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This dissertation addresses the development of enhanced formulations for minimax and mixed-integer programming models for certain industrial and logistical systems, along with the design and implementation of efficient algorithmic strategies. We first examine the general class of minimax mixed-integer 0-1 problems of the type that frequently arise in decomposition approaches and in a variety of location and scheduling problems. We conduct an extensive polyhedral analysis of this problem in order to tighten its representation using the Reformulation-Linearization/Convexification Technique (RLT), and demonstrate the benefits of the resulting lifted formulations for several classes of problems. Specifically, we investigate RLT-enhanced Lagrangian dual formulations for the class of minimax mixed-integer 0-1 problems in concert with deflected/conjugate subgradient algorithms. In addition, we propose two general purpose lifting mechanisms for tightening the mathematical programming formulations associated with such minimax optimization problems. Next, we explore novel continuous nonconvex as well as lifted discrete formulations for the notoriously challenging class of job-shop scheduling problems with the objective of minimizing the maximum completion time (i.e., minimizing the makespan). In particular, we develop an RLT-enhanced continuous nonconvex model for the job-shop problem based on a quadratic formulation of the job sequencing constraints on machines. The tight linear programming relaxation that is induced by this formulation is then embedded in a globally convergent branch-and-bound algorithm. Furthermore, we design another novel formulation for the job-shop scheduling problem that possesses a tight continuous relaxation, where the non-overlapping job sequencing constraints on machines are modeled via a lifted asymmetric traveling salesman problem (ATSP) construct, and specific sets of valid inequalities and RLT-based enhancements are incorporated to further tighten the resulting mathematical program. The efficacy of our enhanced models is demonstrated by an extensive computational experiment using classical benchmark problems from the literature. Our results reveal that the LP relaxations produced by the lifted ATSP-based models provide very tight lower bounds, and directly yield a 0\% optimality gap for many benchmark problems, thereby substantially dominating other alternative mixed-integer programming models available for this class of problems. Notably, our lifted ATSP-based formulation produced a 0\% optimality gap via the root node LP relaxation for 50\% of the classical problem instances due to Lawrence. We also investigate enhanced model formulations and specialized, efficient solution methodologies for applications arising in four particular industrial and sports scheduling settings. The first of these was posed to us by a major trucking company (Volvo Logistics North America), and concerns an integrated assembly and routing problem, which is a unique study of its kind in the literature. In this context, we examine the general class of logistical systems where it is desirable to appropriately ascertain the joint composition of the sequences of vehicles that are to be physically connected along with determining their delivery routes. Such assembly-routing problems occur in the truck manufacturing industry where different models of vehicles designed for a network of customers need to be composed into compatible groups (assemblies) and subsequently dispatched via appropriately optimized delivery routes that are restricted by the particular sequence in which the trucks are connected. A similar structure is exhibited in the business of shipping goods via boat-towed barges along inland waterways, or via trains through railroad networks. We present a novel modeling framework and column generation-based optimization approach for this challenging class of joint vehicle assembly-routing problems. In addition, we suggest several extensions to accommodate particular industrial restrictions where assembly sequence-dependent delivery routes are necessary, as well as those where limited driver- and equipment-related resources are available. Computational experience is provided using large-scale realistic data to demonstrate the applicability of our suggested methodology in practice. The second application addressed pertains to a production planning problem faced by a major motorcycle manufacturing firm (Harley-Davidson Motor Company). We consider the problem of partitioning and sequencing the production of different manufactured items in mixed-model assembly lines, where each model has various specific options and designated destinations. We propose a mixed-integer programming formulation (MPSP1) for this problem that sequences the manufactured goods within production batches in order to balance the motorcycle model and destination outputs as well as the load demands on material and labor resources. An alternative (relaxed) formulation (MPSP2) is also presented to model a closely related case where all production decisions and outputs are monitored within a common sequence of batches, which permits an enhanced tighter representation via an additional set of hierarchical symmetry-defeating constraints that impart specific