Tipología de las Micro y Pequeñas empresas

Simulation studies are often utilised to analyse how systems react to interventions (actions initiated on purpose as a means of improving the system’s performance) or outside influences (often called scenarios). As it may be too costly or time consuming (or both) to try and test these effects in reality, a simulation study is done. Usually a simulation model takes the form of a computer program. Literature describes several types of simulation and reasons for simulating. The view of Robinson

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(2004) is provided in Subsection 3.2.1 The Subsection afterwards describes the process of conducting a simulation study. For that purpose Law (2006) is used.

3.2.1 An introduction to simulation studies

The first question we need to ask ourselves when starting to review various options for simulation is “what is simulation?”. Quite simply put a simulation is an imitation of a phenomenon (in the field of production and logistics usually something like a production system) that can be observed in reality. Robinson (2004) states that there are several types of systems:

1. Natural systems: these all originate from the universe;

2. Designed physical systems: physical systems that are designed and built by humans (these are tangible);

3. Designed abstract systems: systems that are designed by humans (theories, math and literature, none of which is tangible);

4. Human activity system: These usually concern social behaviour. Obviously a waste disposal station is a designed physical system.

The next question that is of importance is “why should one use simulation?”. Robinson mentions three major reasons for using simulation. First on his list is variability. Many operation systems are subject to a form of variability. Popular sources for variability are the arrivals of jobs to the system, the processing times, or the nature of the jobs arriving at the system. Another reason for using a simulation model to analyse a system is the uncertainty surrounding the effect interconnectedness of various parts of the system has on the system. Since this is difficult to model analytically, the best next thing is to simulate the events to observe what those effects might realistically be. The last issue Robinson (2004) brings forward is the complexity of systems. Some systems are too complex to analyse analytically. This statement brings forth another issue. “How do you determine what the complexity of the system is?” Robinson remarks that there are many ways to define complexity yet by employing combinatorial and dynamic complexity one can get a very good indication of complexity. Combinatorial complexity relates to the number of components in a system that leads to different system combinations. In job shops the combination of operations that a job can follow in the system can be made up from a great many number of operations. Job shops are therefore an example of combinatorial complexity.

A waste disposal station displays examples of variability (in arrival process, service process and the variability in jobs), interconnectedness (the business of stations affecting the behaviour of the process for other visitors) and combinatorial complexity. Since there are many decision variables in the process of designing a waste disposal station and therefore many combinations of settings that are possible it seems prudent to state that the combinatorial complexity of a waste disposal design is indeed high.

A third question that needs answering is “when should a simulation study be conducted?”. Robinson sees this question differently from the why-question. He states that in general terms simulation studies (of the discrete event variant) are performed for modelling systems that can be modelled as queueing systems. Conveniently enough that is just the type of system our waste disposal station is (and DES is the type of simulation under consideration). A simulation study is therefore a good option for modelling in our case.

So, how do we go about creating a simulation model for this system? First of all, we need to determine what kind of simulation to perform with the model. There are several forms of simulation according to Law (2006):

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1. Discrete Event Simulation: This type of simulation models a system as it changes over time. These changes occur at discrete time intervals during so-called events;

2. Continuous Simulation: This simulation modelling type concerns a system in which the variables dictating the state of the system change continuously;

3. Combined Discrete-Continuous Simulation: Some systems evolve under influence of both discrete and continuous influences. For these cases the use of a combined discrete-continuous simulation is fitting.

4. Monte Carlo Simulation: This is a modelling method that uses random numbers to solve stochastic or deterministic problems;

5. Spreadsheet Simulation: If the system under study is not too complex a discrete event simulation or a Monte Carlo simulation could be done in a spreadsheet.

For our case, a discrete event simulation seems the most fitting choice as the state of the waste disposal station is determined by the number of visitors at certain locations within the system. These states change according to events like arrivals of new visitors or visitors starting to dump at a certain container or leaving said container upon finishing the dumping process.

Now, a simulation study always occurs according to a structured process. We have dedicated an entire subsection to the description of this process. Law has a thorough description and Mes (2012 ) adapted this list of steps for a simulation study to give more guidance concerning the grouping of the activities done in a simulation study and the phases of a project in which they should occur. The entire process is described in Section 3.2.2.

35 3.2.2 Conducting a simulation study

Law (2006) lists ten steps that need to be taken in a simulation study. The adapted list by Mes (2012) (see Figure 2 below), gives a clear overview of all the steps taken in the execution of a good simulation study.

Figure 2 The 10 steps of any simulation study (Mes, 2012 )

1. Problem definition

This first stage is not just a necessity for simulation studies. Defining the problem involves translating the problem description as provided by the client or employer into a problem that is indeed active, needs attention and provides an opportunity to act on. This translation is not always necessary, but it may happen after some preliminary research that the problem as stated is just a symptom of the real problem. In the final problem description the performance measures used to evaluate the system are decided upon. The scope of the model and the system configurations to be modelled are determined as well along with the time frame available for the study.

2. Model construction

In this stage the model is first defined in a conceptual (paper) model carefully describing the workings of the model, the input and output, the logic and the assumptions and simplifications made while constructing the model. During this stage data collection occurs as well. This data contains both data needed to estimate input parameters and data that can be used later on to assess the model’s validity. When agreement is reached about the conceptual model the computer program is constructed. Test

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runs determine in this case whether the programmed model indeed operates according to the logic of the conceptual model. Once the model has been validated we can move on to the next stage, namely the experimental design.

3. Experimental design

In the final stage the experiments to be conducted are designed in such a way that the goal of the simulation study is reached in a timely and cost efficient manner. After the design for the experiments has been completed the experiments are run and the results are analysed. Once analysed, the results are carefully documented, reported and the results are used.