The increasing demand for mobile communication services without a corresponding increase in RF spectrum allocation motivates the need for new techniques to improve spectrum utilization. The CDMA and adaptive antenna array are two approaches that shows real promise for increasing spectrum efficiency. In this research, we investigate the performance of different blind adaptive array algorithms in the CDMA systems. Two novel algorithms, least-squares despread respread multitarget array (LS-DRMTA) and least-squares despread respread multitarget constant modulus algorithm (LS-DRMTCMA), are developed, and a MATLAB simulation testbed is created to compare the performance of these two novel algorithms with those of the multitarget least-squares constant modulus algorithm (MT-LSCMA) and multitarget steepest-descent decision-directed (MT-SDDD) algorithm. It is shown from the simulation results that these two novel algorithms can outperform the other algorithms in all the test situations (e.g., AWGN channel, timing offset case, frequency offset case, and multipath environment). It is also shown that these two algorithms have less complexity and can converge faster than the other algorithms. / Master of Science

The presence of substitution among multiple similar products plays an important role in inventory management. It has been observed in the literature that incorporating the impact of substitution among products can substantially improve the profit and reduce the understock or overstock risk. This thesis focuses on exploring and exploiting the impact of substitution on inventory management problems by theoretically analyzing mathematical models and developing efficient solution approaches. To that end, we address four problems. In the first problem, we study different pricing strategies and the role of substitution for new and remanufactured products. Our work presents a two-stage model for an original equipment manufacturer (OEM) in this regard. A closed-form one-to-one mapping of product designs onto the optimal product strategies is developed, which provides useful information for the retailer. Our second problem is a multi-product newsvendor problem with customer-driven demand substitution. We completely characterize the optimal order policy when the demand is known and reformulate this nonconvex problem as a binary quadratic program. When the demand is stochastic, we formulate the problem as a two-stage stochastic program with mixed integer recourse, derive several necessary optimality conditions, prove the submodularity of the profit function, develop polynomial-time approximation algorithms, and show their performance guarantees. Our numerical investigation demonstrates the effectiveness of the proposed algorithms and, furthermore, reveals several useful findings and managerial insights. In the third problem, we study a robust multi-product newsvendor model with substitution (R-MNMS), where both demand and substitution rates are uncertain and are subject to cardinality-constrained uncertainty set. We show that for given order quantities, computing the worst-case total profit, in general, is NP-hard, and therefore, address three special cases for which we provide closed-form solutions. In practice, placing an order might incur a fixed cost. Motivated by this fact, our fourth problem extends the R-MNMS by incorporating fixed cost (denoted as R-MNMSF) and develop efficient approaches for its solution. In particular, we propose an exact branch-and-cut algorithm to solve small- or medium-sized problem instances of the R-MNMSF, and for large-scale problem instances, we develop an approximation algorithm. We further study the effects of the fixed cost and show how to tune the parameters of the uncertainty set. / Doctor of Philosophy / In a multi-product supply chain, the substitution of products arises if a customer's first-choice product is out-of-stock, and she/he have to turn to buy another similar product. It has been shown in the literature that the presence of product substitution reduces the assortment size, and thus, brings in more profit. %and reduce the inventory level. However, how to quantitatively study and analyze substitution effects has not been addressed in the literature. This thesis fills this gap by developing and analyzing the profit model, and therefore, providing judicious decisions for the retailer to make in order to maximize their profit. In our first problem, we consider substitution between new products and remanufactured products. We provide closed-form solutions, and a mapping that can help the retailer in choosing optimal prices and end-of-life options given a certain product design. In our second problem, we study multi-product newsvendor model with substitution. We first show that, when the probability distribution of customers' demand is known, we can tightly approximate the proposed model as a stochastic integer program under discrete support. Next, we provide effective solution approaches to solve the multi-product newsvendor model with substitution. In practice, typically, there is a limited information available on the customers' demand or substitution rates, and therefore, for our third problem, we study a robust model with a cardinality uncertainty set to account for these stochastic demand and substitution rates. We give closed-form solutions for the following three special cases: (1) there are only two products, (2) there is no substitution among different products, and (3) the budget of uncertainty is equal to the number of products. Finally, similar to many inventory management problems, we include a fixed cost in the robust model and develop efficient approaches for its solution. The numerical study demonstrates the effectiveness of the proposed methods and the robustness of our model. We further illustrate the effects of the fixed cost and how to tune the parameters of the uncertainty set.

