Title: Inventory management as prerequisite for an efficient warehousing managementstrategy during expansive growth Authors: Oskar Siggesson, Mattias Hedendahl, Anton Olsson & Max Sandstedt Examinator: Hana HulthénTutor: Petra Andersson Background: The company is in a period of strong growth and is suffering from growingpains. The company is moving to a new location with a larger storage space in 2024 butsomething has to be done before the move. Because of high storage levels there is a lack offree storage space and the warehouse is soon reaching its breaking point. Objective: The report investigates how a change in warehousing strategies can helpMicropower cope with the growth in production until 2024. A forecast is created for thefuture need for storage space on a selection of products in a portion of the warehouse. Methods: This study is a case study of Micropower Group. The study uses secondary data inthe form of quantitative data from the company's business system, but to some extent also theauthor’s own observations. The data is used to perform an ABC/XYZ-analysis on thecompany's incoming inventory that forms the basis for segmentation of the articles. The studyalso conducts a sensitivity analysis of the company's storage space needs. Conclusions: By segmenting articles based on the ABC/XYZ-analysis, appropriateinstruments can be presented for the company's articles. The analysis forms a framework forMicropower's inventory management strategy but also for companies in similar situations.Based on the segmentation, information about the article's characteristics and appropriateinstruments. The study's sensitivity analysis of the forecast for storage space requirementsgives the company an indication of what increase in inventory turnover rate is required tomanage production growth. The study finds that in order to manage production growth therewill need to be an equally large increase in inventory turnover rate. Key words: ABC/XYZ-analysis, item management, inventory management strategy,differentiation, classification, forecast, sensitivity analysis, storage space

The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging sensitive battery control is necessary. This refers to control that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application.  In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery, since it reflects the changing properties of an aging battery. This thesis investigates identification of physics-based and empirical battery models from operational EV data. The work is divided into three main studies. 1) A global sensitivity analysis was performed on the parameters of a high-order physics-based model. Measured current profiles from real EV:s were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the data set from an electric truck with few battery packs. Data sets from vehicles with more packs (electric bus) and limited SOC operating window (plug-in hybrid truck) excited fewer model parameters. 2) Empirical linear-parameter-varying (LPV) dynamic models were identified on driving data. Model parameters were formulated as functions of the measured temperature, current magnitude and estimated open circuit voltage (OCV). To handle the time-scale differences in battery voltage response, continuous-time system identification was employed. We concluded that the proposed models had superior predictive abilities compared to discrete and time-invariant counterparts.  3) Instead of using driving data to parametrize models, we also investigated the possibility to design the charging current in order to increase its information content about model parameters. This was formulated as an optimal control problem with charging speed and information content as objectives. To also take battery degradation into account, constraints on polarization was included. The results showed that parameter information can be increased without significant increase in charge time nor aging related stress. / Elekriska fordon utgör en allt större andel av världens fordonsflotta. Batteriet är en komponent med betydande påverkan både på fordonets kostnadoch dess miljö- och klimatpåverkan. Det är därför viktigt att försöka maximera batteriets utnytjandegrad. Användargränser upprätthålls av batterietsstyrsystem, såkallad BMS, för att garantera säker drift samt för att begränsabatteriets åldrande. Användargränserna tenderar att vara konservativa för attta höjd för osäkerhet i tillståndsestimeringen samt batteriets förändrade egenskaper under dess livstid. För att utöka utnyttjandegraden är ålderskänsligstyrning nödvändig. Med detta avses styrning som a) justeras under batterietslivstid och b) balancerar utnyttjande och prestanda på ett sätt som passar enspecifik applikation. Ombord på fordon mäts typiskt tre signaler; ström, spänning och temperatur. Batteriets beteende bestäms dock av andra tillstånd som måste estimeras, såsom dess laddnivåeller lokala koncentrationer och potentialer. BMS:enförlitar sig därför på modeller för att estimera interna tillstånd och utföra styrning. För att uppfylla punkterna a) och b) måste modellerna som användsuppdateras ombord i takt med att batteriet åldras. En uppdaterad modellkan också fungera som ett diagnostiskt verktyg eftersom det speglar batteriets förändrade egenskaper. Den här avhandlingen undersöker identifieringav fysikbaserade och empiriska modeller från kördata. Arbetet delas in i treseparata studier. 1) En global känslighetsanalys utfördes på parametrarna i en fysikbaseradmodell av hög ordning. Som inputsignal användes uppmätt ström från riktigaelfordon i drift. Parametrarnas effekt på både cellspänning och interna batteritillstånd analyserades. Studien visade att alla modellparametrar exciteradesav strömmen från ett helelektriskt fordon. Anledningen var att batteriernaanvändes inom ett brett SOC spann samt att den dragna strömmen var stor.I fordon med snävare SOC span och lägre strömmar var inte alla parametrarkänsliga. 2) Dynamiska parametervarierande modeller formulerades och identifierades från kördata. Den uppmätta temperaturen, samt strömmens storlekoch den estimerade tomgångsspänningen (OCV) användes till parameterberoenden. För att hantera skillnader i tidsskala mellan spänningssvarets olikakomponenter användes systemidentifiering i kontinuerlig tid. Vi kunde draslutsatsen att de föreslagna modellerna var överlägsna motsvarande diskretaoch konstanta modeller. 3) Istället för att använda kördata för att parametrisera modeller undersökte vi också möjligheten att designa laddförloppet för att öka dess informationsinnheåll. Detta formulerades som ett optimeringsproblem med laddtidoch informationsinnehåll i kostnadsfunktionen. För att även ta batteriets åldrande i beaktning, ansattses bivillkor på polariseringsspänningen. / <p>QC 20220516</p>

