XVI International Scientific Conference on Industrial Systems (IS'14)
Novi Sad, Serbia, October 15. – 17. 2014. University of Novi Sad, Faculty of Technical Sciences, Department for Industrial Engineering and Management Available online at http://www.iim.ftn.uns.ac.rs/conferences/is14/
Applying a suitable simulation approach for processes on different management levels Bojan Jovanoski Teach Ass, University Ss. Cyril and Methodius in Skopje, Macedonia,
[email protected]
Radmil Polenakovik Prof, University Ss. Cyril and Methodius in Skopje, Macedonia,
[email protected]
Valentina Gecevska Prof, University Ss. Cyril and Methodius in Skopje, Macedonia,
[email protected]
Robert Minovski Prof, University Ss. Cyril and Methodius in Skopje, Macedonia,
[email protected]
Abstract The business systems are becoming more and more complex. In order one to improve them, they need to be more thoroughly analysed. Simulation and modelling is one of the approaches that can accomplish that. It does not interrupt the system and yet can come up with numerous experiments and solutions for a better performance of the system. The use of simulation modelling has been largely widened since the original use in the military. Now it’s a standard tool for business modelling. In one organization there are different management levels and every level has appropriate processes that differ between each other, especially considering the level of the determinism. Due to these differences, in order to model them correctly different approaches have been developed. In this paper, Discrete-Event Simulation, System Dynamics and Agent Based Modelling have been analysed. Their advantages have been shown and one application of each for a particular study. Key words: Business modelling, Simulation approach, Simulation cases, Simulation models.
1. INTRODUCTION The advances in Industrial Engineering (IE) have gone a long way since the early beginnings and the experiments of Taylor, Gilbreth, Babbage, Towne and others. Not that much in the general ideas of the field, but in the direction of tackling even the smallest details possible. In order to do this, the complexity of the problems grew, along with that the data needed to be obtained and processed was also getting bigger. The IT technology played huge factor in keeping the Industrial Engineering alive and constantly being in trend. Not only because of the hardware possibilities and the calculations that could have been made now, but also from the point of view that many software packages have been developed in order to solve some kind of an IE problem. There are numerous software solutions for finding an optimal layout, managing production processes, tackling ergonomic issues, calculating cost/profit etc. (the intention is not to name vendors here). Simulation and modelling has been widely accepted as one of the most important aspects of the Industrial
Engineering. The application and use of the simulation models has grown exponentially since the 50’ until today. This is mainly because of the advances in the computation field, but also because of the increased number (percentage) of acceptance by the academia and the industry, [1]. The complexity of the simulated issues has been adapted to the complexity of the real world cases and has risen proportionally. That is why no single simulation approach can be used in every scenario possible. In that direction, in Figure 1, a schematic view is presented of the type of processes that are obvious in one enterprise (http://msdn.microsoft.com/enus/library/bb220801.aspx). The well-known managerial pyramid is used in order to show the three different management levels. The processes differ in the frequency of execution, the role of the person that is in charge of it and the time it takes for one to be completed. These differences make it very hard to achieve modelling all of them in one single simulation approach.
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Figure 11. Different frequencies and processes on a particular management level
Based on the previous experience, the three management levels were separately analysed based on these criteria: Time period – how is the time character of the analysis? Level of detailing – what is the degree of details by the analysis/modelling? Internal interactions – what is the need/capability for modelling the correlation between the elements on a same level? Modelling perspective – from which perspective the things have been analysed? Complexity – what is the number and complexity of the elements and their influence when analysing/modelling the system? Goals – what are the set goals?
2. SIMULATION APPROACHES In this section, a brief overview will be given about the three most commonly used simulation approaches: System Dynamics, Discrete-Event Simulation and Agent Based Modelling.
2.1 System Dynamics
Table 1 presents an example of how the output of the analysis looked like for the operational management level. The same analysis was performed for the other management levels.
System Dynamics (SD) is a relatively new technique that has been populated in the last 20 years. The basic principle underlying system dynamics is that the structure of a system determines its behaviour over time, [2, 3]. SD is all about the whole and looking at the system as a unit. In normal cases, a lot of people use the divide-and-conquer system in order to solve complex problems. The philosophy of SD is that every element is connected somehow with other element(s) and those relationships determine how the system performs over time. It is best used when modelling very complex systems that are very hard to perceive and understand.
Table 1. Description of the characteristics of the operational management level Criteria Description Time period Short term Level of detailing High level of details Internal interactions Low to medium level of interaction between the elements Modelling perspective Bottom to top Complexity Medium complexity Goals Short-term goals
There are two main approaches that help define a SD model. The first one is the causal loops (and feedback loops), which are widely spread and very useful. Most of the time, they are the first step in developing a SD model, helping in the conceptualisation. The second tool is the stock and flow diagrams, which aid to describe the model using data. The easiest way to describe this is to think of models like system of water tanks with pipes and valves, [4].
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2.2 Discrete Event Simulation Discrete-event Simulation (DES) is a more widely established simulation technique, [5]. “The system is modelled as a series of events, that is, instants in time when a state-change occurs”, [6]. The models are stochastic and generally represent a queuing system, [7]. From the beginning until now, the models are based on a specific code that manages the simulation. At the beginning, DES was developed and used in the manufacturing sector. But, as the times have changed, so have the areas where DES has found its applicability (hospitals, public offices, document management etc.).
