SimBusPro: A Simulation-Based Decision
Support Tool used for the Optimization of
Business Processes running on the Cloud
Ahmet Özkök, Ali Ercingöz, Halit A. Dönmez,Tansel Dökeroğlu
, Veysi İşler
Simsoft Computer Technologies Ankara/TURKEY{ahmet.ozkok, ali.ercingoz, halit.donmez, tansel.dokeroglu, veysi}@simsoft.com.tr
Abstract - In this study, SimBusPro, a novel business process modelling and optimisation tool that runs on Cloud, is introduced. The tool is developed during a project called SCI-BUS (Scientific Gateway-Based User Support) under the seventh Framework of the European Commission. SimBusPro has a visual editor that uses BPMN 2.0 notation. A wide range of commercial and academic workflow processes can be modelled, simulated and optimised using SimBusPro. The distributed computational architecture of the tool is capable of completing complex simulations in reasonable times. In addition to these features, with the alternative resource planning module, SimBusPro allows optimal resource assignments to be made for the processes that have been modelled.
I. INTRODUCTION
In the contemporary business world, it is necessary for firms to operate in a manner that is both cost-effective and responsive to changing conditions so as to ensure their existence. Thus, firms must continually analyse their business processes in their pursuit of continuous improvement, thereby bringing about continual improvements in their resource utilisation efficiency. One of the most prominent ways in which this continuous improvement can be achieved is via the use of business process modelling, and simulation and optimisation software [1].
SimBusPro [2], which was developed during a project called SCI-BUS (Scientific Gateway-Based User Support) under the 7th Framework of the European Commission, is an example of such a software program. In essence, SimBusPro is a simulation and optimisation-based decision support tool that can be run on cloud [3]. By using SimBusPro’s modelling editor, it is possible to design and develop workflow diagrams that are in line with BPMN 2.0 [4, 5] standards. Once such a model has been established, its simulation, using parameters defined and entered by the user, can be carried out, which, in turn, will yield highly accurate and precise simulation reports containing various graphs representing miscellaneous information about the process that has been modelled. Via the results that are displayed in these reports, it is possible to make analyses that will lead to the achievement of increased efficiency in the utilisation of the resources of the processes that have been modelled.
Business process modelling software programs, despite having the ability to model business processes with consummate ease, are typically limited by their ability to attain simulation results in reasonable times. SimBusPro is an exception to this and can generate simulation reports of complex models rapidly as it is compatible with Cloud technology. SimBusPro uses a scalable, distributed architecture in its calculating infrastructure. As a result, besides possessing the capability to perform simulations rapidly, SimBusPro eliminates any need for setup and updating, meaning that it is always readily available for use. SimBusPro makes use of a pay-per-use model, thereby allowing multiple users to benefit simultaneously in a secure environment.
In July 2014, from the results that were obtained from a modelling and simulation competition in which SimBusPro was used as the modelling and simulating tool, users provided feedback about the software, thereby enabling its weaknesses to be identified. Since then, the problems identified have been addressed and SimBusPro has become more capable of successfully responding to the needs of its prospective users. SimBusPro is now extremely accomplished in the modelling and simulation of a very broad spectrum of processes [2]. Thus far in the paper, SimBusPro’s various proficiencies have been discussed. In the second part of the paper, SimBusPro’s programming design and architecture will be discussed. The third section of the paper will discuss the simulation-based optimisation of a pizzeria, whilst the fourth section will discuss results, and planned actions of the future.
II. SIMBUSPRO’SPROGRAMMING ARCHITECTURE
Business process modelling, simulation and optimisation software programs are designed to model the service and manufacturing processes of business and firms, and to subsequently optimise their resource utilisations. Using SimBusPro, it is possible to obtain simulation data about processes in a virtual environment [6, 9]. This will enable those who opt to use SimBusPro to test certain hypotheses about their business processes without actually having to carry them out in real-life, which, effectively, will enable them save a lot of time and money.
A. BPMN 2.0 Based Visual Editor
BPMN is a universal modelling notation through which businesses are presented with a means with which they can display their internal procedures [4]. BPMN is a model established by an organization called OMG (Object Management Group Inc.) in which businesses and other such entities are presented with a means, or a platform, through which their internal business procedures in a graphical notation. This model was established in the hope of giving organization a means to communicate these procedures in a standard manner, thus facilitating the business transactions and performance collaborations between different organizations. This model will ensure that businesses understand themselves and the participants in their business more clearly and will enable them to adjust to new business circumstances quickly and with ease.
The SimBusPro editor was developed using Eclipse’s BPMN 2.0 library. Using this editor, it is possible to model a broad spectrum of business processes using all elements that are present in BPMN 2.0. In Figure 1, the fundamental elements of BPMN 2.0 are shown:
Figure 1. Fundamental elements of BPMN 2.0
Three basic elements are events, activities and gateways. Events are occurrences cannot be controlled internally - such as the arrival of a message. Events trigger the start or end of activities within a business process. Tasks are planned activities that are carried out by using the resources of business process. They may be manual based on service tool or based on the sending or receiving of messages. Gateways indicate decision points within processes where the work flow is effectively split.