The Macroscopic Fundamental Diagram (MFD) describes the relation of average network flow, density and speed in urban networks. It can be estimated based on empirical or simulation data, or approximated analytically. Two main analytical approximation methods to derive the MFD for arterial roads and urban networks exist at the moment. These are the method of cuts (MoC) and related approaches, as well as the stochastic approximation (SA). This paper systematically evaluates these methods including their most recent advancements for the case of an urban arterial MFD. Both approaches are evaluated based on a traffic data set for a segment of an arterial in the city of Munich, Germany. This data set includes loop detector and signal data for a typical working day. It is found that the deterministic MoC finds a more accurate upper bound for the MFD for the studied case. The estimation error of the stochastic method is about three times higher than the one of the deterministic method. However, the SA outperforms the MoC in approximating the free-flow branch of the MFD. The analysis of the discrepancies between the empirical and the analytical MFDs includes an investigation of the measurement bias and an in-depth sensitivity study of signal control and public transport operation related input parameters. This study is conducted as a Monte-Carlo-Simulation based on a Latin Hypercube sampling. Interestingly, it is found that applying the MoC for a high number of feasible green-to-cycle ratios predicts the empirical MFD well. Overall, it is concluded that the availability of signal data can improve the analytical approximation of the MFD even for a highly inhomogeneous arterial.

Designing object-oriented software is hard, and designing reusable object-oriented software is even harder. This task is even more daunting for a developer of computational intelligence applications, as optimising one design objective tends to make others inefficient or even impossible. Classic examples in computer science include ‘storage vs. time’ and ‘simplicity vs. flexibility.’ Neural network requirements are by their very nature very tightly coupled – a required design change in one area of an existing application tends to have severe effects in other areas, making the change impossible or inefficient. Often this situation leads to a major redesign of the system and in many cases a completely rewritten application. Many commercial and open-source packages do exist, but these cannot always be extended to support input from other fields of computational intelligence due to proprietary reasons or failing to fully take all design requirements into consideration. Design patterns make a science out of writing software that is modular, extensible and efficient as well as easy to read and understand. The essence of a design pattern is to avoid repeatedly solving the same design problem from scratch by reusing a solution that solves the core problem. This pattern or template for the solution has well understood prerequisites, structure, properties, behaviour and consequences. CILib is a framework that allows developers to develop new computational intelligence applications quickly and efficiently. Flexibility, reusability and clear separation between components are maximised through the use of design patterns. Reliability is also ensured as the framework is open source and thus has many people that collaborate to ensure that the framework is well designed and error free. This dissertation discusses the design and implementation of a generic neural network framework that allows users to design, implement and use any possible neural network models and algorithms in such a way that they can reuse and be reused by any other computational intelligence algorithm in the rest of the framework, or any external applications. This is achieved by using object-oriented design patterns in the design of the framework. / Dissertation (MSc)--University of Pretoria, 2007. / Computer Science / unrestricted