identities amongst batches of products under composition. The latter model inspires a third set partitioning-based formulation in concert with an efficient column generation approach that directly achieves the joint partitioning of jobs into batches along with ascertaining the sequence of jobs within each composed batch. Finally, we investigate a subgradient-based optimization strategy that exploits a non-differentiable optimization formulation, which is prompted by the flexibility in the production process as reflected in the model via several soft-constraints, thereby providing a real-time decision-making tool. Computational experience is presented to demonstrate the relative effectiveness of the different proposed formulations and the associated optimization strategies for solving a set of realistic problem instances. The third application pertains to the problem of matching or assigning subassembly parts in assembly lines, where we seek to minimize the total deviation of the resulting final assemblies from a vector of nominal and mean quality characteristic values. We introduce three symmetry-defeating enhancements for an existing assignment-based model, and highlight the critical importance of using particular types of symmetry-defeating hierarchical constraints that preserve the model structure. We also develop an alternative set partitioning-based formulation in concert with a column generation approach that efficiently exploits the structure of the problem. A special complementary column generation feature is proposed, and we provide insights into its vital role for the proposed column generation strategy, as well as highlight its benefits in the broader context of set partitioning-based formulations that are characterized by columns having relatively dense non-zero values. In addition, we develop several heuristic procedures. Computational experience is presented to demonstrate the relative effectiveness of the different adopted strategies for solving a set of realistic problem instances. Finally, we analyze a doubles tennis scheduling problem in the context of a training tournament as prompted by a tennis club in Virginia, and develop two alternative 0-1 mixed-integer programming models, each with three different objective functions that attempt to balance the partnership and the opponentship pairings among the players. Our analysis and computational experience demonstrate the superiority of one of these models over the other, and reflect the importance of model structure in formulating discrete optimization problems. Furthermore, we design effective symmetry-defeating strategies that impose certain decision hierarchies within the models, which serve to significantly enhance algorithmic performance. In particular, our study provides the insight that the special structure of the mathematical program to which specific tailored symmetry-defeating constraints are appended can greatly influence their pruning effect. We also propose a novel nonpreemptive multi-objective programming strategy in concert with decision hierarchies, and highlight its effectiveness and conceptual value in enhancing problem solvability. Finally, four specialized heuristics are devised and are computationally evaluated along with the exact solution schemes using a set of realistic practical test problems. Aside from the development of specialized effective models and algorithms for particular interesting and challenging applications arising in different assembly, routing, and scheduling contexts, this dissertation makes several broader contributions that emerge from the foregoing studies, which are generally applicable to solving formidable combinatorial optimization problems. First, we have shown that it is of utmost importance to enforce symmetry-defeating constraints that preserve the structure of mathematical programs to which they are adjoined, so that their pruning effects are most efficiently coupled with the branch-and-bound strategies that are orchestrated within mathematical programming software packages. In addition, our work provides the insight that the concept of symmetry compatible formulations plays a crucial role in the effectiveness of implementing any particular symmetry-defeating constraints. In essence, if the root node LP solution of the original formulation does not conform relatively well with the proposed symmetry-defeating hierarchical constraints, then a significant branching effort might be required to identify a good solution that is compatible with the pattern induced by the selected symmetry-defeating constraints. Therefore, it is advisable to enforce decision hierarchies that conform as much as possible with the problem structure as well as with the initial LP relaxation. Second, we have introduced an alternative concept for defeating symmetry via augmented objective functions. This concept prompts the incorporation of objective perturbation terms that discriminate amongst subsets of originally undistinguishable solution structures and, in particular, leads to the development of a nonpreemptive multiobjective programming approach based on, and combined with, symmetry-defeating constraints. Interestingly, nonpreemptive multiobjective programming approaches that accommodate symmetry-defeating hierarchical objective terms induce a root node solution that is compatible