Warehouse operations have become an important part of the retail industry today. For many companies in the retail industry, the profit margin can be as little as one cent for every dollar sold. Because of these extremely small profit margins, it is important that companies save as much in costs along the way as they can. One such way is to cut down on warehouse costs. In this paper, we shall study the costs of storing and retrieving products in a pick, pack and ship warehouse for an office supply company. In particular, we will examine possible ways to improve the flow of products into and out of reserve racking, by implementing different storage assignment policies. One way of finding the best possible storage assignments would be to formulate a cost equation along with constraints, and to minimize the cost using linear programming techniques. We shall study the Simplex Method, and we will use it to find optimal locations for a given set of storage tasks. However, the set of products to be stored and retrieved in our warehouse changes from day to day, and is subject to customer demand. Because there is no closed form solution or algorithm to find the optimal storage assignment policy under a random situation, we will use simulation techniques to study different storage assignment policies that could be applied in the warehouse. We will study the efficiency along with the maintenance requirements for each of the different policies and compare them with the current policy being used in the warehouse today.

Este trabalho aborda o problema de dimensionamento portas lógicas e assinalamento de Vt para otimização de potência, área e temporização em circuitos integrados modernos. O fluxo proposto é aplicado aos conjuntos de circuitos de teste dos Concursos do International Symposium on Physical Design (ISPD) de 2012 e 2013. Este fluxo também é adapatado e avaliado nos estágios pós posicionamento e roteamento global em projetos industriais de circuitos integrados, que utilizam uma ferramenta precisa de análise estática de temporização. As técnicas propostas geram as melhores soluções para todos os circuitos de teste do Concurso do ISPD 2013 (no qual foi a ferramenta vencedora), com em média 8% menos consumo de potência estática quando comparada com os outros concorrentes. Além disso, após algumas modificações nos algoritmos, nós reduzimos o consumo em mais 10% em média a pontência estáticas com relação aos resultados do concurso. O foco deste trabalho é desenvolver e aplicar um algoritmo estado-da-arte de seleção portas lógicas para melhorar ainda mais projetos industriais de alto desempenho já otimizados após as fases de posicionamento e roteamento do fluxo de projeto físico industrial. Vamos apresentar e discutir vários problemas encontrados quando da aplicação de técnicas de otimização global em projetos industriais reais que não são totalmente cobertos em publicações encontradas na literatura. Os métodos propostos geram as melhores soluções para todos os circuitos de referência no Concurso do ISPD 2013, no qual foi a solução vencedora. Considerando a aplicação industrial, as técnicas propostas reduzem a potência estática em até 18,2 %, com redução média de 10,4 %, sem qualquer degradação na qualidade de temporização do circuito. / This work addresses the gate sizing and Vt assignment problem for power, area and timing optimization in modern integrated circuits (IC). The proposed flow is applied to the Benchmark Suites of the International Symposium on Physical Design (ISPD) 2012 and 2013 Contests. It is also adapted and evaluated in the post placement and post global routing stage of an industrial IC design flow using a sign-off static timing analysis engine. The proposed techniques are able to generate the best solutions for all benchmarks in the ISPD 2013 Contest (in which we were the winning team), with on average 8% lower leakage with respect to all other contestants. Also, after some refinements in the algorithms, we reduce leakage by another 10% on average over the contest results. The focus of this work is to develop and apply a state-of-the-art cell selection algorithm to further improve already optimized high-performance industrial designs after the placement and routing stages of the industrial physical design flow. We present the basic concepts involved in the gate sizing problem and how earlier literature addresses it. Several problems found when applying global optimization techniques in real-life industrial designs, which are not fully covered in publications found in literature, are presented and discussed. Considering the industrial application, the proposed techniques reduce leakage power by up to 18.2%, with average reduction of 10.4% without any degradation in timing quality.

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Alfred P. Sloan School of Management. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY. / Bibliography: leaves 148-149. / M.S.