Since the discovery three decades ago, fullerenes as well as metallofullerenes have been extensively investigated. However, almost all known fullerenes follow the classical definition, that is, classic fullerenes are comprised of only pentagons and hexagons. Nowadays, more and more evidence, from both theoretical and experimental studies, suggests that non-classical fullerenes, especially heptagon-containing fullerenes, are important as intermediates in fullerene formation mechanisms. To obtain fundamental understandings of fullerenes and their formation mechanisms, new systematic studies should be undertaken. Although necessary tools, such as isomer generating programs, have been developed for classical fullerenes, none of them are able to solve problems related to non-classical fullerenes. In this thesis, existing theories and algorithms of classical fullerenes are generalized to accommodate non-classical fullerenes. A new program based on these generalized principles is provided for generating non-classical isomers. Along with this program, other tools are also attached for accelerating future investigations of non-classical fullerenes. In addition, research to date is also reviewed. / Master of Science

The k–server problem is of significant importance to the theoretical computer science and the operations research community. In this problem, we are given k servers, their initial locations and a sequence of n requests that arrive one at a time. All these locations are points from some metric space and the cost of serving a request is given by the distance between the location of the request and the current location of the server selected to process the request. We must immediately process the request by moving a server to the request location. The objective in this problem is to minimize the total distance traveled by the servers to process all the requests. In this thesis, we present an empirical analysis of a new online algorithm for k-server problem. This algorithm maintains two solutions, online solution, and an approximately optimal offline solution. When a request arrives we update the offline solution and use this update to inform the online assignment. This algorithm is motivated by the Robust-Matching Algorithm [RMAlgorithm, Raghvendra, APPROX 2016] for the closely related online bipartite matching problem. We then give a comprehensive experimental analysis of this algorithm and also provide a graphical user interface which can be used to visualize execution instances of the algorithm. We also consider these problems under stochastic setting and implement a lookahead strategy on top of the new online algorithm. / MS / Motivated by real-time logistics, we study the online versions of the well-known bipartite matching and the k-server problems. In this problem, there are servers (delivery vehicles) located in different parts of the city. When a request for delivery is made, we have to immediately assign a delivery vehicle to this request without any knowledge of the future. Making cost-effective assignments, therefore, becomes incredibly challenging. In this thesis, we implement and empirically evaluate a new algorithm for the k-server and online matching problems.

As the Earth's atmosphere evolves due to human activity, today's modern industrial society relies significantly on the scientific community to foresee possible atmospheric complications such as the celebrated greenhouse effect. Scientists, in turn, rely on accurate measurements of the Earth Radiation Budget (ERB) in order to quantify changes in the atmosphere. The Thermal Radiation Group (TRG), a laboratory in the Department of Mechanical Engineering at Virginia Polytechnic Institute and State University, has been at the edge of technology designing and modeling ERB instruments. TRG is currently developing a new generation of thermoelectric detectors for ERB applications. These detectors consist of an array of thermocouple junction pairs that are based on a new thermopile technology using materials whose electro-thermophysical properties are not completely characterized. The objective of this investigation is to design experiments aimed at determining the electro-thermophysical properties of the detector materials. These properties are the thermal conductivity and diffusivity of the materials and the Seebeck coefficient of the thermocouple junctions. Knowledge of these properties will provide fundamental information needed for the development of optimally designed detectors that rigorously meet required design specifications. / Master of Science

In this thesis, we present variants of Shor and Zhurbenko's r-algorithm, motivated by the memoryless and limited memory updates for differentiable quasi-Newton methods. This well known r-algorithm, which employs a space dilation strategy in the direction of the difference between two successive subgradients, is recognized as being one of the most effective procedures for solving nondifferentiable optimization problems. However, the method needs to store the space dilation matrix and update it at every iteration, resulting in a substantial computational burden for large-sized problems. To circumvent this difficulty, we first develop a memoryless update scheme. In the space transformation sense, the new update scheme can be viewed as a combination of space dilation and reduction operations. We prove convergence of this new algorithm, and demonstrate how it can be used in conjunction with a variable target value method that allows a practical, convergent implementation of the method. For performance comparisons we examine other memoryless and limited memory variants, and also prove a modification of a related algorithm due to Polyak that employs a projection on a pair of Kelley's cutting planes. These variants are tested along with Shor's r-algorithm on a set of standard test problems from the literature as well as on randomly generated dual transportation and assignment problems. Our computational experiments reveal that the proposed memoryless space dilation and reduction algorithm (VT-MSDR) and the proposed modification of the Polyak-Kelly cutting plane method (VT-PKC) provide an overall competitive performance relative to the other methods tested with respect to solution quality and computational effort. The r-Algorithm becomes increasingly more expensive with an increase in problem size, while not providing any gain in solution quality. The fixed dilation (with no reduction) strategy (VT-MSD) provides a comparable, though second-choice, alternative to VT-MSDR. Employing a two-step limited memory extension over VT-MSD sometimes helps in improving the solution quality, although it adds to computational effort, and is not as robust a procedure. / Master of Science