with the imposed symmetry-defeating constraints, and hence affords an automated alternative to the aforementioned concept of symmetry compatible formulations. Third, we have proposed a new idea of complementary column generation in the context of column generation approaches that generally provide a versatile framework for analyzing industrial-related, integrated problems that involve the joint optimization of multiple operational decisions, such as assembly and routing, or partitioning and scheduling. In such situations, we have reinforced the insight that assignment-related problems that involve collections of objects (production batches, final assemblies, etc.) whose permutation yields equivalent symmetric solutions may be judiciously formulated as set partitioning models. The latter can then be effectively tackled via column generation approaches, thereby implicitly obviating the foregoing combinatorial symmetric reflections through the dynamic generation of attractive patterns or columns. The complementary column generation feature we have proposed and investigated in this dissertation proves to be particularly valuable for such set partitioning formulations that involve columns having relatively dense non-zero values. The incorporation of this feature guarantees that every LP iteration (involving the solution of a restricted master program and its associated subproblem) systematically produces a consistent set of columns that collectively qualify as a feasible solution to the problem under consideration. Upon solving the problem to optimality as a linear program, the resultant formulation encompasses multiple feasible solutions that generally include optimal or near-optimal solutions to the original integer-restricted set partitioning formulation, thereby yielding a useful representation for designing heuristic methods as well as exact branch-and-price algorithms. In addition, using duality theory and considering set partitioning problems where the number of patterns needed to collectively compose a feasible solution is bounded, we have derived a lower bound on the objective value that is updated at every LP phase iteration. By virtue of this sequence of lower bounds and the availability of upper bounds via the restricted master program at every LP phase iteration, the LP relaxation of the set partitioning problem is efficiently solved as using a pre-specified optimality tolerance. This yields enhanced algorithmic performance due to early termination strategies that successfully mitigate the tailing-off effect that is commonly witnessed for simplex-based column generation approaches. / Ph. D.

In formulating discrete optimization problems, it is not only important to have a correct mathematical model, but to have a well structured model that can be solved effectively. Two important characteristics of a general integer or mixed-integer program are its size (the number of constraints and variables in the problem), and its strength or tightness (a measure of how well it approximates the convex hull of feasible solutions). In designing model formulations, it is critical to ensure a proper balance between compactness of the representation and the tightness of its linear relaxation, in order to enhance its solvability. In this dissertation, we consider these issues pertaining to the modeling of mixed-integer 0-1 programming problems in general, as well as in the context of several specific real-world applications, including a telecommunications network design problem and an airspace management problem. We first consider the Reformulation-Linearization Technique (RLT) of Sherali and Adams and explore the generation of reduced first-level representations for mixed-integer 0-1 programs that tend to retain the strength of the full first-level linear programming relaxation. The motivation for this study is provided by the computational success of the first-level RLT representation (in full or partial form) experienced by several researchers working on various classes of problems. We show that there exists a first-level representation having only about half the RLT constraints that yields the same lower bound value via its relaxation. Accordingly, we attempt to a priori predict the form of this representation and identify many special cases for which this prediction is accurate. However, using various counter-examples, we show that this prediction as well as several variants of it are not accurate in general, even for the case of a single binary variable. Since the full first-level relaxation produces the convex hull representation for the case of a single binary variable, we investigate whether this is the case with respect to the reduced first-level relaxation as well, and show similarly that it holds true only for some special cases. Empirical results on the prediction capability of the reduced, versus the full, first-level representation demonstrate a high level of prediction accuracy on a set of random as well as practical, standard test problems. Next, we focus on a useful modeling concept that is frequently ignored while formulating discrete optimization problems. Very often, there exists a natural symmetry inherent in the problem itself that, if propagated to the model, can hopelessly mire a branch-and-bound solver by burdening it to explore and eliminate such alternative