Cette thèse est consacrée à l'amélioration de la cohérence dans le régime d'effet Hall quantique entier (EHQE) à facteur de remplissage ν=2, obtenu en appliquant un fort champ magnétique perpendiculairement au plan d'un gaz bidimensionnel d'électrons formé à l'interface d'une hétérostructure semiconductrice d'AlGaAs/GaAs. On obtient alors des conducteurs unidimensionnels chiraux (états de bord) permettant de réaliser l'équivalent électronique de l'interféromètre de Mach-Zehnder (IMZ), pour étudier la cohérence dans ce régime. L'observation inattendue d'une structure périodique en forme de lobes dans la visibilité des interférences en fonction de la tension appliquée en entrée suggère un rôle non négligeable des interactions.Dans un première partie nous expliquons l'émergence des états de bord dans le régime d'EHQE. Nous faisons ensuite l'état de l'art des connaissances concernant leur cohérence, puis nous présentons l'IMZ électronique du point de vue expérimental.Ensuite, nous détaillons les résultats expérimentaux, d'abord concernant la visibilité à tension finie: nos mesures confirment une prédiction théorique concernant un transition de phase quantique en fonction de la dilution de l'état de bord qui interfère ; nous ne voyons pas d'effet flagrant de la relaxation en énergie. Enfin, de précédents travaux(1) ayant identifié clairement l'état de bord voisin de celui qui interfère comme l'environnement limitant la cohérence du système, nous avons réalisé un nouveau type d'échantillon afin de diminuer le couplage à cet environnement de manière contrôlée. Nous avons ainsi augmenté la cohérence de moitié en accord quantitatif avec la théorie issue de précédents travaux(1).(1)P. Roulleau, F. Portier, P. Roche, A. Cavanna, G. Faini, U. Gennser, and D. Mailly. Noise Dephasing in Edge States of the Integer Quantum Hall Regime. Physical Review Letters, 101(18):186803–4, October 2008 / This PhD thesis is devoted to the engineering of quantum coherence in the integer quantum Hall effect regime (IQHE) at filling factor ν=2, obtained by applying a strong perpendicular magnetic field to a bidimensional electron gas formed at the interface of a GaAlAs/GaAs semiconducting heterostructure. Then unidimensional chiral conductors called edge states appear which can be used as electron beams to build the equivalent in condensed matter of a Mach-Zehnder interferometer (MZI) so as to study coherence in this regime. The unexpected periodic lobe structure of the visibility as function of the bias voltage suggests that interactions play an important role.In the first part, we explain how edge states emerge in the IQHE regime. We picture the state of the art on the edge states coherence. Then we present the MZI from the experimental point of view.Next we show our results, first concerning the visibility at finite bias: our measurements confirm a prediction about a quantum phase transition as function of the interfering edge state dilution. We don't see any significant manifestation of energy relaxation in the visibility. Finally, having identified the adjacent edge state as the noisy environment limitating coherence thanks to previous works, we have designed a new kind of sample to decrease the coupling of the system to this environment in a controlled manner. We thus decreased dephasing by half, in quantitative agreement with the theory developped previously in our group.

This thesis examines the problem of identifying patterns in process event logs that are correlated with binary events that are undetected until the end of the process. Specifically, we consider the task of identifying patterns in a machine shop manufacturing process that are correlated with product defect. We introduce a pattern mining algorithm based on Apriori to identify frequent patterns, and use binary correlation measures to identify patterns associated with elevated defect rate. We design a simulation model to generate synthetic datasets to test our algorithm. We compare the effectiveness of different correlation measures, target pattern complexities, and sample sizes with and without knowledge of the underlying process. We show that knowledge of the underlying process helps in identifying the pattern that is associated with defects. We also develop a decision support tool based on p-value simulation to help managers identify sources of error in real-life settings. Finally, we apply our method to real world data and extract useful information from the data to help plant managers make decisions related to investments and workforce planning. This thesis also explores the problem of predicting the defect probability given an ordered list of events and its defect status. We develop a supervised learning model using the frequency of patterns deduced from the event log as the feature set. We discuss the challenges faced in this approach and conclude that random forest algorithm performs better than other methods. We apply this approach to a real world case study and discuss the applications in the machine shop. Finally, the thesis explores the order-bidding process in the machine shop industry, and proposes an optimization-based model to maximize the profit of the machine shop. Through a case study example, we show the advantages of using the defect probability in the proposed optimization model to determine the machine-worker schedule to execute job orders in a machine shop.

