Dissertations / Theses on the topic 'Operations research-Models'

Individuals who screen research grant applications often select candidates on the basis of a few key parameters; success or failure can be reduced to a series of peer-reviewed Likert scores on as little as four criteria: risk, relevance, return, and reasonableness. Despite the vital impact these assessments have upon the sponsors, researchers, and society in general as a benefactor of the research, there is little empirical research into the peer-review process. The purpose of this study was to investigate how reviewers evaluate reasonableness and how the process can be modeled in a decision support system. The research questions both address the relationship between an individual's estimates of reasonableness and the indicators of scope, resources, cost, and schedule as well as evaluate the performance of several cognitive models as predictors of reasonableness. Building upon Brunswik's theory of probabilistic functionalism, a survey methodology was used to implement a policy-capturing exercise that yielded a quantitative baseline of reasonableness estimates. The subsequent data analysis addressed the predictive performance of six cognitive models as measured by the mean-square-deviation between the models and the data. A novel mapping approach developed by von Helversen and Rieskamp, a fuzzy logic model, and an exemplar model were found to outperform classic linear regression. A neural network model and the QuickEst heuristic model did not perform as well as linear regression. This information can be used in a decision support system to improve the reliability and validity of future research assessments. The positive social impact of this work would be more efficient allocation and prioritization of increasingly scarce research funds in areas of science such as social, psychological, medical, pharmaceutical, and engineering.

This thesis considers the development of an operational management procedure (OMP) to provide scientific recommendations for commercial TAC for the South African west coast rock lobster (Jasus lalandii) fishery. This fishery has been under considerable stress in recent years as a result of overfishing and low somatic growth rates. Present catch levels, less than 2000 MT, are substantially smaller than levels recorded in the past. The present biomass (above 75mm carapace length) is estimated to be only six percent of the pristine level. At the start of this research, no long-term management strategy for the resource existed. Neither was there any robust, tested, scientific method available for setting the annual TAC for the fishery, which resulted in a time-consuming and unsatisfactory scientific debate each year in developing a series of ad hoc TAC recommendations. The work presented in this thesis is thus aimed at answering two important questions. i) Can an adequate mathematical model be developed as a basis to simulate the resource and its associated fishery? ii) Can a self-correcting robust OMP be developed for the resource? The first phase of this thesis is the development of a size-structured population model of the resource and the associated fishery. A size-structured model is necessary as lobsters are difficult to age and hence most of the data collected are on a size basis. Furthermore, important management issues, such as the legal minimum size which has changed over time, require a model able to take size-structure into account. This model is fitted to a wide range of data from the fishery, including CPUE (catch-per-unit-effort) and catch-at-size information, by maximising a likelihood function. The model is shown to fit reasonably well to all data, and to provide biologically plausible estimates for its six estimable parameters.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2006.
Thesis Advisor(s): Ronald D. Fricker. "September 2006." Includes bibliographical references (p. 49-50). Also available in print.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2005.
Thesis Advisor(s): Gerald G. Brown, Curtis L. Blais. Includes bibliographical references (p. 113-117). Also available online.

The U.S. Army distributes its 51,000 competitive category officers among manning targets specified by location, rank and skill that change over time in response to changing requirements. The officer inventory also changes over time and does not exactly match the manning target requirements. The Army responds to imbalances by redistributing officers in order to provide each location with the minimum required officers while minimizing the number of unfilled targets and excess officers at each location. This thesis focuses on branch officers, branch targets and generalist targets with ranks from Branch Qualified Captain to Colonel. Using data provided by the Army, we formulate an integer programming model called DISTRIBUTOR. When DISTRIBUTOR allows all officers in the inventory to move, it finds only 340 unfilled targets but this requires 4,688 or 28% of the inventory to move. We reduce the number of moves by using DISTRIBUTOR in two sequential steps. The first step optimally distributes officers at each location and identifies the excess officers and unfilled targets at each location. The second step takes the excess officers and distributes them to unfilled targets at other locations. The two-step leaves only 346 targets unfilled (6 more) but requires only 1,373 or 8% of the inventory to move. By allowing rank substitution DISTRIBUTOR can reduce the unfilled targets to 70.