symmetric solutions. We discuss three applications where such a symmetry arises. For each case, we identify the indistinguishable objects in the model which create the problem symmetry, and show how imposing certain decision hierarchies within the model significantly enhances its solvability. These hierarchies render an otherwise virtually intractable formulation computationally viable using commercial software. For the first problem, we consider a problem of minimizing the maximum dosage of noise to which workers are exposed while working on a set of machines. We next examine a problem of minimizing the cost of acquiring and utilizing machines designed to cool large facilities or buildings, subject to minimum operational requirements. For each of these applications, we generate realistic test beds of problems. The decision hierarchies allow all previously intractable problems to be solved relatively quickly, and dramatically decrease the required computational time for all other problems. For the third problem, we investigate a network design problem arising in the context of deploying synchronous optical networks (SONET) using a unidirectional path switched ring architecture, a standard of transmission using optical fiber technology. Given several rings of this type, the problem is to find a placement of nodes to possibly multiple rings, and to determine what portion of demand traffic between node pairs spanned by each ring should be allocated to that ring. The constraints require that the demand traffic between each node pair should be satisfiable given the ring capacities, and that no more than a specified maximum number of nodes should be assigned to each ring. The objective function is to minimize the total number of node-to-ring assignments, and hence, the capital investment in add-drop multiplexer equipments. We formulate the problem as a mixed-integer programming model, and propose several alternative modeling techniques designed to improve the mathematical representation of this problem. We then develop various classes of valid inequalities for the problem along with suitable separation procedures for tightening the representation of the model, and accordingly, prescribe an algorithmic approach that coordinates tailored routines with a commercial solver (CPLEX). We also propose a heuristic procedure which enhances the solvability of the problem and provides bounds within 5-13% of the optimal solution. Promising computational results that exhibit the viability of the overall approach and that lend insights into various modeling and algorithmic constructs are presented. Following this we turn our attention to the modeling and analysis of several issues related to airspace management. Currently, commercial aircraft are routed along certain defined airspace corridors, where safe minimum separation distances between aircraft may be routinely enforced. However, this mode of operation does not fully utilize the available airspace resources, and may prove to be inadequate under future National Airspace (NAS) scenarios involving new concepts such as Free-Flight. This mode of operation is further compounded by the projected significant increase in commercial air traffic. (Free-Flight is a paradigm of aircraft operations which permits the selection of more cost-effective routes for flights rather than simple traversals between designated way-points, from various origins to different destinations.) We begin our study of Air Traffic Management (ATM) by first developing an Airspace Sector Occupancy Model (AOM) that identifies the occupancies of flights within three dimensional (possibly nonconvex) regions of space called sectors. The proposed iterative procedure effectively traces each flight's progress through nonconvex sector modules which comprise the sectors. Next, we develop an Aircraft Encounter Model (AEM), which uses the information obtained from AOM to efficiently characterize the number and nature of blind-conflicts (i.e., conflicts under no avoidance or resolution maneuvers) resulting from a selected mix of flight-plans. Besides identifying the existence of a conflict, AEM also provides useful information on the severity of the conflict, and its geometry, such as the faces across which an intruder enters and exits the protective shell or envelope of another aircraft, the duration of intrusion, its relative heading, and the point of closest approach. For purposes of evaluation and assessment, we also develop an aggregate metric that provides an overall assessment of the conflicts in terms of their individual severity and resolution difficulty. We apply these models to real data provided by the Federal Aviation Administration (FAA) for evaluating several Free-Flight scenarios under wind-optimized and cruise-climb conditions. We digress at this point to consider a more general collision detection problem that frequently arises in the field of robotics. Given a set of bodies with their initial positions and trajectories, we wish to identify the first collision that occurs between any two bodies, or to determine that none exists. For the case of bodies having linear trajectories, we construct a convex hull representation of the integer programming model of Selim and Almohamad, and exhibit the relative effectiveness of solving this problem via the resultant linear program. We