The field of Human Resource Management (HRM) has experienced a significant transformation with the emergence of big data and algorithms. Major technology companies have introduced software and platforms for analyzing various HRM practices, such as hiring, compensation, employee engagement, and turnover management, utilizing algorithmic approaches. However, scholarly research has taken a cautious stance, questioning the strategic value and causal inference basis of these tools, while also raising concerns about bias, discrimination, and ethical issues in the applications of algorithms. Despite these concerns, algorithmic management has gained prominence in large organizations, shaping workforce management practices. This thesis aims to address the gap between the rapidly changing market of HRM algorithms and the lack of theoretical understanding. The thesis begins by conducting a comprehensive review of HRM algorithms in HRM practice and scholarship, clarifying their definition, exploring their unique features, and identifying specific topics and research questions in the field. It aims to bridge the gap between academia and practice to enhance the understanding and utilization of algorithms in HRM. I then explore the legal, causal, and moral issues associated with HR algorithms, comparing fairness criteria and advocating for the use of causal modeling to evaluate algorithmic fairness. The multifaceted nature of fairness is illustrated and practical strategies for enhancing justice perceptions and incorporating fairness into HR algorithms are proposed. Finally, the thesis adopts an artifact-centric approach to examine the ethical implications of HRM algorithms. It explores competing views on moral responsibility, introduces the concept of "ethical affordances," and analyzes the distribution of moral responsibility based on different types of ethical affordances. The paper provides a framework for analyzing and assigning moral responsibility to stakeholders involved in the design, use, and regulation of HRM algorithms. Together, these papers contribute to the understanding of algorithms in HRM by addressing the research-practice gap, exploring fairness and accountability issues, and investigating the ethical implications. They offer theoretical insights, practical recommendations, and future research directions for both researchers and practitioners. / Thesis / Doctor of Philosophy (PhD) / This thesis explores the use of advanced algorithms in Human Resource Management (HRM) and how they affect decision-making in organizations. With the rise of big data and powerful algorithms, companies can analyze various HR practices like hiring, compensation, and employee engagement. However, there are concerns about biases and ethical issues in algorithmic decision-making. This research examines the benefits and challenges of HRM algorithms and suggests ways to ensure fairness and ethical considerations in their design and application. By bridging the gap between theory and practice, this thesis provides insights into the responsible use of algorithms in HRM. The findings of this research can help organizations make better decisions while maintaining fairness and upholding ethical standards in HR practices.

Optimization problems of location-assignment correspond to a wide range of real situations, such as factory network design. However most of the previous works seek in most cases at minimizing a cost function. Traffic incidents routinely impact the performance and the safety of the supply. These incidents can not be totally avoided and must be regarded. A way to consider these incidents is to design a network on which multiple assignments are performed. Precisely, the problem we focus on deals with power supplying that has become a more and more complex and crucial question. Many international companies have customers who are located all around the world; usually one customer per country. At the other side of the scale, power extraction or production is done in several sites that are spread on several continents and seas. A strong willing of becoming less energetically-dependent has lead many governments to increase the diversity of supply locations. For each kind of energy, many countries expect to deal ideally with 2 or 3 location sites. As a decrease in power supply can have serious consequences for the economic performance of a whole country, companies prefer to balance equally the production rate among all sites as the reliability of all the sites is considered to be very similar. Sharing equally the demand between the 2 or 3 sites assigned to a given area is the most common way. Despite the cost of the network has an importance, it is also crucial to balance the loading between the sites to guarantee that no site would take more importance than the others for a given area. In case an accident happens in a site or in case technical problems do not permit to satisfy the demand assigned to the site, the overall power supply of this site is still likely to be ensured by the one or two available remaining site(s). It is common to assign a cost per open power plant and another cost that depends on the distance between the factory or power extraction point and the customer. On the whole, such companies who are concerned in the quality service of power supply have to find a good trade-off between this factor and their overall functioning cost. This situation exists also for companies who supplies power at the national scale. The expected number of areas as well that of potential sites, can reach 100. However the targeted size of problem to be solved is 50. This thesis focuses on devising an efficient methodology to provide all the solutions of this bi-objective problem. This proposal is an investigation of close problems to delimit the most relevant approaches to this untypical problem. All this work permits us to present one exact method and an evolutionary algorithm that might provide a good answer to this problem. / Master of Science

