| Summary: | One of the most significant tasks for airports is snow removal. Airports must ensure the safety of the airplanes that go through the airport when there is extensive snowfall. With a highly competitive environment between airports, it requires snow removal to be efficient. Efficient snow removal can lead to reducing delays, costs, and the impact that the snowfall has. The aim of this thesis is to assist airports to do snow removal as efficiently as possible for apron areas. With clear mathematical techniques, it could lead to increased savings, improved mobility, and reduce environmental- and societal impacts. This project is done in cooperation with Isavia, a service company that manages the operation of all airports in Iceland. Isavia has not analyzed any snow removal methods, therefore does not know what methods are best to use at Keflavik airport apron. Snow removal at Keflavik airport apron is currently done differently by each operation team. The main goal is to analyze snow removal at Keflavik airport apron and present a mathematical model that will help to select methods used at the apron. This analysis should minimize the operation time of snow removal at aprons. The results show a clear difference between what method should be used on different shapes and sizes of the Keflavik apron. Eitt mikilvægasta verkefni fyrir flugvelli er snjómokstur. Flugvellir verða að tryggja öryggi flugvéla sem fara um flugvöllinn þegar það er mikil snjókoma. Í samkeppnishæfu umhverfi milli flugvalla þarf snjómokstur að vera skilvirkt. Skilvirkur snjómokstur getur leitt til þess að draga úr töfum, kostnaði og fleiri áhrifum sem snjókoman getur haft á flugvöllinn. Markmið þessarar ritgerðar er að aðstoða flugvelli við að greina snjómokstur á flughlaði eins skilvirkt og mögulegt er. Með því að greina snjómokstur gæti það valdið auknum sparnaði, bættum hreyfanleika á flughlaði og dregið úr umhverfis- og samfélagslegum áhrifum. Þetta verkefni er unnið í samvinnu við Isavia, þjónustufyrirtæki sem heldur utan um rekstur allra ...
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