also extend this analysis to model a situation in which bodies move along piecewise linear trajectories, possibly rotating at the end of each linear translation. For this case, we again compare an integer programming approach with its linear programming convex hull representation, and exhibit the relative effectiveness of solving a sequence of problems based on applying the latter construct to each time segment. Returning to Air Traffic Management, another future difficulty in airspace resource utilization stems from a projected increase in commercial space traffic, due to the advent of Reusable Launch Vehicle (RLV) technology. Currently, each shuttle launch cordons off a large region of Special Use Airspace (SUA) in which no commercial aircraft are permitted to enter for the specified duration. Of concern to airspace planners is the expense of routinely disrupting air traffic, resulting in circuitous diversions and delays, while enforcing such SUA restrictions. To provide a tool for tactical and planning purposes in such a context within the framework of a coordinated decision making process between the FAA and commercial airlines, we develop an Airspace Planning Model (APM). Given a set of flights for a particular time horizon, along with (possibly several) alternative flight-plans for each flight that are based on delays and diversions due to special-use airspace (SUA) restrictions prompted by launches at spaceports or weather considerations, this model prescribes a set of flight-plans to be implemented. The model formulation seeks to minimize a delay and fuel cost based objective function, subject to the constraints that each flight is assigned one of the designated flight-plans, and that the resulting set of flight-plans satisfies certain specified workload, safety, and equity criteria. These requirements ensure that the workload for air-traffic controllers in each sector is held under a permissible limit, that any potential conflicts which may occur are routinely resolvable, and that the various airlines involved derive equitable levels of benefits from the overall implemented schedule. In order to solve the resulting 0-1 mixed-integer programming problem more effectively using commercial software (CPLEX-MIP), we explore the use of various facetial cutting planes and reformulation techniques designed to more closely approximate the convex hull of feasible solutions to the problem. We also prescribe a heuristic procedure which is demonstrated to provide solutions to the problem that are either optimal or are within 0.01% of optimality. Computational results are reported on several scenarios based on actual flight data obtained from the Federal Aviation Administration (FAA) in order to demonstrate the efficacy of the proposed approach for air traffic management (ATM) purposes. In addition to the evaluation of these various models, we exhibit the usefulness of this airspace planning model as a strategic planning tool for the FAA by exploring the sensitivity of the solution provided by the model to changes both in the radius of the SUA formulated around the spaceport, and in the duration of the launch-window during which the SUA is activated. / Ph. D.

In this dissertation, we develop feedback control strategies that can be used for evacuating people. Pedestrian models are based on macroscopic or microscopic behavior. We use the macroscopic modeling approach, where pedestrians are treated in an aggregate way and detailed interactions are overlooked. The models representing evacuation dynamics are based on the laws of conservation of mass and momentum and are described by nonlinear hyperbolic partial differential equations. As such the system is distributed in nature. We address the design of feedback control for these models in a distributed setting where the problem of control and stability is formulated directly in the framework of partial differential equations. The control goal is to design feedback controllers to control the movement of people during evacuation and avoid jams and shocks. We design the feedback controllers for both diffusion and advection where the density of people diffuses as well as moves in a specified direction with time. In order to achieve this goal we are assuming that the control variables have no bounds. However, it is practically impossible to have unbounded controls so we modify the controllers in order to take the effect of control saturation into account. We also discuss the feedback control for these models in presence of uncertainties where the goal is to design controllers to minimize the effect of uncertainties on the movement of people during evacuation. The control design technique adopted in all these cases is feedback linearization which includes backstepping for higher order two-equation models, Lyapunov redesign for uncertain models and robust backstepping for two-equation uncertain models. The work also focuses on abstraction of evacuation system which focuses on obtaining models with lesser number of partial differential equations than the original one. The feedback control design of a higher level two-equation model is more difficult than the lower order one-equation model. Therefore, it is desirable to perform control design for a simpler abstracted model and then transform control design back to the original model. / Ph. D.