<p>Energy management has become a major concern in the past two decades with the increasing energy prices, overutilization of natural resources and increased carbon emissions. According to the department of Energy the industrial sector solely consumes 22.4% of the energy produced in the country [1]. This calls for an urgent need for the industries to design and implement energy efficient practices by analyzing the energy consumption, electricity data and making use of energy efficient equipment. Although, utility companies are providing incentives to consumer participating in Demand Response programs, there isn’t an active implementation of energy management principles from the consumer’s side. Technological advancements in controls, automation, optimization and big data can be harnessed to achieve this which in other words is referred to as “Smart Manufacturing”. In this research energy management techniques have been designed for two SEU (Significant Energy Use) equipment HVAC systems, Compressors and load shifting in manufacturing environments using control and optimization.</p> <p>The addressed energy management techniques associated with each of the SEUs are very generic in nature which make them applicable for most of the industries. Firstly, the loads or the energy consuming equipment has been categorized into flexible and non-flexible loads based on their priority level and flexibility in running schedule. For the flexible loads, an optimal load scheduler has been modelled using Mixed Integer Linear Programming (MILP) method that find carries out load shifting by using the predicted demand of the rest of the plant and scheduling the loads during the low demand periods. The cases of interruptible loads and non-interruptible have been solved to demonstrate load shifting. This essentially resulted in lowering the peak demand and hence cost savings for both “Time-of-Use” and Demand based price schemes. </p> <p>The compressor load sharing problem was next considered for optimal distribution of loads among VFD equipped compressors running in parallel to meet the demand. The model is based on MILP problem and case studies was carried out for heavy duty (>10HP) and light duty compressors (<=10HP). Using the compressor scheduler, there was about 16% energy and cost saving for the light duty compressors and 14.6% for the heavy duty compressors</p> <p>HVAC systems being one of the major energy consumer in manufacturing industries was modelled using the generic lumped parameter method. An Electroplating facility named Electro-Spec was modelled in Simulink and was validated using the real data that was collected from the facility. The Mean Absolute Error (MAE) was about 0.39 for the model which is suitable for implementing controllers for the purpose of energy management. MATLAB and Simulink were used to design and implement the state-of-the-art Model Predictive Control for the purpose of energy efficient control. The MPC was chosen due to its ability to easily handle Multi Input Multi Output Systems, system constraints and its optimal nature. The MPC resulted in a temperature response with a rise time of 10 minutes and a steady state error of less than 0.001. Also from the input response, it was observed that the MPC provided just enough input for the temperature to stay at the set point and as a result led to about 27.6% energy and cost savings. Thus this research has a potential of energy and cost savings and can be readily applied to most of the manufacturing industries that use HVAC, Compressors and machines as their primary energy consumer.</p><br>

Less-than-truckload industry has a valuable potential for applications of operations research in two areas, network design and efficiency improvement within existing networks. This thesis focuses on the latter, specifically the less-than-truckload terminals where cross docking operations occur. The assignment of incoming trailers to inbound docks is one of the critical decisions that affect the performance of less-than-truckload terminals. This research reviews existing models in literature and introduces an optimal mixed integer quadratic model with the objective of generating assignments that are robust against variability in system parameters such as truck arrival and service times, terminal characteristics and trailer load content. The computational limitations of the optimal model are discussed. A dock assignment heuristic is developed to overcome the computational difficulties reported with the optimal model to solve realistic size problems. It is concluded that the heuristic is generally applicable and is robust against system variably. A dynamic dock assignment heuristic is later introduced to implement the decision process at real time. It is concluded that the dynamic dock assignment heuristic is also robust against system variability. The last part presents a case study that benchmarks the dynamic dock assignment heuristic and existing static assignments at a real terminal. The results show that the dynamic dock assignment heuristic outperforms the static assignment under system variability. Conclusions and future research areas are finally addressed in the last chapter.