With the advent of easily accessible, deployable, and usable 802.11 technology, users can connect and network with practically any infrastructure that exists today. Due to that simplicity and ease of use, it only seems logical that the military and tactical users should also employ these technologies. The questions regarding 802.11 network performances in a hostile and signal-unfriendly environment (i.e., high temperature and high humidity) have yet to be answered. The goal of this thesis is to quantify 802.11 network capabilities, in terms of throughput, while it is employed in those areas. Ultimately, the objective is to produce statistical models able to represent any variations in the 802.11 signals and network due to those environmental factors.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2009.
Thesis Advisor(s): Otte, Douglas E. "September 2009." Description based on title screen as viewed on November 10, 2009. Author(s) subject terms: Littoral Combat Ship, Steregushchiy, Iran, Strait of Hormuz, Hughes' Salvo Equations, Harpoon3 Advanced Naval Warfare. Includes bibliographical references (p. 95-96). Also available in print.

Approved for public release; distribution is unlimited
Senior officers in the United States Army have a high degree of confidence that National Training Center simulated combat results are representative, under similar circumstances, of actual combat. A validation methodology for high resolution combat models, primarily based on data acquired from the National Training Center, is the focus of this thesis. The validation methodology, where appropriate, translates confidence in National Training Center realism, to confidence in the combat model. Theoretical issues, existing methodologies, and the impact of model purpose are considered in this research. The final product is a validation methodology that makes use of a realistic representation of combat, automatically updates validation criteria to account for changes in weapons and tactics, and is responsive to the purpose for which the model was designed.
http://archive.org/details/methodologyforva00covi
Captain, United States Army

Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2006.
Thesis Advisor(s): Robert A. Koyak. "September 2006." Includes bibliographical references (p. 51). Also available in print.

Thesis (Ph.D. (Statistics)) -- University of Limpopo, 2017
Operations Research (OR) is a scientific method for developing quantitatively well-grounded recommendations for decision making. While it is true that it uses a variety of mathematical techniques, OR has a much broader scope. It is in fact a systematic approach to solving problems, which uses one or more analytical tools in the process of analysis. Over the years, OR has evolved through different stages. This study is motivated by new real-world challenges needed for efficiency and innovation in line with the aims and objectives of OR – the science of better, as classified by the OR Society of the United Kingdom. New real-world challenges are encountered on a daily basis from problems arising in the fields of water, energy, agriculture, mining, tourism, IT development, natural phenomena, transport, climate change, economic and other societal requirements. To counter all these challenges, new techniques ought to be developed. The growth of global markets and the resulting increase in competition have highlighted the need for OR techniques to be improved. These developments, among other reasons, are an indication that new techniques are needed to improve the day-to-day running of organisations, regardless of size, type and location. The principal aim of this study is to modify and develop new OR techniques that can be used to solve emerging problems encountered in the areas of linear programming, integer programming, mixed integer programming, network routing and travelling salesman problems. Distribution models, resource allocation models, travelling salesman problem, general linear mixed integer ii programming and other network problems that occur in real life, have been modelled mathematically in this thesis. Most of these models belong to the NP-hard (non-deterministic polynomial) class of difficult problems. In other words, these types of problems cannot be solved in polynomial time (P). No general purpose algorithm for these problems is known. The thesis is divided into two major areas namely: (1) network models and (2) resource allocation and distribution models. Under network models, five new techniques have been developed: the minimum weight algorithm for a non-directed network, maximum reliability route in both non-directed and directed acyclic network, minimum spanning tree with index less than two, routing through 0k0 specified nodes, and a new heuristic to the travelling salesman problem. Under the resource allocation and distribution models section, four new models have been developed, and these are: a unified approach to solve transportation and assignment problems, a transportation branch and bound algorithm for the generalised assignment problem, a new hybrid search method over the extreme points for solving a large-scale LP model with non-negative coefficients, and a heuristic for a mixed integer program using the characteristic equation approach. In most of the nine approaches developed in the thesis, efforts were done to compare the effectiveness of the new approaches to existing techniques. Improvements in the new techniques in solving problems were noted. However, it was difficult to compare some of the new techniques to the existing ones because computational packages of the new techniques need to be developed first. This aspect will be subject matter of future research on developing these techniques further. It was concluded with strong evidence, that development of new OR techniques is a must if we are to encounter the emerging problems faced by the world today. Key words: NP-hard problem, Network models, Reliability, Heuristic, Largescale LP, Characteristic equation, Algorithm.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1993.
Vita. Abstract. Attached pocket for diagrams. Includes bibliographical references (leaves 155-162). Also available via the Internet.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2006.
Thesis Advisor(s): Moshe Kress, Roberto Szechtman. "September 2006." Includes bibliographical references (p. 57-59). Also available in print.

