Assumptions and simplifications

In creating this model we are fully aware that we have based this model on our interpretation of the real system (the waste disposal station). Since we do not have all information on waste disposal stations, or how these operate in Canada, we decided to make assumptions that allow us to create a computer model that serves our needs. Furthermore, there are characteristics of the waste disposal station, from our perspective, that, if taken into account, would complicate our model needlessly. Therefore certain simplifications seemed in order.

Robinson (2004) advocates creating models that are as simple as possible yet contain enough detail to be credible and useful. He argues that simple models have many advantages:

1. They are easy to understand and therefore easier in use;

2. They are flexible and can be adapted easier to suit multiple situations or changes therein; 3. They require less data;

4. They run faster;

5. The results are easier to interpret.

In order to keep an easy overview, the assumptions and simplifications that have been made are divided in the major process groups that can be observed in Section 4.4.1.1, Section 4.4.1.2, and others (visitor characteristics). Each has been argued upon and justifications have been given to clarify their necessity.

Assumptions made for the entrance process:

1. There are no breakdowns of the system, i.e., we assume that once installed, any (electrical) systems like an electronic gate, work without failure. Since we have no information on these breakdowns or what the repair times might be, ignoring this is necessary.

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2. No visitors drive by if they observe that there is a queue at the entrance, i.e., no visitors are lost to the system. It is possible that in reality visitors’ behaviour is such that they may decide to actually postpone their visit. However, measuring how many visitors are dissuaded by long queues is difficult and time consuming. Visitors may after all have differing views on when a queue is too long. That is why we have assumed an identical response to queues. Visitors join in every case.

Simplifications made for the entrance process:

1. If there is no space available for a visitor at the first substation he needs to visit the visitor will wait at a buffer after the entrance and before the respective substation. In reality cars drive to the first container where they need to dump something. If there is no space there, they will wait on the side of the road or behind the current occupant of the filled space. Since this would require modelling extra buffer space for each container it seems prudent to model one buffer space for each of the substations that serves this purpose.

2. There is enough space in front of the entrance to accommodate any number of visitors (infinite buffer). Realistically this is impossible, but for the purpose of the model it is not necessary to put a limit on this. The output may show that long driveways are needed to prevent traffic from the waste disposal station from disrupting traffic on the public road. Modulo’s clients may then decide how to alter their waste disposal design to reduce the length of this queue. 3. The server at the entrance always has resources, i.e., there is always an employee present to do

the intake process for visitors. In case of emergencies this post may be abandoned. Since this means that events out of the ordinary take place ignoring them is reasonable.

4. At closing time no new visitors are admitted at the queue in front of the entrance. Usually the employees at a waste disposal station work until closing time. They do not admit any visitors that arrive after closing time. If there is a queue at closing time, then they get those visitors on the terrain, but they do not allow any new visitors.

Assumptions made for the process at substations:

1. Visitors park in the first available spot from where they can visit the first container they need to visit. If no spot is available the visitor waits in the buffer. In reality it may well be that visitors decide to drive past that container and drive or walk back later. Since this behaviour is difficult to accurately capture it is reasonable to assume the former to be true.

2. There is enough space for visitors to drive past parked cars. This may be inaccurate as it prevents blocking from occurring.

3. The entire waste disposal station has a limited capacity. As there is only a fixed number of parking spots, a capacity upper limit is placed on the waste disposal station and the

substations. Since this exact number may be difficult to measure and would be different for each site, it seems logical to decide upon a fixed number for a generic model. This maximum is not to be confused with the fourth variable mentioned in Section3.2.2.1. This maximum is the absolute maximum of cars that the station can possibly contain. The maximum that is a “variable” is a maximum that clients might want to maintain themselves in order to improve the flow of the visitors through the waste disposal station. The more cars are present on the site the more likely it is that blocking occurs. Blocking slows down the entire station and is thus best avoided by making sure that it is never too busy on the waste disposal station.