The tire-terrain interaction is complex and tremendously important; it impacts the performance and safety of the vehicle and its occupants. Icy roads further enhance these complexities and adversely affect the handling of the vehicle. The analysis of the tire-ice contact focusing on individual aspects of tire construction and operation is imperative for tire industry's future. This study investigates the effects of the tread rubber compound on the drawbar pull performance of tires in contact with an ice layer near its melting point. A set of sixteen tires of eight different rubber compounds were considered. The tires were identical in design and tread patterns but have different tread rubber compounds. To study the effect of the tread rubber compound, all operational parameters were kept constant during the testing conducted on the Terramechanics Rig at the Terramechanics, Multibody, and Vehicle Systems laboratory. The tests led to conclusive evidence of the effect of the tread rubber compound on the drawbar performance (found to be most prominent in the linear region of the drawbar-slip curve) and on the resistive forces of free-rolling tires. Modeling of the tire-ice contact for estimation of temperature rise and water film height was performed using ATIIM 2.0. The performance of this in-house model was compared against three classical tire-ice friction models. A parametrization of the Magic Formula tire model was performed using experimental data and a Genetic Algorithm. The dependence of individual factors of the Magic Formula on the ambient temperature, tire age, and tread rubber compounds was investigated. / Master of Science / The interaction between the tire and icy road conditions in the context of the safety of the occupants of the vehicle is a demanding test of the skills of the driver. The expected maneuvers of a vehicle in response to the actions of the driver become heavily unpredictable depending on a variety of factors like the thickness of the ice, its temperature, ambient temperature, the conditions of the vehicle and the tire, etc. To overcome the issues that could arise, the development of winter tires got a boost, especially with siping and rubber compounding technology. This research focuses on the effects on the tire performance on ice due to the variation in the tread rubber compounds. The experimental accomplishment of the same was performed using the Terramechanics rig at the Terramechanics, Multibody, and Vehicle Systems (TMVS) laboratory. It was found that the effect of the rubber compound is most pronounced in the region where most vehicles operate under normal circumstances. An attempt was made to simulate the temperature rise in the contact patch and the water film that exists due to the localized melting of ice caused by frictional heating. Three classical friction models were used to compare the predictions against ATIIM 2.0, an in-house developed model. Using an optimization technique namely the Genetic Algorithm, efforts were made to understand the effects of the tread rubber compound, the ambient temperature, and the aging of the tire on the parameters of the Magic Formula model, an empirical model describing the performance of the tire.

Microgrid deployment in electric grids improves reliability, efficiency, and quality, as well as the overall sustainability and resiliency of the grid. Specifically, microgrids alleviate the effects of power outages. However, microgrid implementations impose additional challenges on power systems. Microgrid protection is one of the technical challenges implicit in the deployment of microgrids. These challenges occur as a result of the unique properties of microgrid networks in comparison to traditional electrical networks. Differential protection is a fast, selective, and sensitive technique. Additionally, it offers a viable solution to microgrid protection concerns. The differential zone protection scheme is a cost-effective variant of differential protection. To implement a differential zone protection scheme, the network must be split into different protection zones. The reliability of this protection scheme is dependent upon the number of protective zones developed. This thesis proposes a new differential zone protection scheme using a graph partitioning algorithm. A graph partitioning algorithm is used to partition the microgrid into multiple protective zones. The IEEE 13-node microgrid is used to demonstrate the proposed protection scheme. The protection scheme is validated with MATLAB Simulink, and its impact is simulated with DIgSILENT PowerFactory software. Additionally, a comprehensive comparison was made to a comparable differential zone protection scheme. / Master of Science / A microgrid is a group of connected distributed energy resources (DERs) with the loads to be served that acts as a local electrical network. In electric grids, microgrid implementation enhances grid reliability, efficiency, and quality, as well as the system's overall sustainability and resiliency. Microgrids mitigate the consequences of power disruptions. Microgrid solutions, on the other hand, bring extra obstacles to power systems. One of the technological issues inherent in the implementation of microgrids is microgrid protection. These difficulties arise as a result of microgrid networks' distinct characteristics as compared to standard electrical networks. Differential protection is a technique that is fast, selective, and sensitive. It also provides a feasible solution to microgrid protection problems. This protection scheme, on the other hand, is more expensive than others. The differential zone protection scheme is a cost-effective variation of differential protection that lowers protection scheme expenses while improving system reliability. The network must be divided into different protection zones in order to deploy a differential zone protection scheme. The number of protective zones generated determines the reliability of this protection method. Using a network partitioning technique, this thesis presents a new differential zone protection scheme. The microgrid is divided into various protection zones using a graph partitioning algorithm. The proposed protection scheme is demonstrated using the IEEE 13-node microgrid. MATLAB Simulink is used to validate the protection scheme, while DIgSILENT PowerFactory is used to simulate its impact. A comparison of a similar differential zone protection scheme was also done.