<p>Connected and autonomous vehicle (CAV) technologies provide disruptive and transformational opportunities for innovations toward intelligent transportation systems. Compared with human driven vehicles (HDVs), the CAVs can reduce reaction time and human errors, increase traffic mobility and will be more knowledgeable due to vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. CAVs’ potential to reduce traffic accidents, improve vehicular mobility and promote eco-driving is immense. However, the new characteristics and capabilities of CAVs will significantly transform the future of transportation, including the dissemination of traffic information, traffic flow dynamics and network equilibrium flow. This dissertation seeks to realize and enhance the application of CAVs by specifically advancing the research in three connected topics: (1) modeling and controlling information flow propagation within a V2V communication environment, (2) designing a real-time deployable cooperative control mechanism for CAV platoons, and (3) modeling network equilibrium flow with a mix of CAVs and HDVs. </p> <p>Vehicular traffic congestion in a V2V communication environment can lead to congestion effects for information flow propagation due to full occupation of the communication channel. Such congestion effects can impact not only whether a specific information packet of interest is able to reach a desired location, but also the timeliness needed to influence traffic system performance. This dissertation begins with exploring spatiotemporal information flow propagation under information congestion effects, by introducing a two-layer macroscopic model and an information packet relay control strategy. The upper layer models the information dissemination in the information flow regime, and the lower layer model captures the impacts of traffic flow dynamics on information propagation. Analytical and numerical solutions of the information flow propagation wave (IFPW) speed are provided, and the density of informed vehicles is derived under different traffic conditions. Hence, the proposed model can be leveraged to develop a new generation of information dissemination strategies focused on enabling specific V2V information to reach specific locations at specific points in time.</p> <p>In a V2V-based system, multiclass information (e.g., safety information, routing information, work zone information) needs to be disseminated simultaneously. The application needs of different classes of information related to vehicular reception ratio, the time delay and spatial coverage (i.e., distance it can be propagated) are different. To meet the application needs of multiclass information under different traffic and communication environments, a queuing strategy is proposed for each equipped vehicle to disseminate the received information. It enables control of multiclass information flow propagation through two parameters: 1) the number of communication servers and 2) the communication service rate. A two-layer model is derived to characterize the IFPW under the designed queuing strategy. Analytical and numerical solutions are derived to investigate the effects of the two control parameters on information propagation performance in different information classes. </p> <p>Third, this dissertation also develops a real-time implementable cooperative control mechanism for CAV platoons. Recently, model predictive control (MPC)-based platooning strategies have been developed for CAVs to enhance traffic performance by enabling cooperation among vehicles in the platoon. However, they are not deployable in practice as they require anembedded optimal control problem to be solved instantaneously, with platoon size and prediction horizon duration compounding the intractability. Ignoring the computational requirements leads to control delays that can deteriorate platoon performance and cause collisions between vehicles. To address this critical gap, this dissertation first proposes an idealized MPC-based cooperative control strategy for CAV platooning based on the strong assumption that the problem can be solved instantaneously. It then develops a deployable model predictive control with first-order approximation (DMPC-FOA) that can accurately estimate the optimal control decisions of the idealized MPC strategy without entailing control delay. Application of the DMPC-FOA approach for a CAV platoon using real-world leading vehicle trajectory data shows that it can dampen the traffic oscillation effectively, and can lead to smooth deceleration and acceleration behavior of all following vehicles.</p> <p>Finally, this dissertation also develops a multiclass traffic assignment model for mixed traffic flow of CAVs and HDVs. Due to the advantages of CAVs over HDVs, such as reduced value of time, enhanced quality of travel experience, and seamless situational awareness and connectivity, CAV users can differ in their route choice behavior compared to HDV users, leading to mixed traffic flows that can significantly deviate from the single-class HDV traffic pattern. However, due to a lack of quantitative models, there is limited knowledge on the evolution of mixed traffic flows in a traffic network. To partly bridge this gap, this dissertation proposes a multiclass traffic assignment model. The multiclass model captures the effect of knowledge level of traffic conditions on route choice of both CAVs and HDVs. In addition, it captures the characteristics of mixed traffic flow such as the difference in value of time between HDVs and CAVs and the asymmetry in their driving interactions, thereby enhancing behavioral realism in the modeling. New solution algorithms will be developed to solve the multiclass traffic assignment model. The study results can assist transportation decision-makers to design effective planning and operational strategies to leverage the advantages of CAVs and manage traffic congestion under mixed traffic flows.</p> <p>This dissertation deepens our understanding of the characteristics and phenomena in domains of traffic information dissemination, traffic flow dynamics and network equilibrium flow in the age of connected and autonomous transportation. The findings of this dissertation can assist transportation managers in designing effective traffic operation and planning strategies to fully exploit the potential of CAVs to improve system performance related to traffic safety, mobility and energy consumption. </p>

nÃo hà / Esta DissertaÃÃo trata de pesquisa sobre a anÃlise de sÃries temporais para previsÃo mensal do Imposto Sobre CirculaÃÃo e Mercadorias e PrestaÃÃo de ServiÃos â ICMS no estado do PiauÃ. Objetiva-se com essa pesquisa oferecer aos gestores do estado um modelo de previsÃo consistente e com bom poder preditivo, de forma a contribuir com a gestÃo financeira estadual. No trabalho, utilizaram-se os modelos ARIMA e FunÃÃo de TransferÃncia para realizar previsÃes, bem como o Modelo CombinaÃÃo de PrevisÃes. A dissertaÃÃo apresenta um diagnÃstico do ICMS no estado do Piauà e uma revisÃo da literatura onde sÃo abordados os principais aspectos teÃricos dos modelos utilizados no trabalho, bem como a anÃlise dos resultados empÃricos. Ao final, pode-se observar que os resultados obtidos na presente dissertaÃÃo, estÃo em sintonia com outros resultados obtidos em trabalhos semelhantes realizados sobre o tema, o que vem a confirmar a importÃncia dos modelos que utilizam a anÃlise de sÃries temporais como instrumento de prediÃÃo. / This dissertation deals with a research on the temporal series analysis for the monthly forecast of the turnover and services tax â ICMS in Brazil â in the state of PiauÃ. The aim of this research is to offer the statewide policymakers a consistent forecast and powerfully predictive model, so as to contribute to the state finance management. In this work, the ARIMA and Assignment Function models were used to carry out forecasts, as well as Forecast Combination. The dissertation presents a diagnosis of the ICMS in the state of PiauÃ, a review on the literature where the main theoretical aspects of the models carried out in the work are addressed, in addition to the empirical findings analysis. As a conclusion, it can be observed that the findings carried out in this dissertation are in harmony with other results of similar works carried out on the theme, which corroborates the importance of the models using the temporal series analysis as a forecasting instrument.