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Simplifications made for the substations and the process there:

1. Visitors visit stations in a fixed order. In reality most visitors do not have all their waste sorted and park at the first open place on the terrain near a container they need to visit. It could be that this is before or after that container. In any case, the visitor will walk between all containers that are easily accessible from their parked car. This results in too many random patterns. So for modelling purposes it is simpler to dictate a fixed routing for all visitors. 2. The next waste stream, substation and container to visit are determined as soon as a current

location is left (the front desk, a container, etc.). This is unrealistic (for the containers), as a visitor only sees whether a parking spot is available at a container as he reaches the next substation. However, for modelling purposes we have decided to program it this way. 3. To ensure that there is a possibility for cars to “drive” past cars that are blocked, as the next

waste stream they need to visit and the buffer have no free capacity, a fictional buffer has been created with infinite capacity right before roads leading to the substations. We realize this is unrealistic as well, but there was no other way of ensuring that lockdowns do not occur (blocked roads due to capacity being occupied at a substation at the end of the waste disposal station in by a visitor who is still at the beginning of the waste disposal station), we had to explore options that were readily applicable.

4. Visitors may walk up to a certain number of containers from their parking spot to throw away waste. This limitation is logical, since operators at waste disposal want to reduce walking to ensure safety and increase the flow of visitors through the station, as they believe too many walkers will reduce the flow. Also it is a fact that visitors cannot carry heavy loads for large distances. For simplicity reasons it is reasonable to set a fixed walking distance for each separate waste stream.

5. Travel times within substations are not taken into account. These depend heavily on the distance between the containers visitors visit subsequently and on their driving speed. This could be measured, but since that would take some time we have chosen to ignore it for now. 6. In this model we do not account for double visits (going back) in case a visitor forgets

something. No container or substation is visited two times. Since we have decided upon a fixed visiting order to simplify programming, allowing backtracking would take away any advantage gained by that simplification.

7. In the model overflow bins have not been taken into account. This will have an influence on the result as the parking places located in front of them are not used by the visitors. In real life visitors may park in front of these containers in order to visit waste streams located at nearby containers.

Assumptions made about the visitors:

1. We assume that all visitors in the system have a citizenship pass or some other identification for control. In reality some visitors do not have a pass and these can get a day pass if they have a sound explanation for wanting to bring waste without a pass. Since we do not know how often this occurs we may assume that all visitors in the model are allowed to visit.

2. We do not specifically account for the influence of the amount of waste visitors bring. I.e. there is no attribute that gives information on how much waste visitors bring. We assume that, by analysing server times at the containers and allotting a distribution to the different servers, this issue is covered. Other influences related to the amount of waste brought are unknown, since we were unable to clearly demonstrate these correlations, so we assume that the only effect is observed in the time it takes visitors to dump their waste.

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3. We assume that no contamination occurs. I.e., the visitors throw the waste they have in the correct containers. The effects of incorrect behaviour or the frequency of these occurrences is unknown and therefore we assume that the containers are visited and used correctly.

4. Waste brought by visitors is usually sorted; sometimes, however, it is not. We assume that the differences caused by lack of sorting beforehand, is reflected in the variance observed in the dumping times. Another consequence of badly sorted waste may be incorrect dumping at the waste disposal stations. Since we assumed this would not occur it is logical that the only effect is observed in the dumping time.

Simplifications made for the visitors:

1. Time spent by visitors looking for either a place to park or the location of the waste streams they brought with them are not taken into account. Since these depend heavily upon the visitors’ behaviour we have made no attempt to measure this.

2. The arrival process is estimated by utilising data from the system of the partnering waste disposal company.

After completing the conceptual model we programmed said model in a software tool in order to demonstrate its use. Chapter 5 describes the programmed simulation model briefly. It also

elaborates on the validation process and the experimental design used to demonstrate the model’s usefulness.

4.5 Summary

In this chapter we conclude the answering of the research questions. We started with the first steps towards creating a conceptual model in the first section. We translated our problem into a queueing problem and determined the variables and input parameters and output of our model. In Section 4.2 we described a real case of a waste disposal station. We have collected data at that waste disposal station and the collection process and the analysis of the collected data are described in Section 4.3. After determining the form of our model (in queueing terms) and the variables, the input parameters and the output all that remained in order to start programming the model is clearly

describing the content of our model. This means that we needed to describe the processes that are part of our model. We also had to provide details (values) for the input parameters. The final step of creating the conceptual model was to determine the assumptions and simplifications needed to make a translation to a programmed model. All of these steps were taken care of in Section 4.4. So, at the end of this chapter we have answered all the research questions needed to program a simulation model that can analyse a waste disposal station based on design decisions.

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Chapter 5 Simulation Model and Experimental Design