The continuous conduction mode (CCM) boost, buck-boost and buck-boost derived pulse-width modulation dc-dc converters suffer from the large-signal control-to-output nonlinearity. Without feedback control, the large-signal control-to-output nonlinearity would lead to output overregulation and even damage the components. The control gain is defined as the ratio of output voltage to control signal. The small-signal control gain is defined as differentiating output voltage with respect to control signal. Feedback control helps to make the output trace the reference signal. A large-signal control-to-output linearity is established. Compared with open loop control, the feedback loop design is complex; and the feedback control might suffer from the instability caused by the negative small-signal control gain, which is due to the loss and parasitic in practice. Except feedback control, open loop linearization methods can also realize the large-signal control-to-output linearity. A modulated-ramp pulse-width modulation generator is introduced in [6]. A current source works as the control signal. A capacitor is charged by the current source, whose voltage works as the carrier and compared with a constant dc bias voltage to determine the duty cycle. When applying this method to boost, buck-boost and buck-boost derived PWM dc-dc converters, a large-signal control-to-output linearity is established. However, the control gain is dependent on the input voltage; it cannot maintain constant when input voltage varies. A feedforward pulse width modulator is introduced in [39] to realize a large-signal control-to-output linearity. The static conversion ratio is divided into numerator and denominator as the functions of duty cycle. An integrator with reset clock signal helps to determine the right timing. The control gain is ideally constant and independent of input voltage. However, the mismatch between the integrator time constant and the switching period would result in a nonlinear control gain, which is dependent on the input voltage. In the thesis work, a self-oscillating unified linearizing modulator is introduced. It first provides a unified procedure to establish a large-signal control-to-output linearity for different pulse-width modulation dc-dc converters. Feedforward is employed to mitigate the impact from line voltage. Self-oscillation is adopted to provide the internal clock signal and to determine the switching frequency. A constant control gain is obtained, independent on the input voltage or the mismatch between clock signals. The modulator is constructed by three simple and standard building blocks. With the considerations of parasitic components and loss, how to design the constant gain, which excludes the negative small-signal control gain within the entire control signal range, is analyzed and discussed. The performance of this self-oscillating unified linearizing modulator is verified by experiments. The impacts from propagation delay in practical components are taken into considerations, which improves the quality of generated signals. Combined with a boost converter, a good large-signal control-to-output linearization is demonstrated. In the future work, the small-signal control-to-output transfer function is first deduced based on the SOUL modulator. Bode plots show the unique characteristic based on the SOUL modulator compared with the conventional modulator. Next, the impacts from this unique characteristic to feedback loop design and dynamic performance are discussed. / Master of Science

Gallium Nitride (GaN) technology is capable of very high power levels but suffers from high non-linearity. With the advent of 5G technologies, high linearity is in greater demand due to complex modulation schemes and crowded RF (Radio Frequency) spectrum. Because of the non-linearity issue, GaN power amplifiers have to be operated at back-off input power levels. Operating at back-off reduces the efficiency of the power amplifier along-with the output power. This research presents a technique to linearize GaN amplifiers. The linearity can be improved by splitting a large device into multiple smaller devices and biasing them individually. This leads to the cancellation of the IMD3 (Third-order Intermodulation Distortion) components at the output of the FETs and hence higher linearity performance. This technique has been demonstrated in Silicon technology but has not been previously implemented in GaN. This research work presents for the first time the implementation of this technique in GaN Technology. By the application of this technique, improvement in IMD3 of 4 dBc has been shown for a 0.8-1.0 GHz PA (Power Amplifier), and 9.5 dBm in OIP3 (Third-order Intercept Point) for an S-Band GaN LNA, with linearity FOM (IP3/DC power) reaching up to 20. Large-signal simulation and analysis have been done to demonstrate linearity improvement for two parallel and four parallel FETs. A simulation methodology has been discussed in detail using commercial CAD software. A power sampler element is used to compute the IMD3 currents coming out of various FETs due to various bias currents. Simulation results show by biasing one device in Class AB and others in deep Class AB, IMD3 components of parallel FETs can be made out of phase of each other, leading to cancellation and improvement in linearity. Improvement up to 20 dBc in IMD3 has been reported through large-signal simulation when four parallel FETs with optimum bias were used. This technique has also been demonstrated in simulation for an X-Band MMIC PA from 8-10 GHz in GaN technology. Improvements up to 25-30 dBc were shown using the technique of biasing one device with Class AB and other with deep class AB/class B. The proposed amplifier achieves broadband linearization over the entire frequency compared to state-of-the-art PA's. The linearization technique demonstrated is simple, straight forward, and low cost to implement. No additional circuitry is needed. This technique finds its application in high dynamic range RF amplifier circuits for communications and sensing applications. / Doctor of Philosophy / Power amplifiers (PAs) and Low Noise Amplifiers (LNAs) form the front end of the Radio Frequency (RF) transceiver systems. With the advent of complex modulation schemes, it is becoming imperative to improve their linearity. Through this dissertation, we propose a technique for improving the linearity of amplifier circuits used for communication systems. Meanwhile, Gallium Nitride (GaN) is becoming a technology of choice for high-power amplifier circuits due to its higher power handling capability and higher breakdown voltage compared with Gallium Arsenide (GaAs), Silicon Germanium (SiGe) and Complementary Metal-Oxide-Semiconductor (CMOS) technologies. A circuit design technique of using multiple parallel GaN FETs is presented. In this technique, the multiple parallel FETs have independently controllable gate voltages. Compared to a large single FET, using multiple FETs and biasing them individually helps to improve the linearity through the cancellation of nonlinear distortion components. Experimental results show the highest linearity improvement compared with the other state-of-the-art linearization schemes. The technique demonstrated is the first time implementation in GaN technology. The technique is a simple and cost-effective solution for improving the linearity of the amplifier circuits. Applications include base station amplifiers, mobile handsets, radars, satellite communication, etc.