In the first part of this thesis, we study revenue management in resource exchange alliances. We first show that without an alliance the sellers will tend to price their products too high and sell too little, thereby foregoing potential profit, especially when capacity is large. This provides an economic motivation for interest in alliances, because the hope may be that some of the foregone profit may be captured under an alliance. We then consider a resource exchange alliance, including the effect of the alliance on competition among alliance members. We show that the foregone profit may indeed be captured under such an alliance. The problem of determining the optimal amounts of resources to exchange is formulated as a stochastic mathematical program with equilibrium constraints. We demonstrate how to determine whether there exists a unique equilibrium after resource exchange, how to compute the equilibrium, and how to compute the optimal resource exchange. In the second part of this thesis, we study the estimation of risk measures in risk management. In the financial industry, sell-side analysts periodically publish recommendations of underlying securities with target prices. However, this type of analysis does not provide risk measures associated with underlying companies. In this study, we discuss linear regression approaches to the estimation of law invariant conditional risk measures. Two estimation procedures are considered and compared; one is based on residual analysis of the standard least squares method and the other is in the spirit of the M-estimation approach used in robust statistics. In particular, Value-at-Risk and Average Value-at-Risk measures are discussed in detail. Large sample statistical inference of the estimators is derived. Furthermore, finite sample properties of the proposed estimators are investigated and compared with theoretical derivations in an extensive Monte Carlo study. Empirical results on the real data (different financial asset classes) are also provided to illustrate the performance of the estimators.

