Furthermore, the process related issues of interest are the influence of building speed, building space volume, material price, machine purchase price and cool down time. Strategy related issues are multi-machine and multi-material production strategies in several setups.
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Also simulation investigation of different spare part stock properties are executed and analyzed by using different part size distributions. This dissertation establishes fundamental understanding of the characteristics of the additive manufacturing system for spare part supply strategies. This model could directly help the decision-making processes in whether to adopt additive manufacturing technology, and also helps the evaluation of different additive manufacturing strategies when the technology is adopted.
Both decisions adoption and strategies are made based on cost analysis for spare parts in a broader supply chain.
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Jedeck, Stefan, "Spare parts on demand using additive manufacturing : a simulation model for cost evaluation. Electronic Theses and Dissertations.
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Model Verification Network Simulation Dissertation
Title Spare parts on demand using additive manufacturing : a simulation model for cost evaluation. The objective of model verification is to ensure that the implementation of the model is correct.
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There are many techniques that can be utilized to verify a model. These include, but are not limited to, having the model checked by an expert, making logic flow diagrams that include each logically possible action, examining the model output for reasonableness under a variety of settings of the input parameters, and using an interactive debugger. Validation checks the accuracy of the model's representation of the real system.
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Model validation is defined to mean "substantiation that a computerized model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model". There are many approaches that can be used to validate a computer model. The approaches range from subjective reviews to objective statistical tests. One approach that is commonly used is to have the model builders determine validity of the model through a series of tests.
Naylor and Finger  formulated a three-step approach to model validation that has been widely followed: . Step 3. Compare the model input-output transformations to corresponding input-output transformations for the real system. A model that has face validity appears to be a reasonable imitation of a real-world system to people who are knowledgeable of the real world system.
Assumptions made about a model generally fall into two categories: structural assumptions about how system works and data assumptions.
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Assumptions made about how the system operates and how it is physically arranged are structural assumptions. For example, the number of servers in a fast food drive through lane and if there is more than one how are they utilized? Do the servers work in parallel where a customer completes a transaction by visiting a single server or does one server take orders and handle payment while the other prepares and serves the order. Many structural problems in the model come from poor or incorrect assumptions.
There must be a sufficient amount of appropriate data available to build a conceptual model and validate a model. Lack of appropriate data is often the reason attempts to validate a model fail. A typical error is assuming an inappropriate statistical distribution for the data. Any outliers in the data should be checked.
The model is viewed as an input-output transformation for these tests. The validation test consists of comparing outputs from the system under consideration to model outputs for the same set of input conditions.http://gelatocottage.sg/includes/2020-11-04/2649.php
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Data recorded while observing the system must be available in order to perform this test. The model would be run with the actual arrival times and the model average time in line would be compared with the actual average time spent in line using one or more tests.
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Statistical hypothesis testing using the t-test can be used as a basis to accept the model as valid or reject it as invalid. To perform the test a number n statistically independent runs of the model are conducted and an average or expected value, E Y , for the variable of interest is produced.