Business process optimization is one of the main activities in organizations, but it is long and usually time-consuming work. One way to automate this process is to use business process simulation which is one of the most widely applied method for analysing and improving business processes. However, standard simulation models are static e.g. control flow of the process is defined beforehand. In contrast, the concept of dynamic business process states that a set of business process activities might change at a certain point in time. Therefore, in this paper we suggested to combine dynamic business process simulation tool with multi-objective optimization in order to automate business process optimization. The paper highlights main principles of the simulation based multi-objective simulation, describes general problem, presents research hypothesis and research questions.
In the modern business world there is a frequent need for enterprises to revise and
improve the structure of their business processes. Business processes are the core of
all organisations, therefore focusing on the optimisation of business processes,
organizations can effectively compete in today’s business environment
Simulation of business process allows to analyse various business scenarios under
different circumstances, provides an understanding of the most important factors
affecting the process. However, standard simulation models are static e.g. control flow
of the process is defined beforehand. In contrast, the concept of dynamic business
process states that a set of business process activities might change at a certain point
in time due to the changes of business process context, therefore a sequence of
activity execution cannot be predefined in advance
Therefore, the goal of this research is to combine the multi-objective optimization with dynamic business process simulation tool. This method may result in new approaches and more efficient ways to improve business processes. The following benefits may be achieved: 1. more than one optimisation function can be selected; 2. at any time, additional rules can be added to the simulation model; 3. several suitable solutions can be found; 4. simulation results can be used for further business process analysis; 5. already created models of business processes can be used to create simulation models.
In this paper, we present an ongoing research on dynamic business process simulation and optimization. The paper highlights main research problems, principles of the dynamic business process simulation approach and presents the model of simulation based multi-objective optimization for dynamic business processes. Also presents research methods and plan how the tool validation will be performed.
The rest of the paper is organized as follows: Section 2 reflects the state of the art in dynamic business process simulation and optimization. Section 3 presents the problem statement. Section 4 describes research methods which will be used in this research. The results and analysis will be discussed in Section 5. The last section concludes the paper. 2
Two main parts of this work – the dynamic business process simulation and multiobjective optimization – will be described in this section. 2.1
General Problem Definition
A multi-objective optimisation problem involves many goal functions which are to be
either minimised or maximised. All goals are equally important and must be
accomplished during simulation
Dynamic Business Process Simulation
In the simplest business process simulation case, a process has decision points where
a human or an automated system decides next step based on predefined rules. In more
sophisticated approaches, as in
The idea of dynamic business process states that a set of activities might change at
a certain point in time due to the changes of the business process context. However,
the dynamic business processes do not have a pre-defined sequence of steps
Due to its ability to use data generated during the execution of the processes and
ability to use complex contextual rules for controlling the execution of the process
Business Process Optimization
In the article
Table 1 classifies the techniques that are used in practice for different optimization problems. Math programming is used to optimize a real function by systematically choosing input values from the allowed set. Furthermore, sometimes a function might be non-smooth or time-consuming to evaluate therefore to optimize such function require to use algorithms which do not use derivatives or finite differences other known as derivative-free optimization. Stochastic programming is used when uncertainty must be included in the optimization problem. Whereas deterministic optimization problems are formulated with known parameters, real world problems usually include some unknown parameters. Simulation optimization is used when other methods are too expensive because of the complexity of the model. Computer-based simulation provide information about the model behaviour. In this method, the different sets of input values are varied and then the effect on the described objectives are observed.
In
They suggested that business process models should be mathematical in order to be able to run optimization algorithms. However, as the authors have mentioned before the large part of the business process models are already graphical therefore it is possible to create simulation models of them by adding necessary parameters. By doing like this we can create simulation models and save time and money. Also, the creation of the mathematical model could be complicated task, other than the creation of the simulation model. 3
The main problem of this research is that the accuracy of existing simulation based multi-objective optimization methods, for dynamic business processes, is not enough. We are planning to apply evolutionary multi-objective optimization algorithms in the context of dynamic business processes simulation. As we described before there is a quite clear need for automated way to optimize business processes and facilitate their improvements. Also, it is required to take into account the dynamic nature and unpredictable factors of the business processes. Therefore, the following research hypotheses are defined to help find an answer to the defined problem: H0: Dynamic business process simulation can be used to optimize business processes. We assume that there is a solution for this optimization problem.