Tesis por compendio
[ES] Algunos productos se caracterizan por su falta de homogeneidad, lo que significa que productos con diferentes características pueden ser obtenidos de un mismo proceso de producción debido a factores incontrolables como la naturaleza de las materias primas o las condiciones ambientales durante la producción. Hay cuatro aspectos que caracterizan la falta de homogeneidad en el producto: los subtipos homogéneos que se obtienen de un mismo lote de producción, la cantidad de productos que componen cada subtipo, el valor de cada uno de los subtipos, y el estado de los productos. La falta de homogeneidad en el producto dificulta la gestión de los procesos de las empresas y cadenas de suministro en el momento en el que los clientes requieren homogeneidad entre las unidades de producto que adquieren. Un ejemplo de esto se produce en el sector de la cerámica, en el que los clientes requieren que todas las unidades que van a ser ensambladas juntas tengan el mismo color, espesor y calidad por razones estéticas y de seguridad. Otro ejemplo es el extraído del sector agroalimentario, en el que el mercado final requiere productos que cumplan con un tamaño mínimo, un color particular, o sabor en el caso de las frutas. Además, el sector agroalimentario tiene la complejidad añadida producida por el deterioro de los productos a lo largo del tiempo, y la necesidad de los mercados de ofrecer a los clientes productos con una mínima duración tras su venta. En esta Tesis, se define como productos heterogéneos a aquellos productos que se pueden clasificar en subtipos homogéneos con una cantidad variable, mientras que los productos perecederos son aquellos que, además de ser heterogéneos, tienen falta de homogeneidad en su estado. De acuerdo con estos conceptos, el sector cerámico comercializa productos heterogéneos mientras que el sector agroalimentario comercializa productos perecederos. Esta Tesis propone marcos conceptuales y modelos de Investigación Operativa que soporten la gestión de cadenas de suministro con productos heterogéneos y perecederos en la toma de decisiones centralizada y distribuidas relacionadas con los niveles de decisión estratégica, táctica y operativa. El objetivo es mejorar la competitividad, sostenibilidad y flexibilidad de la cadena de suministro para adaptarse a los requerimientos del mercado bajo condiciones de incertidumbre. Para esto, se han propuesto modelos de Investigación Operativa deterministas e inciertos, cuyos resultados se comparan concluyendo que los resultados obtenidos con los modelos inciertos se adaptan mejor al comportamiento real de las cadenas de suministros.Los modelos de Investigación Operativa propuestos han contribuido a tres áreas de investigación: problemas operativos en el sector cerámico, problemas estratégicos en el sector agroalimentario y problemas de planificación en el sector agroalimentario. Las principales novedades en los problemas operativos en el sector cerámico son el modelado de las características de las baldosas cerámicas, la consideración de los requerimientos de homogeneidad entre unidades de diferentes líneas de pedido, y la posibilidad de realizar entregas parciales y entregas con retraso. Esta Tesis contribuye a los problemas estratégicos en el sector agroalimentario al diseñar una cadena de suministro completa de productos agroalimentarios frescos considerando el aspecto perecedero de los productos e integrando decisiones tácticas, y determinando el impacto real que tiene considerar el aspecto perecedero de los productos durante el diseño de la cadena de suministro ...
[CA] Alguns productes es caracteritzen per la seua falta d'homogeneïtat, el que significa que productes amb diferents característiques poden ser obtinguts d'un mateix procés de producció degut a factors incontrolables com la naturalesa de les matèries primeres o les condicions ambientals durant la producció. Hi ha quatre aspectes que caracteritzen la falta d'homogeneïtat en el producte: els subtipus homogenis que s'obtenen d'un mateix lot de producció, la quantitat de productes que componen cada subtipus, el valor de cada un dels subtipus, i l'estat dels productes. La falta d'homogeneïtat en el producte dificulta la gestió dels processos de les empreses i cadenes de subministrament en el moment en què els clients requerixen homogeneïtat entre les unitats de producte que adquirixen. Un exemple d'açò es produïx en el sector de la ceràmica, en el que els clients requerixen que totes les unitats que seran acoblades juntes tinguen el mateix color, grossària i qualitat per raons estètiques i de seguretat. Un altre exemple és l'extret del sector agroalimentari, en el que el mercat final requerix productes que complisquen amb una grandària mínima, un color particular, o sabor en el cas de les fruites. A més, el sector agroalimentari té la complexitat afegida produïda pel deteriorament dels productes al llarg del temps, i la necessitat dels mercats d'oferir als clients productes amb una mínima duració després de la seua venda. En aquesta Tesi, es definix com a productes heterogenis a aquells productes que es poden classificar en subtipus homogenis amb una quantitat variable, mentres que els productes peribles són aquells que, a més de ser heterogenis, tenen falta d'homogeneïtat en el seu estat. D'acord amb aquests conceptes, el sector ceràmic comercialitza productes heterogenis mentres que el sector agroalimentari comercialitza productes peribles. Aquesta Tesi proposa marcs conceptuals i models d'Investigació Operativa que suporten la gestió de cadenes de subministrament amb productes heterogenis i peribles en la presa de decisions centralitzada i distribuïdes relacionades amb els nivells de decisió estratègica, tàctica i operativa. L'objectiu és millorar la competitivitat, sostenibilitat i flexibilitat de la cadena de subministrament per adaptar-se als requeriments del mercat sota condicions d'incertesa. Per a açò, s'han proposat models d'Investigació Operativa deterministes i incerts, els resultats es comparen concloent que els resultats