Alla företag har – från tid till annan – någon form av investeringar i sin verksamhet. För att utvärdera olika investeringsalternativ används investeringskalkyleringar, men de bakomlig-gande antaganden som görs vid dessa kalkyler är präglade av osäkerhet då ingen kan sia om framtidens utfall. Ett verktyg som kan användas för att hantera denna osäkerhet är känslighets-analys. Det finns många studier gjorda om känslighetsanalyser, men nästan alla följer en rationell an-sats och tar sällan hänsyn till den begränsade rationella människan som står bakom analyserna. Syftet med denna studie är att analysera hur företag använder sig av känslighetsanalys i sam-band med investeringsbeslut, oavsett om de förhåller sig till den rationella ansatsen eller inte. Studien har därför genomförts med intervjuer för att kunna uppnå detta syfte. Studien visar på att det finns viktiga faktorer som gör att känslighetsanalysens roll och använ-dande inför investeringsbeslut skiljer sig åt i de intervjuer vi gjort. Faktorerna som denna studie har tagit hänsyn till är hur ägarstrukturen ser ut, vilka typer av investeringar som görs och organisationens tolkningsprocess av omgivningen. Genom vår analysmodell hoppas vi kunna visa på varför behovet och utförandet av känslighetsanalysen ser ut som det gör, men även ge en förståelse för varför den skiljer sig åt mellan olika företag. / All companies have some form of investment in their business. To evaluate different invest-ment options, they will use some form of capital budgeting techniques. However, the underly-ing assumptions made in these calculations are characterized by uncertainty, as no one can predict the outcome of the future. One tool that can be used to deal with this uncertainty is sensitivity analysis. There have been many studies done on the subject of sensitivity analysis. But almost all of them follow a rational approach and rarely consider the limited rational person behind these analyses. The purpose of this study is to analyse how companies use sensitivity analysis for investment decisions, regardless of whether they relate to the rational approach or not. This study was therefore conducted with interviews to be able to achieve this purpose. This study shows that there are important factors that change the sensitivity analysis purpose and how it is used for investments decisions in the interviews that we have done. The factors this study will take into account are company structures, types of investments and how organ-izations manage their interpretation process of their environment. Through our analysis model we hope to show why the need and implementation of sensitivity analysis looks the way it does and provide an understanding of why it differs between companies.

Les polluants organiques persistants (POPs) sont des substances toxiques ayant la capacité de se bioaccumuler le long de la chaîne alimentaire. Une fois émis dans l'atmosphère, ils sont dispersés par le vent puis se déposent au sol. Du fait de leur persistance, ils peuvent être réémis depuis le sol vers l'atmosphère et parcourir ainsi de longues distances. Ce processus est couramment appelé « effet saut de sauterelle ». On peut donc retrouver les POPs très loin de leurs sources d'émissions. Pour pouvoir prendre des décisions visant à réduire leur impact environnemental, il est nécessaire de comprendre leur comportement dans l'atmosphère mais également dans les autres milieux, tels que le sol, la végétation ou l'eau. De nombreux modèles numériques de complexité variable ont été développés dans le but de prédire le devenir des POPs dans l'environnement. La plupart d'entre eux considèrent le sol comme un compartiment homogène, pouvant ainsi mener à une sous-estimation des réémissions du sol vers l'atmosphère. Or, du fait de la mise en place de réglementations visant à réduire les émissions anthropiques des POPs, la concentration dans l'atmosphère tend à diminuer et le sol, qui semblait jusqu'alors être seulement un réservoir, devient une source potentielle de POPs pour l'atmosphère. Il apparaît donc nécessaire de coupler les modèles de dispersion atmosphérique à un modèle de sol réaliste. Mes recherches ont permis d'étudier l'impact des interactions entre le sol et l'atmosphère sur la concentration dans les différents milieux. Pour cela, nous avons développé un modèle de sol multi-couches permettant de mieux estimer le profil de concentration dans le sol et les échanges entre ces deux milieux. Une analyse de sensibilité a été effectuée afin d'identifier les paramètres clés dans la détermination des réémissions. Puis ce modèle a été couplé à un modèle 3D de chimie-transport atmosphérique. Une étude de cas à l'échelle européenne a alors été réalisée afin d'évaluer ce modèle et d'estimer l'impact des réémissions sur les concentrations de POPs dans l'environnement / Persitent Organic Pollutants (POPs) are toxic substances that bioaccumulate in the food chain. Once emitted in the atmosphere, they are transported by the wind and deposited on soil. Since they are persistent, they can be reemited from soil to atmosphere by volatilization and travel over very long distances. This process is called grasshopper effect. Thus, POPs may be found at significant levels far from their emission source. It is necessary to understand the transport and fate of these pollutants in order to support the decision making process and reduce human exposure to POPs. Regulations over the last decades lead to a decrease of anthropogenic emissions and subsequent decrease of atmospheric concentration. In this context, the soil is no longer a sink of POPs but can be a source to the atmosphere. Many numeric models aim to study the behavior of POPs in the environment. Most of them consider soil compartment as a homogeneous box, leading to an underestimation of reemissions. Then, it appears of great importance to develop more realistic soil models. The objective of my thesis was to develop such a model, with vertical transport within the soil. This model was evaluated against measured concentration soil profile. We also conducted a sensitivity analysis to identify the key parameters involved in the process of reemissions. Then, the soil model was coupled with an atmospheric transport model. A case study was finally undertaken to estimate the impacts of reemissions on global-mass balance of POPs at European scale

