An evolutionary multi-objective framework for business process optimisation

Abstract

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.

Introduction

A business process is perceived as a collective set of tasks that when properly connected and sequenced perform a business operation. The aim of a business process is to perform a business operation, i.e. any service-related operation that produces value to the organisation. In response to increasingly volatile and competitive environments, organisations are examining how their core business processes may be re-designed to improve business performance and market responsiveness. The design and management of business processes is a key factor for companies to effectively compete in today's volatile business environment. By focusing on the optimisation and continuous improvement of business processes, organisations can establish a solid competitive advantage by reducing cost, improving quality and efficiency, and enabling adaptation to changing requirements. It has been argued in [23] that for systematic and holistic business process planning based on business process automation, there must be techniques that support modelling, analysis and optimisation of business processes.

The focus of this paper is on business process optimisation and analysis and not on modelling [1], [12]. Business process optimisation is a new area that provides a formal methodology for improving business processes based on specific objectives. To achieve formal improvement however, business processes also require a formal representation. Despite the abundance of business process modelling techniques in literature, only a few are capable of capturing a business process in a quantitative way so that it can be optimised.

The aim of this paper is to investigate the application of evolutionary multi-objective optimisation to the new domain of business process optimisation. The rationale for adopting a multi-objective optimisation approach towards business processes lies in the following:

• Business process optimisation is inherently a multi-objective optimisation problem due to the variety of factors that a business process can be evaluated with. Dealing with multiple objectives can make this research more appealing and applicable to real-life business process optimisation problems.

• Evaluation business processes based on a series of relevant factors ensures that this research is versatile in dealing with different objectives for different business goals at a time. The capability of simultaneously addressing a series of customised quantifiable objectives ensures the generality of the research and its potential applicability in a wider context of business process improvement initiatives.

• On the contrary, a single-objective optimisation framework focuses on a particular objective (e.g. cost reduction) and thus loosing its generality and its advantages over context-specific business process improvement approaches that target a specific aspect of a business process (e.g. Six Sigma).

This paper presents the first research attempt aimed at addressing the challenges of business process optimisation using evolutionary multi-objective optimisation algorithms. It introduces a business process representation approach and an evolutionary multi-objective optimisation algorithm that constructs optimised business process designs based on specific process requirements. The utilisation of evolutionary techniques provides the advantage of working with a population of designs, thus providing the capability of generating a set of diverse optimised business process designs in the presence of multiple objectives.