B. Infrastructure of SimBusPro
The SimBusPro Gateway is used for optimizing business processes through simulation by utilizing the SCI-BUS framework. To simulate multiple models simultaneously, the application sends models to the portal, which are then transferred grid machines via the gUSE system. The SimBusPro gateway has used the BOINC Desktop Grid as its Distributed Computing Infrastructure [7, 10, 11, 12, 13]. SimBusPro’s architecture can be seen in Figure 2:
Figure 2. SimBusPro’s architecture C. Resource Utilisation Optimisation
One prominent feature of SimBusPro is its ability to generate alternative solutions of models. This allows users to evaluate the performance of a given system under certain induced and controlled changes. SimBusPro enables users to toggle with the parameters of an existing model and display the results of the simulations of different versions of this model simultaneously on the Cloud. This endows users with the ability to easily identify superior models, thereby providing them with a means to improve their processes or systems. These capabilities will provide a multitude of benefits to firms who are seeking to improve their existing business processes or systems, and also to those who are planning the development of some system but have several alternative versions to choose from.
Thus, SimBusPro can be used to optimise the number of resources that are used in a modelled system. To illustrate the way in which SimBusPro finds an optimal solution among various alternative models, let us imagine a fictional pizzeria. Let us assume further that the number of employees is not fixed and that 1 to 10 waiters, 1 to 8 delivery boys and 2 to 4 chefs can be employed. In such a case, SimBusPro would calculate all of the possible resource combinations and identify the best model with respect to some key performance indicator – such as cost or customers served. In this scenario, SimBusPro must evaluate 240 separate models (number of waiters * number of delivery boys * number of chefs) and identify the best one among the lot. Due to the fact that SimBusPro makes use of a multitude of processors upon the Cloud in carrying out its calculations, it can generate the simulation reports corresponding to alternative models in reasonable times.
D. Multi-Objective Optimisation Capability
SimBusPro has the capability of carrying out both single-objective and multi-objective optimisations [8]. Multi-objective optimisations may be carried out with respect to any parameter including cost and process times (both of which it would typically seek to minimise). In the formula for multi-objective optimisation, each measure that is aimed to be optimised is multiplied by some weighted coefficient, the size of which depends on its
where n is the number of measures that are to be optimised, wi is the weighted coefficient of measure i and riis the value that is assumed by measure i.
III. SOLUTIONOFANOPTIMISATIONPROBLEM In this section of the paper, the optimisation of the business processes of a pizzeria will be discussed. The business processes of this pizzeria were modelled and simulated using SimBusPro. Alternative models were then generated by running simulations on the Cloud, after which an analysis of the alternatives was carried out in order to identify the optimal solution.
A. Optimisation of the Business Processes of a Pizzeria The problem is as follows: A pizzeria is open for 10 hours (600 minutes) a day. When a customer arrives (once every 2 minutes) he/she chooses whether to eat in (80%) or take away (20%). The restaurant also accepts orders via phone or internet (which arrive once every 5 minutes). The details of the different processes that take place within the restaurant are given in Tables 1-3.
TABLE I. EAT IN
Activity Duration Resources used
Take a seat 1 minute
Order 5 minutes 1 waiter Pizza made 15 minutes 1 chef
Dine 15 minutes
Pay and leave 5 minutes 1 waiter
TABLE II. TAKE AWAY
Activity Duration Resources used
Go to counter 0.5 minutes
Order 2 minutes 1 clerk Payment 1.5 minutes 1 clerk Pizza made and exit 10 minutes 1 chef
TABLE III. ONLINE/PHONE ORDERS
Activity Duration Resources used
Order 2 minutes 1 telephone operator Pizza made 10 minutes 1 chef Delivery and payment 15 minutes 1 delivery boy
Return to restaurant 13 minutes 1 delivery boy
The restaurant currently employs 5 waiters, 10 chefs, 1 clerk, 1 telephone operator and 5 delivery boys, all of whom are paid $6 per hour. Assuming that the restaurant can spend up to $1,400 daily on wages, find the optimal combination of employees that is required to maximise the number of customers served during the course of a day.
From the problem description, the following BPMN 2.0 diagram may be constructed as in Figure 3.
Figure 3. BPMN Diagram of the Pizzeria
Once this model was constructed on SimBusPro and the simulation data corresponding to the problem description were filled in, the model was simulated, yielding simulation reports containing miscellaneous information about the process.
B. Analysis Results
Before alternative models were developed and simulated in the search for the optimal solution, the simulation results of the existing system were analysed. The fundamental information regarding the simulation of the existing system is displayed in Table 4.