The aim of the thesis is to devise a methodology which is capable of reliable evaluation of road vehicle’s crosswind sensitivity. The work consists of writing the mathematical model of a vehicle which includes aerodynamic loads and conducting a validation by means of technical experiment as defined by ISO 12021. A sensitivity analysis describing the influence of individual inputs on evaluation criteria of vehicle’s crosswind sensitivity is performed using the model, thereby establishing the overall model error caused by possible inaccuracies of input parameters. At the end, the relationship between discrete pressures on the surface of the vehicle in several aerodynamic configurations and the properties representing the vehicle’s motion is presented.

Doctoral thesis deals with reliability analysis of flood protection dikes by estimating the probability of dike failure. This study based on theoretical knowledge, experimental and statistical researches, mathematical models and field survey extends present knowledge concerning with reliability analysis of dikes vulnerable to the problem of breaching due to overtopping. This study contains the results of probabilistic solution of breaching of a left bank dike of the River Dyje at a location adjacent to the village of Ladná near the town of Břeclav in the Czech Republic. Within thin work, a mathematical model describing the overtopping and erosion processes was proposed. The dike overtopping is simulated using simple surface hydraulics equations. For modelling the dike erosion which commences with the exceedance of erosion resistance of the dike surface, simple transport equations were used with erosion parameters calibrated depending on data from past real embankment failures. In the context of analysis of the model, uncertainty in input parameters was determined and subsequently the sensitivity analysis was carried out using the screening method. In order to achieve the probabilistic solution, selected input parameters were considered random variables with different probability distributions. For generating the sets of random values for the selected input variables, the Latin Hypercube Sampling (LHS) method was used. Concerning with the process of dike breaching due to overtopping, four typical phases were distinguished. The final results of this study take the form of probabilities for those typical dike breach phases.

The diploma thesis is focused on evaluation of the economic efficiency of investment projects of transport infrastructure in the Czech Republic. These projekcts are evaluated according to the methodological documents of the Ministry of Transport of the Czech Republic, the State Fund for Transport Infrastructure and thr Directorate of Roads and Motorways of the Czech Republic. The diploma thesis also deals with the comparison od individual methodological procedures and their influence on the resulting indicators of economic efficiency of transport infrastructure condtructions.

Teil 1 Karsten Berndt Die Festlegung von Toleranzen ist eine alltägliche Aufgabenstellung des Konstrukteurs. Dabei bedingt die Berechnung nichtlinearer Toleranzketten einen erheblichen Zeitaufwand, wodurch meist auf deren genaue Berechnung vezichtet wird und Toleranzen stattdessen auf Basis von Erfahrungswerten festgelegt werden. Die vorgestellte Methodik zeigt Wege, wie schnell und frühzeitig im Konstruktionsprozess belastbare Aussagen zu Toleranzen komplexer Mechanismen getroffen werden können. Dazu werden sogenannte Sensitivitätsanalysen in der CAD-Software "Creo Elements" durchgeführt und ausgewertet. Das Ergebnis sind erste konkrete Toleranzfelder für alle den Mechanismus beschreibenden, geometrischen Abmessungen, welche sich als Startwerte für den anschließenden Toleranzsynthese/-analyseprozess eignen. Teil 2 Marko Ebermann Dieser zweite Vortragsteil behandelt eine mögliche Vorgehensweise zur Tolerierung von Geometrieabweichungen in der frühen Entwurfsphase am Beispiel des Koppelgliedes einer Verpackungsmaschine. Ausgangspunkt für die frühe Tolerierung bildet die im ersten Vortragsteil behandelte Sensitivitätsanalyse des Koppelgetriebes, welche Informationen zur Empfindlichkeit der Funktionsmaße bezüglich der Einhaltung der Schließmaßtoleranz lieferte. Die daraus abgeleitete Form- und Lagetolerierung des Koppelgliedes soll durch anschließende Toleranzamalysen die Tolerierung im Baugruppenkontext bestätigen und auf möglich Fertigungsverfahren abzielen, ohne die genaue Gestalt der Komponenten zu kennen. So können teure und zeitintensive Iterationsschleifen im Konstruktionsprozess minimiert und die Funktionalität frühzeitig gesichert werden.