obtinguts amb els models incerts s'adapten millor al comportament real de les cadenes de subministraments. Els models d'Investigació Operativa proposats han contribuït a tres àrees d'investigació: problemes operatius en el sector ceràmic, problemes estratègics en el sector agroalimentari i problemes de planificació en el sector agroalimentari. Les principals novetats en els problemes operatius en el sector ceràmic són el modelatge de les característiques de les rajoles ceràmiques, la consideració dels requeriments d'homogeneïtat entre unitats de diferents línies de comanda, i la possibilitat de realitzar lliuraments parcials i lliuraments amb retard. Aquesta Tesi contribueix als problemes estratègics en el sector agroalimentari al dissenyar una cadena de subministrament completa de productes agroalimentaris frescos considerant l'aspecte perible dels productes, integrant decisions tàctiques, i determinant l'impacte real que té considerar l'aspecte perible dels productes durant el disseny de la cadena de subministrament ...
[EN] Some products are characterised by their lack of homogeneity, what means that products with different characteristics can be obtained from the same production process due to uncontrollable factors such as the nature of raw materials or the environmental conditions during production. There are four aspects that characterize the lack of homogeneity in the product: the homogeneous subtypes to be obtained from a production lot, the quantity of products that belong to each subtype, the value related to each of the subtypes and the state of the products. The lack of homogeneity in the product hinders the management of the supply chain or company's processes at the time customers require the homogeneity among the acquired units of product. An example of this is produced in the ceramic tile sector, in which customers need all acquired ceramic tiles that are going to be jointly assembled to have the same colour, thickness and quality for aesthetic and safety reasons. Another example is the extracted from the agri-food sector, in which final markets require products that meet some characteristics such as a minimum size, a particular colour or flavour in the case of fruits. In addition, the agri-food sector has the added complexity produced by the deterioration of products over time, and the need of markets to offer to end consumers products with a minimum durability after sale. In this Thesis, heterogeneous products are defined as products for which different subtypes can be obtained in a variable quantity while perishable products are those that, apart from being heterogeneous, have a lack of homogeneity in their state. According to these concepts, ceramic sectors would commercialize heterogeneous products while the agri-food sector would do so with perishable products. This Thesis proposes conceptual frameworks and Operations Research models to support the management of supply chains with heterogeneous and perishable products in centralized and distributed decision-making processes related to strategic, tactical and operative decisional levels. The objective is to improve the supply chain competitiveness, sustainability and flexibility to adapt to market requirements under uncertain conditions. For this, both deterministic and uncertain Operations Research models have been proposed, whose results are compared concluding that results obtained with uncertain models better fit with the behaviour of real supply chains. The proposed Operations Research models have contributed to three research areas: operational problems in the ceramic sector, strategic problems in the agri-food sector and planning problems in the agri-food sector. Main novelties in the ceramic operational problems are the modelling of the characteristics of ceramic tile products, the consideration of homogeneity requirements between units from different order lines, and the possibility of making partial deliveries and delayed deliveries. This Thesis contributes to strategic problems in agri-food products by designing an entire fresh agri-food supply chain considering the perishability of products and integrating tactical decisions, and by determining the real impact that considering the products' perishability has on the supply chain design process ...
This Thesis has been developed in the Research Centre of Management and Production Engineering (CIGIP, for its acronym in Spanish “Centro de Investigación en Gestión e Ingeniería de Producción”) of the Universitat Politècnica de València with the support of the predoctoral grant Programme of Formation of University Professors (FPU, for its acronym in Spanish “Formación de Profesorado Universitario”) from the Spanish Ministry of Science, Innovation and Universities (Ref. FPU15/03595). The supervisors of this Thesis are Dr. Angel Ortiz, and Dra. María del Mar Alemany Diaz that are Professors in the Research Centre of Management and Production Engineering (CIGIP) of the Universitat Politècnica de València. The FPU grant has been endorsed by the supervisor Dr. Ángel Ortiz. This Thesis has also been supported by the project ‘RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems’ (Ref. 691249) funded by the EU under its funding scheme H2020-MSCA-RISE-2015, the project ‘Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity’ (PLANGES-FHP) (Ref. DPI2011- 23597) funded by the Spanish Ministry of Economy and Competitiveness. The projects RUC-APS and PLANGES-FHP have been led by the one of the supervisors of this Thesis Dr. María del Mar Eva Alemany. In order to obtain the international mention for this Thesis, three months of research stages have been made in the research agency Agenzia Lucana di Sviluppo e di Innovazione in Agricoltora, located in Metaponto (Italy)
Esteso Álvarez, A. (2020). Operations research models for the management of supply chains of perishable and heterogeneous products in uncertain contexts. Application to the agri-food and ceramic sectors [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/141099
TESIS
Compendio

