approach and finally, our aim was to compare the effectiveness of the findings with prior research [15,21].…”

Selection and Configuration Optimization for Customizable Cloud Services

“…High-variability applications require different service configurations for specific requirement, which is the process of selecting proper cloud services with different quality [3]. In order to offer cost-effective solutions to multiple tenants in the cloud environment, customizable cloud services are necessary, i.e., the ability to satisfy multiple tenants simultaneously based on one customizable cloud services [4]. Currently such customizable cloud services are often developed on an ad-hoc basis.…”