This thesis deals with real logistic problem of the Heineken CZ Company. The company sets down an itinerary for each vehicle to distribute its goods to particular customers on daily basis. These itineraries are created manually, only with the skill of experienced driver. The goal of this thesis is to find a solution with an algorithm, which will be able to set optimal itineraries of all vehicles, so the total distance and therefore operating costs are minimized, with only the knowledge of distances between each two nodes.

The threaded fastener is by far the most common method for securing components together and plays a significant role in determining the quality of a product. Atlas Copco offers industrial tools for tightening these fasteners, which are today suffering from errors in the applied torque. These errors have been found to behave in periodic patterns which indicate that the errors can be predicted and therefore compensated for. However, this is only possible by knowing the rotational position of the tool. Atlas Copco is interested in the possibility of acquiring this rotational position without installing sensors inside the tools. To address this challenge, the thesis explores the feasibility of estimating the rotational position by analysing the behaviour of the errors and finding periodicities in the data. The objective is to determine whether these periodicities can be used to accurately estimate the rotation of the torque errors of unknown data relative to errors of data where the rotational position is known. The tool analysed in this thesis exhibits a periodic pattern in the torque error with a period of 11 revolutions.  Two methods for estimating the rotational position were evaluated: a simple nearest neighbour method that uses mean squared error (MSE) as distance measure, and a more complex circular fully convolutional network (CFCN). The project involved data collection from a custom-built setup. However, the setup was not fully completed, and the models were therefore evaluated on a limited dataset. The results showed that the CFCN method was not able to identify the rotational position of the signal. The insufficient size of the data is discussed to be the cause for this. The nearest neighbour method, however, was able to estimate the rotational position correctly with 100% accuracy across 1000 iterations, even when looking at a fragment of a signal as small as 40%. Unfortunately, this method is computationally demanding and exhibits slow performance when applied to large datasets. Consequently, adjustments are required to enhance its practical applicability. In summary, the findings suggest that the nearest neighbour method is a promising approach for estimating the rotational position and could potentially contribute to improving the accuracy of tools. / Skruvförband är den vanligaste typen av förband för att sammanfoga komponenter och är avgörande för en produkts kvalitet. Atlas Copco tillverkar industriverktyg avsedda för sådana skruvförband, som dessvärre lider av små avvikelser i åtdragningsmomentet. Avvikelserna uppvisar ett konsekvent periodiskt mönster, vilket indikerar att de är förutsägbara och därför möjliga att kompenseras för. Det är dock endast möjligt genom att veta verktygets vinkelposition. Atlas Copco vill veta om det är möjligt att erhålla vinkelpositionen utan att installera sensorer i verktygen. Denna uppsats undersöker möjligheten att uppskatta vinkelpositionen genom att analysera beteendet hos avvikelserna i åtdragningsmomentet och identifiera periodiciteter i datan, samt undersöka om dessa periodiciteter kan utnyttjas för att uppskatta rotationen hos avvikelserna hos okänd data i förhållande till tidigare data. Det verktyget som används i detta projekt uppvisar en tydlig periodicitet med en period på 11 varv. Två metoder för att uppskatta vinkelpositionen utvärderades: en simpel nearest neighbour-metod som använder mean squared error (MSE) som mått för avstånd, och ett mer komplext circular fully convolutional network (CFCN). Projektet innefattade datainsamling från en egendesignad testrigg som tyvärr aldrig blev färdigställd, vilket medförde att utvärderingen av modellerna utfördes på ett begränsat dataset.  Resultatet indikerade att CFCN-metoden kräver en större datamängd för att kunna uppskatta rotationen hos den okända datan. Nearest neighbour-metoden lyckades uppskatta rotationen med 100% noggrannhet över 1000 iterationer, även när endast ett segment så litet som 40% av signalen utvärderades. Tyvärr lider denna metod av hög beräkningsbelastning och kräver förbättringar för att vara praktiskt tillämpbar. Sammantaget visade resultaten att nearest neighbour-metoden har potential att vara ett lovande tillvägagångssätt för att uppskatta vinkelpositionen och kan på så sätt bidra till förbättring av verktygens noggrannhet.

This diploma thesis deals with static and dynamic capacitated messenger problem and its solving with heuristic algorithms. Different variations of the capacitated messenger problem were considered, with a single messenger or multiple messengers, with one depot or multiple depots in case of multiple messengers. Limited time for route realization was another modification that was considered. Modified nearest neighbour method, modified insertion method and modified exchange method were used to solve the problem. The main contribution of the thesis is deriving heuristics for described types of messenger problem and programming the algorithms in VBA (Visual Basic for Applications) in MS Excel. The results of computational experiments indicate that modified nearest neighbour method leads to better outcomes in static multiple messenger problems with a single depot, while modified insertion method is associated with lower values of objective function in static multiple messenger problem with multiple depots. Modified exchange method improves original solutions. Modified insertion method was approved for solving dynamic multiple messenger problems.