The objective of this research is to establish consistency thresholds linked to alpha (α) levels for HDM’s (Hierarchical Decision Model) judgment quantification method. Measuring consistency in order to control it is a crucial and inseparable part of any AHP/HDM experiment. The researchers on the subject recommend establishing thresholds that are statistically based on hypothesis testing, and are linked to the number of decision variables and (α) level. Such thresholds provide the means with which to evaluate the soundness and validity of an AHP/HDM decision. The linkage of thresholds to (α) levels allows the decision makers to set an appropriate inconsistency tolerance compatible with the situation at hand. The measurements of judgments are unreliable in the absence of an inconsistency measure that includes acceptable limits. All of this is essential to the credibility of the entire decision making process and hence is extremely useful for practitioners and researchers alike. This research includes distribution fitting for the inconsistencies. It is a valuable and interesting part of the research results and adds usefulness, practicality and insight. The superb fits obtained give confidence that all the statistical inferences based on the fitted distributions accurately reflect the HDM’s inconsistency measure.

The aim of the research project that is the subject of this thesis is to apply mathematical techniques, especially those in the area of operations research, to the problem of maritime minefield transit. We develop several minefield models applicable to different aspects of the minefield problem. These include optimal mine clearance, shortest time traversal and time constrained traversal. We hope the suite of models and tools developed will help make mine field clearance and traversal both safer and more efficient and that exposition of the models will bring a clearer understanding of the mine problem from a mathematical perspective. In developing the solutions to mine field models, extensive use is made of network path planning algorithms, particularly the Weight Constrained Shortest Path Problem (WCSPP) for which the current state-of-the-art algorithm is extended. This is done by closer integration of Lagrangean relaxation and preprocessing to reduce the size of the network. This is then integrated with gap-closing algorithms based on enumeration to provide optimal or near optimal solutions to the path planning problem. We provide extensive computational evidence on the performance of our algorithm and compare it to other algorithms found in the literature. This tool then became fundamental in solving various separate minefield models. Our models can be broadly separated into obstacle models in which mine affected regions are treated as obstacles to be avoided and continuous threat in which each point of space has an associated risk. In the later case, we wish to find a path that minimizes the integral of the risk along the path while constraining the length of the path. We call this the Continuous Euclidean Length Constrained Minimum Cost Path Problem (C-LCMCPP), for which we present a novel network approach to solving this continuous problem. This approach results in being able to calculate a global lower bound on a non-convex optimization problem.

Fractional aircraft ownership programs offer companies and individuals all the benefits of owning private jet, such as safety, consistency, and guaranteed availability, at a fraction of the cost of owning an aircraft. In the fractional ownership model, the partial owners of an aircraft are entitled to certain number of hours per year, and the management company is responsible for all the operational considerations and making sure an aircraft is available to the owners at the requested time and location. This thesis research proposes advance optimization techniques to help the management company to optimally operate its available resources and provides tools for strategic decision making. The contributions of this thesis are: (i) The development of optimization methodologies to assign and schedule aircraft and crews so that all flight requests are covered at the lowest possible cost. First, a simple model is developed to solve the crew pairing and aircraft routing problem with column generation assuming that a crew stays with one specific aircraft during its duty period. Secondly, this assumption is partially relaxed to improve resource utilization by revising the simple model to allow a crew to use another aircraft when its original aircraft goes under long maintenance. Thirdly, a new comprehensive model utilizing Benders decomposition technique and a fleet-station time line is proposed to completely relax the assumption that crew stays with one specific aircraft. It combines the fleet assignment, aircraft routing, and crew pairing problems. In the proposed methodologies, real world details are taken into consideration, such as crew transportation and overtime costs, scheduled and unscheduled maintenance effects, crew rules, and the presence of non-crew-compatible fleets. Scheduling with time windows is also discussed. (ii) The analysis of operational strategies to provide decision making support. Scenario analyses are performed to provide insights on improving business profitability and aircraft availability, such as impact of aircraft maintenance, crew swapping, effect of increasing demand by Jet-card and geographical business expansion, size of company owned aircraft, and strategies to deal with the stochastic feature of unscheduled maintenance and demand.

In the past two decades, retailers have witnessed rapid changes in markets due to an increase in competition, the rise of e-commerce, and ever-changing consumer behavior. As a result, retailers have become increasingly aware of the need to better coordinate inventory control with pricing in order to maximize their profitability. This dissertation was motivated by two of such problems facing retailers at the interface between pricing and inventory control. One considers inventory control decisions for settings in which planned prices fluctuate over time, and the other considers pricing of multiple substitutable products for settings in which customers hold inventory as a consequence of stockpiling when promotional prices are offered.

In Chapter 1, we provide a brief motivation for each problem. In Chapter 2, we consider optimization of procurement and inventory allocation decisions by a retailer that sells a product with a long production lead time and a short selling season. The retailer orders most products months before the selling season, and places only one order for each product due to short product life cycles and long delivery lead times. Goods are initially stored at the warehouse and then sent to stores over the course of the season. The stores are in high-rent locations, necessitating efficient use of space, so there is no backroom space and it is uneconomical to send goods back to the warehouse; thus, all inventory at each store is available for sale. Due to marketing and logistics considerations, the planned trajectory of prices is determined in advance and may be non-monotonic. Demand is stochastic and price-dependent, and independent across time periods. We begin our analysis with the case of a single store. We first formulate the inventory allocation problem given a fixed initial order quantity with the objective of maximizing expected profit as a dynamic program and explain both technical and computational challenges in identifying the optimal policy. We then present two variants of a heuristic based on the notion of equalizing the marginal value of inventory across the time periods. Results from a numerical study indicate that the more sophisticated variant of the heuristic performs well when compared with both an upper bound and an industry benchmark, and even the simpler variant performs fairly well for realistic settings. We then generalize our approaches to the case of multiple stores, where we allow the stores to have different price trajectories. Our numerical results suggest that the performance of both heuristics is still robust in the multiple store setting, and does not suffer from the same performance deterioration observed for the industry benchmark as the number of stores increases or as price differences increase across stores and time periods. For the pre-season procurement problem, we develop a heuristic based on a generalization of the newsvendor problem that accounts for the two-tiered salvage values in our setting, specifically, a low price during end-of-season markdown periods and a very low or zero salvage value after the season has concluded. Results for numerical examples indicate that our modified newsvendor heuristic provides solutions that are as good as those obtained via grid search.