2.3 Agent Based Modelling Agent Based Modelling (ABM) has been one of the most popular simulation approaches in the last decade. Maybe this is because one of the most focal points of ABM is to model social systems and activity. Since the burst of the social media like Facebook, twitter, etc., ABM has been closely connected to this phenomenon and has been used in order to model problems connected to this. Examples are spreading the word of
Table 2. Overview of the simulation approaches Approach DES Criteria
mouth about the product/service, potentials of new marketing channels etc, [8, 9]. The idea behind ABM is the agent which can be programmed to have an artificial intelligence. Based on state charts for decision making and flow charts for movement, these agents move like ants in the model.
3. COMPARISON OF THE SIMULATION APPROACHES According to the experience gained in the past with the experimentation of these three modelling approaches and the analysis made by several researches [10-18], an overview is given (Table 1). The modelling approaches are compared based on the goals, the modelling perspective, philosophy, representation of the real system, interpretation of the results, data sources and the complexity of building a model in the suitable environment. As it can be seen from it, big difference appear when these modelling approaches are put side by side and compared. The perspective that is used largely in the DES environment I micro level, whereas SD is more focused on the macro level; the ABM is seen as an approach that can model both perspectives.
SD
ABM
Goal
Decisions: optimization, assumptions and benchmarking
Creating policies: gaining knowledge about the system
Behaviour: determining the dynamics or patterns through agents
Perspective
Micro
Macro
Micro-macro
Modelling philosophy
Randomness associated with interconnected variables leads to system behaviour.
Causal structure of the system causes behaviour
Rules and states of agents create the dynamics of the model
Representation
System represented as queues and activities, processes
System represented as stocks and flows
System represented as agents
Interpretation of results
Interpretation of results require statistical knowledge
Results are easy to interpret, it does not require in-depth knowledge of stats
Results are easy to interpret, it does not require in-depth knowledge of stats
Data Sources
Primarily numerical, tangible data with some informational element
Broadly drawn: Subjective , judgemental data held in the form of mental maps is also crucial
Data from surveys, drawn from some analysis
Complexity
Complexity increases exponentially with size
Complexity increases linearly with size
Complexity increases exponentially with size
The makers of the first simulation software package that can be used to model and simulate the most frequently used simulation approaches, Anylogic, have presented a scheme for better understanding of the capabilities of each of them (Figure 2). According to this type of mapping that is widely used by the simulation community, each of the simulation approach has “found” its place in the application on a different management level. For instance, DES is more details oriented and easier to manage when modelling straight
line processes. Because of that, this simulation approach has been widely used in modelling production systems. Because of this big diversity and the differentiating advantages of the simulation approaches the decision of choosing a simulation approach for a particular problem/case is not an easy one. In .the next chapter, three cases will be shown where these simulation approaches have been used as most suitable as it can.
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Figure 2. Approaches in simulation modelling on abstraction level scale, [13]
4. APPLICATION OF THE SIMULATION APPROACHES So far there have been models developed in all previously mentioned simulation approaches. Three different cases are shown in Figure 3. The first one is a car repair shop developed in DES. As it can be seen, it is a straight going process, moving the car from one station (step) to the other. It uses men as resources, meaning that no station can operate without an interaction from human. This model was constructed in order to test the utilization percentages of the machines and the workers. The main analysed result was the time a car stays in the shop, from the point it arrives until it leaves the shop. The goal was to see if additional sets would decrease the waiting time and queue, having in mind the utilization factors. DES was perfect for this purpose because it can track the time very easy and can present the processes and steps in a very meaningful fashion. The second model was created in SD based on a case study, for one amusement park. The main analysed result was the customer satisfaction, or the visitor satisfaction. Several key influencers were modelled like the number of rides in the park, the number of restaurants and toy shops, price of ticket, number of visitors, size of parking space, size of queues etc. As it can be seen from the picture, the model is far from linear. All these factors are connected with each other – directly or indirectly.
there would be no parking space for late arrivals etc. So, everything is connected in a causal loop and every change in the model, triggers change on other aspects as well. The model was also connected with a performance measurement system and its key indicators. Part of them were analysed, experimented and some recommendations were given. The third model was done in ABM environment. It analysed the penetration level of each of the three most relevant supermarkets in the capitol Skopje. Based on the word of mouth effect, money spent on marketing and promotions, the people choose where to buy. Distance to a supermarket was also implemented as a decision factor for the consumers. The state chart in the figure shows the logic of each agent in the model and is only one of the many implemented. Based on a three months simulation run, the agents decide where to buy and get a specific colour based on the preferred supermarket. After the analysis, it was clearly shown that some areas are grey (not coloured) which meant that this is the place for opening a new supermarket and getting new customers. The huge potential of each of the models could have not been presented here due to the space limit. The models were a fundamental block in the decision making process in each of the cases – whether to buy new equipment (DES), rule the ticket entry price (SD) or open a new market on a researched location (ABM).
For instance, if the price of the ticket is reduced to an amazing 1$ then the visitors would be satisfied based on the price criteria. But, this would attract enormous number of new visitors, the queue lines would grow exponentially, time for waiting for a ride would increase,
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ABM
SD
DES
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Figure 3. Examples of models for the simulation approach
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5. CONCLUSION In this paper an analysis and comparison has been given for the three most commonly used simulation approaches – Discrete-Event Simulation, System Dynamics and Agent Based Modelling. Their advantages in modelling a certain case/model were taken in consideration when choosing the right simulation approach. Three different cases have been presented that were modelled in each of the simulation approaches. DES was perfectly suitable for the detailed time analysis that was needed to perform during an automobile repair shop; SD was an unflawed match when needed to model different impact factors and their weight value on different parameters; ABM was ideal when artificial intelligence needed to be inserted in a model in order for the agents to make logical decisions.
6. REFERENCES
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