Extending evolutionary multi-objective optimization of business process designs

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019.Optimizing a problem to produce a set of improved solutions is not a new concept. Many scientific areas have been benefited by the application of optimizations techniques and so have business processes. The competitive business environments have led organizations into examining and re-designing their core business processes, aiming for improving their performance and market responsiveness. The optimization and the continuous improvement of business processes within a company, can give the advantage to the company to be more competitive by reducing its costs, improving the delivery quality and efficiency, and enabling adaptation to changing environments. This thesis focuses on business process multi-objective optimization with evolutionary algorithms. There have already been optimization approaches with evolutionary algorithms for business process optimization problems that demonstrated rather satisfactory results. This thesis aims to improve and extent those approaches by providing a revised and refined version of an existing business process optimization framework by Vergidis (2008), that incorporates a pre-processing technique for enhancing the efficiency of the employed Evolutionary Multi-objective Optimization Algorithms (EMOAs), a new process composition algorithm that make the new framework capable of fulfilling more real-life constraints and handling more complex problems and many other features such as ease of use, more efficient I/O, better interactivity and easy maintenance. The proposed pre-processing technique was tested as a standalone procedure and demonstrated satisfactory results, managing to reduce drastically the problem dataset of all scenarios examined. The results of the whole optimization framework for the real-life scenarios examined, were very promising and indicated that the framework work as expected. It can automate the process composition and identify alternative business process designs with optimized attribute values

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 framework's 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