In Chapter 3, we address a retailer's problem of setting prices, including promotional prices, over a multi-period horizon for multiple substitutable products in the same product category. We consider the problem in a setting in which customers anticipate the retailer's pricing strategy and the retailer anticipates the customers' purchasing decisions. We formulate the problem as a two-stage game in which the profit maximizing retailer chooses prices and the utility maximizing customers respond by making explicit decisions regarding purchasing and consumption, and thus also implicit decisions regarding stockpiling. We incorporate a fairly general reference price formation process that allows for cross-product effects of prices on reference prices. We initially focus on a single customer segment. The representative customer's utility function accounts for the value of consumption of the products, psychological benefit (for deal-seekers) from purchasing at a price below his/her reference price but with diminishing marginal returns, costs of purchases, penalties for both shortages and holding inventory, and disutility for deviating from a consumption target in each period (where applicable). We are the first to develop a model that simultaneously accounts for this combination of realistic factors for the customer, and we also separate the customer's purchasing and consumption decisions. We develop a methodology for solving the customer's problem for arbitrary price trajectories based on a linear quadratic control formulation of an approximation of the customer's utility maximization problem. We derive analytical representations for the customer's optimal decisions as simple linear functions of prices, reference prices, inventory levels (as state variables), and the cumulative aggregate consumption level (as a state variable). (Abstract shortened by ProQuest.)

Thesis (Ph. D.)--Massachusetts Institute of Technology, Operations Research Center, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 129-132).
Over 90% of the data available across the world has been produced over the last two years, and the trend is increasing. It has therefore become paramount to develop algorithms which are able to scale to very high dimensions. In this thesis we are interested in showing how we can use structural properties of a given problem to come up with models applicable in practice, while keeping most of the value of a large data set. Our first application provides a provably near-optimal pricing strategy under large-scale competition, and our second focuses on capturing the interactions between extreme weather and damage to the power grid from large historical logs. The first part of this thesis is focused on modeling competition in Revenue Management (RM) problems. RM is used extensively across a swathe of industries, ranging from airlines to the hospitality industry to retail, and the internet has, by reducing search costs for customers, potentially added a new challenge to the design and practice of RM strategies: accounting for competition. This work considers a novel approach to dynamic pricing in the face of competition that is intuitive, tractable and leads to asymptotically optimal equilibria. We also provide empirical support for the notion of equilibrium we posit. The second part of this thesis was done in collaboration with a utility company in the North East of the United States. In recent years, there has been a number of powerful storms that led to extensive power outages. We provide a unified framework to help power companies reduce the duration of such outages. We first train a data driven model to predict the extent and location of damage from weather forecasts. This information is then used in a robust optimization model to optimally dispatch repair crews ahead of time. Finally, we build an algorithm that uses incoming customer calls to compute the likelihood of damage at any point in the electrical network.
by Matthieu Monsch.
Ph.D.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2000.
Also available online at the DSpace at MIT website.
Includes bibliographical references (p. 119-122).
Organizations perform work essentially through operations and projects. The characteristics of projects makes them extremely difficult to manage: their non repetitive nature discards the trial and error learning, while their short life span is particularly unforgiving to misjudgments. Some authors have found that effective scheduling is an important contributor to the success of research and development (R&D), as well as construction projects. The widely used critical path method for scheduling projects and identifying important activities fails to capture two important dimensions of the problem: the availability of different technologies (or options) to perform the activities, and the inherent problem of limited availability of resources that most managers face. Nevertheless, when one tries to account for such additional constraints, the problems become very hard to solve. In this thesis we propose an approach to the scheduling problem using a genetic algorithm, and try to compare its performance to more traditional approaches, such as an extension to a very innovative Lagrangian relaxation approach recently proposed. The purpose of using genetic algorithms is twofold: first to obtain good approximations to very hard problems, and second to realize the limitations and virtues of this search technique. The purpose of this thesis is not only to develop the algorithms, but also to obtain insight about the implications of the additional constraints in the perspective of a project manager.
by Eduardo B. Messmacher.
S.M.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
117
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 173-180).
The last decade has seen an explosion in the availability of data. In this thesis, we propose new techniques to leverage these data to tractably model uncertainty and behavior. Specifically, this thesis consists of three parts: In the first part, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using hypothesis testing. The approach is flexible and widely applicable, and robust optimization problems built from our new data driven sets are computationally tractable, both theoretically and practically. Optimal solutions to these problems enjoy a strong, finite-sample probabilistic guarantee. Computational evidence from classical applications of robust optimization { queuing and portfolio management { confirm that our new data-driven sets significantly outperform traditional robust optimization techniques whenever data is available. In the second part, we examine in detail an application of the above technique to the unit commitment problem. Unit commitment is a large-scale, multistage optimization problem under uncertainty that is critical to power system operations. Using real data from the New England market, we illustrate how our proposed data-driven uncertainty sets can be used to build high-fidelity models of the demand for electricity, and that the resulting large-scale, mixed-integer adaptive optimization problems can be solved efficiently. With respect to this second contribution, we propose new data-driven solution techniques for this class of problems inspired by ideas from machine learning. Extensive historical back-testing confirms that our proposed approach generates high quality solutions that compare with state-of-the-art methods. In the third part, we focus on behavioral modeling. Utility maximization (single agent case) and equilibrium modeling (multi-agent case) are by far the most common behavioral models in operations research. By combining ideas from inverse optimization with the theory of variational inequalities, we develop an efficient, data-driven technique for estimating the primitives of these models. Our approach supports both parametric and nonparametric estimation through kernel learning. We prove that our estimators enjoy a strong generalization guarantee even when the model is misspecified. Finally, we present computational evidence from applications in economics and transportation science illustrating the effectiveness of our approach and its scalability to large-scale instances.
by Vishal Gupta.
Ph. D.