This paper presents a novel controller architecture and speed control design methodology suitable for low cost, low performance switched reluctance motor drives. By utilizing inexpensive components in a simple, compact architecture, a low cost controller is developed which achieves a performance level similar to many high performance controllers. A speed control design methodology is established and analyzed based on the linearized small signal model of the switched reluctance motor. This unique control methodology is simple and provides a starting point for further research into speed/current controller parameter design for switched reluctance motors. The analysis, design and realization of the speed controller are presented. The derivation of the design methodology for speed controlled, switched reluctance motor drives is discussed, along with computer simulations for verification. Experimental results utilizing the proposed architecture and design methodology verify the control design and performance capabilities of the speed controller system. / Master of Science

Traffic incidents routinely impact the flow of vehicles on roadways. These incidents need to be identified, and responded to in a timely fashion in order to keep traffic moving safely and efficiently. One of the main areas of transportation research that remains of contemporary interest is the study of travel times. Travel time information technologies, until very recently, have not been efficient enough to provide instantaneous information for managing traffic flow. The Virginia Department of Transportation (VDOT) currently operates a number of surveillance technologies. Of particular interest to us are Automatic Vehicle Identification (AVI) tag readers to assimilate travel time information. One of VDOT's latest research thrusts has been to develop efficient algorithms for estimating link travel times using such advanced technologies. To achieve this purpose, VDOT is currently monitoring volunteer tagged cars by using AVI tag readers fixed at certain specific locations. This thesis focuses on devising an efficient methodology to capture as much travel time information as possible, by solving a Reader Location Problem that maximizes the benefit accruing from measuring travel time variability with respect to freeways. This problem is formulated as a quadratic 0-1 optimization problem. The objective function parameters in the optimization problem represent certain benefit factors resulting from the ability to measure travel time variability along various origin-destination paths. A simulation study using the INTEGRATION package is performed to derive these benefit factors for various types of freeway sections, and two composite functions that measure benefits for O-D paths that are comprised of several such sections are presented. The simulation results are presented as generic look-up tables, and can be used for any freeway section for the purpose of computing the associated benefit factor coefficient. An optimization approach based on the Reformulation-Linearization Technique coupled with Semidefinite Programming concepts is designed to solve the formulated reader location problem. This approach can be used to derive alternative equivalent formulations of the problem that vary in the degree of tightness of their underlying linear programming relaxations. Four such model representations are explored by using the software package, AMPL-CPLEX 6.5.3, to solve them for some sample transportation networks. The sensitivity of the reader locations to the different proposed benefit factor composite functions is also investigated. The results indicate that the first level continuous RLT relaxation to problem RL produces a tight underlying representation and that the optimal solution obtained for this relaxation tends to be very close to the actual integer optimum. Moreover, it is found that the optimal locations of the readers are insensitive to either the traffic, or the benefit factor used, or the density of the graph, when these factors are considered individually. However, a combination of two or more of these factors can lead to a change in the optimal locations of the readers. / Master of Science