TABLE IV. SIMULATION RESULTS OF THE EXISTING SYSTEM
Simulation period 600 minutes Tokens entering the system 420 Tokens existing the system 376 Total cost $1,320
Of the 420 customers that entered the system, whether it be in person either to eat in or take away, or via orders placed by telephone or internet, 376 managed to get served within the simulation period, which corresponds to 89.5%. Thus, in the existing system, 89.5% of customers who enter the system get served during the course of a working day, whereas the remaining 10.5% do not. This is due to the fact that they must await the resources (the employees) that have been occupied by prior customers. The expenditure on daily wages can be seen from the table to be $1,320. The objective of this optimisation problem is to ultimately serve the maximum amount of customers without exceeding the daily wage budget of $1,400. In order to achieve this goal, simulation results from the existing system must be analysed.
The resource utilization rates must be analysed to determine which resources are being used efficiently, and which are not. The resource utilisation rates can be seen in Figure 4.
Figure 4. Utilisation Rates of the Pizzeria’s Resources From this graph, it can be seen that the waiters, chefs and delivery boys are extremely busy whereas the clerks and telephone operators are not. This shows that in the prevention of the formation of queues and as per the objective of serving the maximum amount of customers, ideally, more waiters, chefs and delivery boys should be hired. However, the management must take heed not to violate the budget constraint. Thus, the resource for which most customers are waiting for must be determined, and its numbers must be increased.
Figure 5 shows the total time waited for each resource by the customers throughout the simulation time span.
Figure 5. Minimum, Maximum and Average Times Waited by Tokens for each resource
One can see from this graph that it is the waiters, chefs and delivery boys for whom the customers wait the longest periods of time. This reflects the information shown previously in the utilisation rates graph. The total time waited for the delivery boy is particularly high (assuming an average value of approximately 20 minutes and a maximum value of over 180 minutes), thereby once again hinting at the fact that the hiring of new (a) delivery boy(s) ought to be priorities in order to improve the existing system.
In Figure 6, the total number of customers that wait for each resource throughout the simulation duration is displayed.
Figure 6. The Number of Customers that wait for each Resource
The graph shows that most customers are waiting for waiters, chefs and delivery boys. There are also some customers who wait for the clerk. However, the number waiting for this resource is far less when compared to the 3 aforementioned resources. This graph also shows that none of the customers wait for the operator, who is always readily available.
To summarise, the resource utilisation graphs show that the chefs, delivery boys and waiters are extremely busy, whereas the clerk and operator are not. The waiting times of tokens for each resource graph shows that tokens do not wait for clerks or operators for prolonged periods of time, whereas queues may sometimes be formed where they sometimes have to wait for the other 3 resources. This graph shows that it is the chefs, delivery boys and waiters for which most tokens have to wait. This information shows that, in the establishment of alternative models, the numbers of the chefs, waiters and delivery boys have to be modified, whereas the numbers of the operators and clerks should be kept constant.
Our analysis shows that in our search for alternative solutions, the number of chefs will either be 9, 10 or 11, whereas the numbers of the waiters and delivery boys will take the values of 4, 5 and 6. Thus, this corresponds to 27 alternative solutions. SimBusPro will generate simulation reports of the different resource combinations. Among these alternative solutions, the one that serves the most customers whilst meeting budget constraints will be chosen as the optimal.
It takes approximately 7 minutes to run each simulation. 27 separate processors were used in order to carry out the simulation of the alternative solutions, corresponding to a total simulation time of 7-8 minutes. Among the 27 alternatives, the optimal solution was identified as the one having served the most customers whilst not breaching the budget constraint. In this new system, the restaurant is to employ 5 waiters, 10 chefs, 1 clerk, 1 operator and 6 delivery boys. In this solution, the only difference from the original system is that 1 more delivery boy has been hired. The basic information corresponding to the simulation of the new system is displayed in Table 5.
When compared to the previous system, it can be seen that the number of customers served during a typical working day has increased to 395, which corresponds to a 5% increase and that the daily wage budget of $1,400 has not been exceeded.
Figure 7 shows the number of tokens waiting for each resource in the optimal system.
Figure 7. The Number of tokens that wait for each Resource in the Optimal System
When compared to the original system, the numbers of the customers waiting for the chefs, delivery boys and waiters have decreased by 38%, 71% and 47% respectively, whereas the number waiting for the clerk has increased by 66% (due to the fact that he/she is now being kept busier by serving an increased number of customers). The mean queue length in the system has therefore decreased by 41%.
Figure 8 shows the maximum, minimum and average time waited by tokens for each resource in the optimised system.
Figure 8. Time Waited by Tokens for each Resource in the Optimal System
When compared to the original data, the average waiting times in the queues of the system can be seen to have decreased by 90%, whereas the decrease in the maximum waiting times is 78%. Thus, in the new system, customer satisfaction can be expected to rise significantly as a result of no longer having to wait as much to be served.
IV. CONLUSION
In this paper, the description of SimBusPro, a simulation-based decision support tool that makes use of BPMN 2.0, has been discussed, and an optimisation problem has been solved. SimBusPro is proficient in the modelling and simulation of a broad spectrum of business processes. Being compatible with Cloud technologies, SimBusPro offers end users a plethora of advantages including the ability to perform rapid simulations. In the future, a new optimiser module that can solve integer programming and linear programming problems is planned to be added to the software. This module will allow for rapid optimisation, and will pose an alternative to the already-present simulation module.
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