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Unmanned cargo aircraft : the structured development of a deployment area assessment instrument

Wolters, Jeffrey (2019) Unmanned cargo aircraft : the structured development of a deployment area assessment instrument.

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Abstract:In this research a method is developed that helps its users assessing the attractiveness of an area regarding the deployment of Unmanned Cargo Aircraft (UCA). International market selection strategies used by companies as well as reports published by the Platform Unmanned Cargo Aircraft (PUCA) have been conducted. Research about UCA is still scarce. For this reason, existing knowledge has been supplemented with the knowledge from two experts within the field of UCA deployment. Literature combined with expert knowledge resulted in a list of factors that influence the attractiveness of an area. To keep the overview, a causal model has been developed with those factors that have a causal relationship with the main variable area attractiveness. The requirements for the existence of a causal relationship were provided by the book Geen Probleem (2012). One of the requirements for the instrument set by the Platform Unmanned Cargo Aircraft was that the method should be generally applicable. To do so, the analytical hierarchy process (AHP) has been used to reflect one’s indicator preference. Users must compare sets of two indicators after which the relative importance per factor is calculated according to the AHP approach. This relative importance per factor is called the eigenvector. In order to be able to assign a score for each area per factor, an adapted version of the GIS-based Landscape Appreciation Model (GLAM) has been used. This model uses positive and negative indicators that are assigned a score at the interval between 0 and 4. Scores can only be natural numbers. To ensure that the model is also applicable in this study, the model will be expanded with selection indicators that either can be assigned a score 0 or 1. The final score per area is calculated by multiplying the relative importance per factor with the area score for that factor. Positive scores are added up after which the scores for negative indicators will be subtracted from the this. The remaining score will be multiplied by the score for each selection indicator. This means that if an area does not meet a selection indicator and thus receives a score zero for this indicator, that the total score for that area also equals zero. For a given moment in time, the score of an area per factor is fixed. However, it is not likely to assume that every area has the same score on each criterion at a later point in time. For this reason, the source (often a database) from which the scores were derived, are described. The factors, the AHP and the adapted version of the GLAM model have been implemented in Microsoft Excel. Microsoft Excel is ideally suited because a user can perform the pair wise comparisons relatively easy, after which they are automatically calculated to a relative importance per factor. This relative importance together with the score of the areas on the factors are used to calculate the final score per area.
Item Type:Essay (Bachelor)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:02 science and culture in general, 55 traffic technology, transport technology
Programme:Industrial Engineering and Management BSc (56994)
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