This thesis aims to implement a combination of Linear Programming Column Generation and a Large Neighbourhood Search heuristic to solve scheduling problems. The resulting method is named Integer Programming Column Search (IPCS). For computational evaluation, the IPCS method is applied to the problem Prize-Collecting Job Sequencing with One Common and Multiple Secondary Resources generalised to parallel identical machines. The interest of combining exact procedures with heuristic approaches is quickly growing since scheduling problems have many and complex real-world applications. Most of these problems are NP-hard and therefore very challenging to solve. By using a combination of heuristic strategies and exact procedures, it can be possible to find high-quality solutions to such problems within an acceptable time horizon. The IPCS method uses a greedy integer programming column generating problem introduced in a previous work. This problem is designed to generate columns that are useful in near-optimal integer solutions. A difference to previously introduced method is that we here build a master problem, an Integer Programming Column Search Master (IPCS-Master). This is used to update the dual solution that is provided to the greedy integer programming column generating problem. The computational performance of the IPCS method is evaluated on instances with 60, 70, 80, 90 and 100 jobs. The result shows that the combined design encourage the generation of columns that benefit the search of near-optimal integer solutions. The introduction of an IPCS-Master, which is used to update the dual variable values, generally leads to fewer pricing problem iterations than when no master problem is used.

<p>In this thesis we address the problem of integrated software pipelining for clustered VLIW architectures. The phases that are integrated and solved as one combined problem are: cluster assignment, instruction selection, scheduling, register allocation and spilling.</p><p>As a first step we describe two methods for integrated code generation of basic blocks. The first method is optimal and based on integer linear programming. The second method is a heuristic based on genetic algorithms.</p><p>We then extend the integer linear programming model to modulo scheduling. To the best of our knowledge this is the first time anybody has optimally solved the modulo scheduling problem for clustered architectures with instruction selection and cluster assignment integrated.</p><p>We also show that optimal spilling is closely related to optimal register allocation when the register files are clustered. In fact, optimal spilling is as simple as adding an additional virtual register file representing the memory and have transfer instructions to and from this register file corresponding to stores and loads.</p><p>Our algorithm for modulo scheduling iteratively considers schedules with increasing number of schedule slots. A problem with such an iterative method is that if the initiation interval is not equal to the lower bound there is no way to determine whether the found solution is optimal or not. We have proven that for a class of architectures that we call transfer free, we can set an upper bound on the schedule length. I.e., we can prove when a found modulo schedule with initiation interval larger than the lower bound is optimal.</p><p>Experiments have been conducted to show the usefulness and limitations of our optimal methods. For the basic block case we compare the optimal method to the heuristic based on genetic algorithms.<em></em></p><p><em>This work has been supported by The Swedish national graduate school in computer science (CUGS) and Vetenskapsrådet (VR).</em></p>

We study bounds on the distances of roots of integer polynomials and applications of such results. The separation of complex roots for reducible monic integer polynomials of fourth degree is thoroughly explained. Lemmas on roots of polynomials in the p-adic setting are proved. Explicit families of polynomials of general degree as well as families in some classes of quadratic and cubic polynomials with very good separation of roots in the same setting are exhibited. The second part of the thesis is concerned with results on p-adic versions of Mahler's and Koksma's functions wn and w*n and the related classifications of transcendental numbers in Cp. The main result is a construction of numbers such that the two functions wn and w*n differ on them for every n and later on expanding the interval of possible values for wn-w*n. The inequalities linking values of Koksma's functions for algebraically dependent numbers are proved.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 250-274).

