Simulation Study

To perform a case study, eventually a model is required to integrate the complexities and to assemble all the different pieces. Company X has the wish to obtain an animated version of reality to come up with conclusions and trade-offs prior to the operationality phase of the new warehouse. Thereby they want to make use of a simulation model as a convincing tool for the future situation. The suitability of building a simulation model for comparing it with reality needs to be investigated, since it can visualize and realize the new situation for Company X. A definition of simulation is retrieved from literature followed by the purpose of simulation with accompanying advantages and disadvantages. Moreover, the concept of Discrete-Event Simulation is explained. Lastly, the principle of simulation optimization is described.

3.2.1. Definition

Computer based dynamic simulation can be defined as: “experimentation with a simplified imitation (on a computer) of an operations system as it progresses through time, for the purpose of better understanding and/or improving that system” (Robinson, 2014). Therefore, by means of simulation, multiple future projects can be simulated beforehand to check what the impacts are. Key Performance Indicators (KPIs) primarily determine the impact and consequences that future alternations to logistical networks might have. By means of the performance upon the established KPIs, decisions can be made beforehand via the experimentation by means of a simulation model.

3.2.2. Purpose of Simulation and (dis)advantages

Simulation modelling is a complex modelling technique. To emphasize its usefulness, this section deals with the purpose of simulation and the advantages that this technique can have on the one hand and the possible disadvantages that might occur during the process on the other hand. The need for simulation can be present within a large range of systems. Banks et al. (1996) proposed multiple systems that might have affinity towards simulation:

• Manufacturing systems

• Public systems: health care, military, natural resources • Transportation systems

• Construction systems

• Restaurant and entertainment systems • Business process reengineering/management • Computer system performance

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The eventual logistical purpose of simulation can be reduced to three major points: • Analyzing supply chains

• Designing and operating transportation systems • Designing and analyzing manufacturing systems

With regards to the impact upon the logistical network of Company X, simulation can ground the analysis of the entire network and address possible malfunctioning beforehand. Therefore, within this research the main purpose of simulation lies upon the alternation of the transportation system.

Advantages

The most important advantages of the usage of simulation techniques are explained based on literature. In total, six significant advantages in relation to other modelling approaches are provided. (Robinson, 2014).

• Transparency → a manager faced with a set of mathematical equations or a large spreadsheet may struggle to understand, or believe, the results from the model. Simulation is appealing because it is more intuitive, and an animated display of the system can be created, giving a non- expert greater understanding of, and confidence in, the model.

• Creating knowledge and understanding→ the development and use of a simulation model forces people to think through issues that otherwise may not have been considered. The modeler seeks information, asks for data and questions assumptions, all of which lead to an improved knowledge and understanding of the system that is being simulated.

• Fostering creativity → by means of a simulation, ideas can be tried in an environment that is free of risk. This can often help to encourage creativity in tackling problem situations.

• Modeling variability → simulations are able to model variability and its effects. If the systems being modelled are subject to significant levels of variability, then simulation is often the only means for accurately predicting performance.

• Consensus building → many simulation studies are performed in the light of differing opinions as to the way forward. A simulation model can be a powerful means for sharing concerns and testing ideas with a view to obtaining an accommodation of views.

• Visualization and communication → visual simulations prove a powerful tool for communication. It may be that an idea has already been proven, but it is deemed necessary to build a simulation model in order to convince senior managers and colleagues of its validity.

Disadvantages

Several disadvantages are worth mentioning, since these need to be taken account when the deployment of the simulation model is performed (Robinson, 2014):

• Time consuming → simulation is a very time-consuming approach. Due to the extensive amount of programming, data and often a long simulation horizon, the actual simulation requires a lot of time.

• Data hungry → most simulation models require a significant amount of data. Often, lots of analysis is required to put it in a suitable form for simulation.

• Requires expertise → simulation requires skills in conceptual modelling, validation and statistics, as well as skills in working with people and project management. This expertise is not always readily available.

• Expensive → the simulation software is usually rather expensive and the cost of model development and use may be considerable. Especially when consultants are involved.

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3.2.3. Discrete-Event Simulation (DES)

“Discrete-Event Simulation concerns the modeling of a system as it evolves over time by a representation in which the state variables change instantaneously at separate points in time” (Law, 2015). These points in time are the ones at which an event occurs, where an event is defined as an instantaneous occurrence that may change the state of the system. Therefore, discrete-event simulation focuses primarily on the change of states due to events that occur. With regards to the new warehouse of Company X, discrete- event simulation is a logical option. The state of the logistical network changes by means of different events occurring over time and interaction among agents (vehicles) is partly integrated as well.

3.2.4. Simulation Optimization

Simulation is a useful tool for evaluating the performance of systems. However, a simple evaluation of performance is often insufficient. Therefore, simulation optimization might come in handy. “Simulation optimization is the process of finding the best values of some decision variables for a system where the performance is evaluated based on the output of a simulation model of this system (Olafsson et al., 2002). Within a discrete environment optimization involves selecting the best alternative. When we have a set of alternatives, with m relatively small it may be possible to evaluate every solution and eventually compare performance. However, the simulation output is usually stochastic and comparing of alternatives becomes hard.

The two methods that can be used to statistically find (sub)-optimal solutions are (1) indifference zone ranking and selection (R&S) and (2) multiple comparisons procedures (MCP). The most common approach is to define an indifference zone for the performance and develop a procedure that selects a solution with performance that is within units of the optimal performance with a given probability, that is, if is the optimal solution and is the selection solution then:

Where 1 –α is the desired probability (Kelton et al., 2000).

Multiple comparisons procedures (MCPs) is another approach for selecting the best solution. MCPs calculate simultaneous confidence intervals for , i = 1, …, m, where is as before the optimal solution (Matejcik and Nelson, 1995).

When it is impossible to evaluate every solution by means of one of the two beforementioned statistical selection procedures, one might also think about a random search algorithm to iteratively find a good solution. The procedure for this random search is as follows (Olafsson et al., 2002):

0. Select and initial solution and simulate its performance . Set k = 0.

1. Select a candidate solution from the neighborhood of the current solution and simulate its performance .

2. If the candidate satisfied the acceptance criterion based on the simulated performance, then let ; otherwise let

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