The Deliberate Speed of the Tar Heel State offers readers an examination of the efforts undertaken by North Carolina in hope of resisting public school desegregation between the Brown v. Board decisions of 1954, 1955, and 1966. It will examine the state’s use of a series of legal, legislative maneuvers, The Pupil Assignment Act of 1955 and the Pearsall Plan of 1956, which attempted to show definitive progress to the federal government while simultaneously ensuring the segregated public school system remained intact. By examining the efforts of individuals such as William Umstead, Luther Hodges, Terry Sanford, Thomas Pearsall, and others, this thesis will analyze how North Carolina attempted to use more “moderate” means of resisting federally mandated school desegregation and whether the state was successful in their efforts.

Workers whose credentials and skills do not meet or exceed the required competencies for their jobs have been of interest to scholars investigating the transition from school to jobs. To understand how such mismatch arises in the transitional period, some scholars emphasize that the labor market cannot keep up with the pace of educational expansion. Thus, many highly educated workers do not find jobs that fit their schooling and skill level. Others locate the source of mismatch in the inability of education to produce enough workers with the desired skill levels in the labor market. By focusing on this mismatch, this dissertation aims to provide a better understanding of the relationship between education and work. In particular, this study examines data covering the past two decades to see how the number of workers with skill and educational mismatch has changed and how educational expansion and transformations in the labor market have contributed to the change. The results indicate that workers with such mismatch have generally increased over the past two decades, but educational expansion has minimally contributed to this change. Rather, it is more likely caused by business cycles or job characteristics. The study also explores how the practices applied to select suitable workers in the hiring process affects workers’ job matching. This study suggests that workers are classified into various types depending on strategies by which employers use to determine workers’ degree of fit. Subsequently, their earnings and job satisfaction vary according to workers’ membership in these types of groups.

This dissertation explores workforce planning in manufacturing and healthcare systems. In manufacturing systems, the existing workforce planning models often lack fidelity with respect to the mechanism of learning. Learning refers to that employees’ productivity increases as they gain more experience. Workforce scheduling in the short term has a longer term impact on organizations’ capacity. The mathematical representations of learning are usually nonlinear. This nonlinearity complicates the planning models and provides opportunities to develop solution methodologies for realistically-sized instances. This research formulates the workforce planning problem as a mixed-integer nonlinear program (MINLP) and overcomes the limitations of cur- rent solution methods. Specifically, this research develops a reformulation technique that converts the MINLP to a mixed integer linear program (MILP) and proposes several techniques to speed up the solution time of solving the MILP. In organizations that use group work, workers learn not only by individual learning but also from knowledge transferred from team members. Managers face the decision of how to pair or team workers such that organizations benefit from this transfer of learning. Using a mathematical representation that incorporates both in- dividual learning and knowledge transfer between workers, this research considers the problem of grouping workers to teams and assigning teams to sets of jobs based on workers’ learning and knowledge transfer characteristics. This study builds a Mixed- integer nonlinear programs (MINP) for parallel systems with the objective of maximizing the system throughput and propose exact and heuristic solution approaches for solving the MINLP. In healthcare systems, we focus on managing medical technicians in medical laboratories, in particular, the phlebotomists. Phlebotomists draw specimens from patients based on doctors’ orders, which arrive randomly in a day. According to the literature, optimizing scheduling and routing in hospital laboratories has not been regarded as a necessity for laboratory management. This study is motivated by a real case at University of Iowa Hospital and Clinics, where there is a team of phlebotomists that cannot fulfill doctors requests in the morning shift. The goal of this research is routing these phlebotomists to patient units such that as many orders as possible are fulfilled during the shift. The problem is a team orienteering problem with stochastic rewards and service times. This research develops an a priori approach which applies a variable neighborhood search heuristic algorithm that improves the daily performance compared to the hospital practice.

Heterogeneous Computing (HC) systems achieve high performance by networking together computing resources of diverse nature. The issues of task assignment and scheduling are critical in the design and performance of such systems. In this thesis, an auction based game theoretic framework is developed for dynamic task scheduling in HC systems. Based on the proposed game theoretic model, a new dynamic scheduling algorithm is developed that uses auction based strategies. The dynamic scheduling algorithm yields schedules with shorter completion times than static schedulers while incurring higher scheduling overhead. Thus, a second scheduling algorithm is proposed which uses an initial schedule generated with a learning automaton based algorithm, and then heuristics are used to identify windows of tasks within the application that can be rescheduled dynamically during run time.

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