Road traffic dominates in domestic Swedish transportation and is highly dependent on fossil fuels, petrol and diesel. Currently, the use of renewable fuels in transportation accounts for less than 6% of the total energy use in transport. The demand for bioethanol to fuel transportation is growing and cannot be met through current domestic production alone. Lignocellulosic ethanol derived from agricultural crop residues may be a feasible alternative source of ethanol for securing a consistent regional fuel supply in Swedish climatic conditions.  This licentiate thesis focuses on regional transport fuel supply by considering local small-scale ethanol production from straw. It presents the results of investigations of regional transport fuel supply with respect to minimising regional CO2 emissions, cost estimates for transport fuel supply, and the availability of lignocellulosic resources for small-scale ethanol production. Regional transport fuel demand between the present and 2020 is also estimated. The results presented here show that significant bioethanol can be produced from the straw and Salix available in the studied regions and that this is sufficient to meet the regions’ current ethanol fuel demand.  A cost optimisation model for regional transport fuel supply is developed and applied for two cases in one study region, one when the ethanol production plant is integrated with an existing CHP plant (polygeneration), and one with a standalone ethanol production plant. The results of the optimisation model show that in both cases the changes in ethanol production costs have the biggest influence on the cost of supplying the regional passenger car fleet with transport fuel, followed by the petrol price and straw production costs.  By integrating the ethanol production process with a CHP plant, the costs of supplying regional passenger car fleet with transport fuel can be reduced by up to a third. Moreover, replacing petrol fuel with ethanol can cut regional CO2 emissions from transportation by half.

This thesis consists of an introduction and seven appended papers. The purpose of the introduction is to give an overview of the field of topology optimization of discretized load carrying continuum structures. It is assumed that the design domain has been discretized by the finite element method and that the design variable vector is a binary vector indicating presence or absence of material in the various finite elements. Common to all papers is the incorporation of von Mises stresses in the problem formulations. In the first paper the design variables are binary but it is assumed that the void structure can actually take some load. This is equivalent to adding a small positive value, epsilon, to all design variables, both those that are void and those that are filled with material. With this small positive lower bound the stiffness matrix becomes positive definite for all designs. If only one element is changed (from material to void or from void to material) the new global stiffness matrix is just a low rank modification of the old one and thus the Sherman-Morrison-Woodbury formula can be used to compute the displacements in the neighbouring designs efficiently. These efficient sensitivity calculations can then be applied in the context of a neighbourhood search method. Since the computed displacements are exact in the 1-neighbourhood (when one design variable is changed) the neighbourhood search method will find a local optimum with respect to the 1-neighbourhood. The second paper presents globally optimal zero-one solutions to some small scale topology optimization problems defined on discretized continuum design domains. The idea is that these solutions can be used as benchmarks when testing new algorithms for finding pure zero-one solutions to topology optimization problems. In the third paper the results from the first paper are extended to include also the case where there is no epsilon&gt;0. In this case the stiffness matrix will no longer be positive definite which means that the Sherman-Morrison-Woodbury formula can no longer be applied. The changing of one or two binary design variables to their opposite binary values will still result in a low rank change, but the size of the reduced stiffness matrix will change with the design. It turns out, however, that it is possible to compute the effect of these low rank changes efficiently also without the positive lower bound. These efficient sensitivity calculations can then be used in the framework of a neighbourhood search method. In this case the complete 1-neighbourhood and a subset of the 2-neighbourhood is investigated in the search for a locally optimal solution. In the fourth paper the sensitivity calculations developed in the third paper are used to generate first and partial second order approximations of the nonlinear functions usually present in topology optimization problems. These approximations are then used to generate subproblems in two different sequential integer programming methods (SLIP and SQIP, respectively). Both these methods generate a sequence of iteration points that can be proven to converge to a local optimum with respect to the 1-neighbourhood. The methods are tested on some different topology optimization problems. The fifth paper demonstrates that the SLIP method developed in the previous paper can be applied also to the mechanism design problem with stress constraints. In order to generate the subproblems in a fast way small displacements are assumed, which implies that the efficient sensitivity calculations derived in the third paper can be used. The numerical results indicate that the method can be used to lower the stresses and still get a functional mechanism. In the sixth paper the SLIP method developed in the fourth paper is used as a post processor to obtain locally optimal zero-one solutions starting from a rounded solution to the corresponding continuous problem. The numerical results indicate that the method can perform well as a post processor. The seventh paper is a theoretical paper that investigates the validity of the commonly used positive lower bound epsilon on the design variables when stating and solving topology optimization problems defined on discretized load carrying continuum structures. The main result presented here is that an optimal "epsilon-1" solution to an "epsilon-perturbed" discrete minimum weight problem with constraints on compliance, von Mises stresses and strain energy densities, is optimal, after rounding to zero-one, to the corresponding "unperturbed" discrete problem. This holds if the constraints in the perturbed problem are carefully defined and epsilon&gt;0 is sufficiently small. / QC 20100917