Business process optimisation

Optimisation is considered as a class of business process performance analysis. Zhou and Chen [83] reported that business process optimization aims at reducing lead- time and cost, improving the quality of products, and enhancing the degree of satisfaction among customer and personnel so that the competitive advantages of organisations can be maintained. However, despite the importance of optimisation, there is no systematic optimisation methodology for business processes [68]. Several approaches exist for analysis of business processes. Vergidis et al. [84] stated that business process optimisation is the automated improvement of business processes using prespecified quantitative measures of performance. They identified three main problems that obstacle business processes optimisation:

1. There is a lack of business process representation suitable for quantitative mod- elling. Most business process modelling techniques use visual diagrammatic approaches which do not allow quantitative analysis.

2. Methods of business process optimisation have been mostly manual and based on simplistic or isolated cases without generalisation capabilities.

3. There is no attempts in business process optimisation under multiple criteria. Chen and Zhang [85] proposed an optimisation approach based on the used of al- gorithms to schedule large-scale business process workflows with various quality of service (QoS) parameters. This algorithm enables users to specify their own QoS preferences, and its objective is to find a solution that meets all QoS constraints and optimises the user-preferred QoS parameters. Van Hee et al. [86] proposed an algorithm to determine the optimal allocation of resources in order to maximise the time performance.

Kamrani et al. [87] presented an approach which aimed to optimise overall busi- ness processes by optimally assigning tasks to agents. Two main categories of pro- cesses, assignment-independent and assignment-dependent, are distinguished, and then each of these categories is divided into three types: deterministic, Markovian and non-Markovian processes. Two algorithms were introduced for these types of processes, one of which finds the optimal solution and the other is applicable to a large number of critical tasks provides a near-optimal solution.

According to Van der Aalst et al. [65], formal business process languages are as- sociated with analytic techniques that can be used for investigating the properties of processes, but an optimisation approach based on executable process languages was not observed in literature. One of the most important technique related to business process optimisation is workflow scheduling. It considered as the successful optimisation approach in business process workflow area [88]. In the next section, the details of workflow scheduling is presented.

2.3.1.1 Resources scheduling

Resource scheduling is considered as the main approach used in business process optimisation and is important in this study where the framework is used for the automation of real workflow scheduling problem. Generally speaking, scheduling is a decision making process to determine when, where and how to produce a set of products, given specific requirements concerning a time horizon, a set of limited resources, and processing recipes [89]. In the context of business processes, Van der Aalst [88] defined the scheduling problem as the optimal allocation of scarce resources to tasks over time. Another definition presented by Senkul el al. [90] is that Workflow scheduling is the problem of finding a correct execution sequence for tasks over time while satisfying constraints within the model. Business process per- formance may vary in terms of time and cost depending on the workflow scheduling used. An effective methodology for workflow scheduling means that business process take less time, costs less and is of higher quality. This indicates the importance of workflow scheduling for organisations.

Floudas and Lin [91] reported that mathematical programming, and especially Mixed Integer Linear Programming, has become one of the most widely explored methods for scheduling problems, because of its rigour, flexibility and extensive modeling capability. Josef Kallrath [92] claimed that mixed integer optimization can provide a quantitative basis for decisions and allow complex problems to be coped with successfully as a useful technique to reduce costs and support other ob- jectives.

There are many researches have been presented in literature concern workflow schedul- ing approach. Floudas and Lin [91] presented an overview of advances in MILP- based approaches to process scheduling problems. They proposed a classification of types of mathematical models and identified the strengths and limitations of each. Pinto and Grossmann [93] proposed a classification of scheduling problems and an overview of assignment and sequencing models used in scheduling techniques with mathematical programming . Yash et al. [21] presented a static optimisation workflow model using a stochastic programming technique to establish a Service Level Agreement (SLA) and satisfying the end-user workflow QoS requirements. Buyya and Tham [94] presented a cost-based workflow scheduling algorithm for time-critical applications. Their proposed scheduling algorithm develops a workflow schedule which minimises execution costs and meets the time constraints imposed by the user. Senkul et al. [95] presented a framework for workflows whose correct- ness is determined according to set of resource allocation constraints and develop techniques for scheduling such systems. Combi and Pozzi [96] focused on the tempo- ral aspects of business process scheduling and presented conceptual organisational model for task assignment policies including time and skills of agents, deadlines for task completion, and other temporal aspect which improve the overall performances of scheduling activity of business processes. Brataas et al. (1997) consider the evaluating of the performance of workflows they proposed a framework involving both manual and automated activities. The lack of this framework is have not been adopted automation.

Several important observations can be made from the above survey of business pro- cess performance analysis and optimisation. Firstly, quantitative analysis has not received as much attention as qualitative analysis. Most existing modelling tech- niques use visual diagrammatic methods which cannot be used to conduct quanti- tative analysis. Business process performance analysis and optimisation techniques adopted different types of algorithms, which use different types of parameters in their execution. Specifying these parameters by modeller and analysts takes time and effort, and is error prone. This highlights the importance of a generic method- ology that can overcome these shortages and support the modeller and analysts in order to conduct performance analysis and optimisation in easy and flexible way.