H1: The optimization goal can be defined as the set of resources and context variables; H2: Simulation based multi-objective optimization is more accurate than the alternative ways to optimize business processes; H3: Data mining methods can be applied to gather additional information about the simulation model and improve it.
Based on the problem and hypotheses the following research questions are defined in this paper: RQ1: What methods and techniques exist that use computer simulation to optimize business processes? What are the limitations of these methods? RQ2: How existing optimization methods can be applied to the dynamic business process simulation? RQ3: What is alternative ways to optimize business process? RQ4: How simulation data can be used to improve the simulation model? What methods can be adapted? 4
The research approach is divided in different areas, each one following specific methodology.
Literature Review: An extensive literature review is performed to present the current
state of research related to dynamic business process simulation and multi-objective
optimization. This helps to get a clear understanding of the existing methods of any
related business process optimisation approaches along with their strengths and
weaknesses. The following good practices of systematic literature review will be
applied
Improvement of existing dynamic business process simulation tool: This research must create additional functionality to already existing tool to enable dynamic business process optimization. Based on the research findings the objectives of the simulation tool are defined.
Development of the simulation based multi-objective optimization tool for dynamic business processes: We will apply existing evolutionary multi-objective algorithms to get optimal input data for the specific process simulation model. The prototype for the proposed method will be implemented using C# programming language, .NET framework and WPF libraries.
Experimental analysis: Dynamic business process simulation will be used to evaluate the correctness of the prototype and the results.
Validation of the tool: A set of different business process scenarios reported in literature will be tested within the prototype.
What Has Already Been Achieved?
The dynamic business process simulation tool has been implemented. The Fig. 1
shows the simulation approach which has been implemented in the DBPSim tool
Resources
Context
Activities Select Activity No
Historical Data
Perform Task
Goal Yes Reached?
Update Simulation
State
Update Simulation
Statistics Activity Selected?
No Is Queue Yes Empty?
No
Yes
Add All Activity Tasks to Queue
Queue
Resources Context Activities The sub process Select Activity starts with simulation engine selecting a set of possible activities based on the information in the current simulation step. After a set of possible activities is generated, activity evaluation is performed. Evaluation is twofold – the rules of the activities are evaluated to see if they can be performed at all, and the possible benefit to the process is evaluated. The benefit value is calculated based on historical data – whether an activity leads to dead end or is not useful to the end. Upon full evaluation, the simulation engine selects an activity which can be executed and is the most benefiting to the process execution. This selected activity is returned back to the simulation engine for execution. 5.2
What Has Still to Be Done? The idea of this research is to use evolutionary computation algorithms to find optimal set of resources for the specified business process. By optimizing input data of simulation, we can optimize entire business process. First of all, it is necessary to update the DBPSim tool with the components which will be responsible for the selection of input resources.
The Fig. 2 shows the possible approach of simulation based optimization. First, it is necessary to create the primary simulation model with the required information. Then the objectives of the process should be defined. As mentioned before more than one objective function can be defined. Next step is to execute selected evolutionary algorithm. First time initial input variables will be created at random. Then standard steps of evolutionary algorithms like evaluation, selection, mutation and recombination are executed. After this step a new set of resources is generated and then perform simulation N times. All results are recorded into the database for analysis. After all iterations of simulation is over then the simulation result is evaluated. Then if termination criteria are not reached Execute Evolutionary Algorithm sub process repeats again. Otherwise, process optimization process is over. At the end the best set of resources will represent the outcome of the optimization. The new automatic way to optimize business processes by using dynamic business process simulation tool will be created at the end. The following benefits may be achieved: 1. 2.
more than one optimisation function can be selected; at any time, additional rules can be added to the simulation model;
several suitable solutions can be found; simulation results can be used for further business process analysis; already created models of business processes can be used to create simulation models. 6
The object of study in engineering sciences is an artefact in a context of use
In this paper the idea how to create simulation based multi-objective optimization method for dynamic business processes has been presented. The implementation possibilities have been described. The paper provided explanations of the simulation based multi-objective optimization mechanism.
The main conclusions of the paper are as follows: 1. Literature review has shown that multi-objective optimization algorithms can be used in the simulation and produce decent results. 2. Suggested tool will help business analyst and provide a new way to improve business processes. The results presented in this paper are promising and it is necessary to continue research in the area of the simulation based dynamic business process optimization.