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 163-176).
With the rising popularity of ride-sharing and alternative modes of transportation, there has been a renewed interest in transit planning to improve service quality and stem declining ridership. However, it often takes months of manual planning for operators to redesign and reschedule services in response to changing needs. To this end, we provide four models of transportation planning that are based on data and driven by optimization. A key aspect is the ability to provide certificates of optimality, while being practical in generating high-quality solutions in a short amount of time. We provide approaches to combinatorial problems in transit planning that scales up to city-sized networks. In transit network design, current tractable approaches only consider edges that exist, resulting in proposals that are closely tethered to the original network. We allow new transit links to be proposed and account for commuters transferring between different services. In integrated transit scheduling, we provide a way for transit providers to synchronize the timing of services in multimodal networks while ensuring regularity in the timetables of the individual services. This is made possible by taking the characteristics of transit demand patterns into account when designing tractable formulations. We also advance the state of the art in demand models for transportation optimization. In emergency medical services, we provide data-driven formulations that outperforms their probabilistic counterparts in ensuring coverage. This is achieved by replacing independence assumptions in probabilistic models and capturing the interactions of services in overlapping regions. In transit planning, we provide a unified framework that allows us to optimize frequencies and prices jointly in transit networks for minimizing total waiting time.
by Yee Sian Ng.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 251-257).
This thesis considers problems in two areas in the healthcare operations: Kidney Paired Donation (KPD) and scheduling medical residents in hospitals. In both areas, we explore the implications of policy change through high fidelity simulations. We then build stochastic models to provide strategic insight into how policy decisions affect the operations of these healthcare systems. KPD programs enable patients with living but incompatible donors (referred to as patient-donor pairs) to exchange kidneys with other such pairs in a centrally organized clearing house. Exchanges involving two or more pairs are performed by arranging the pairs in a cycle, where the donor from each pair gives to the patient from the next pair. Alternatively, a so called altruistic donor can be used to initiate a chain of transplants through many pairs, ending on a patient without a willing donor. In recent years, the use of chains has become pervasive in KPD, with chains now accounting for the majority of KPD transplants performed in the United States. A major focus of our work is to understand why long chains have become the dominant method of exchange in KPD, and how to best integrate their use into exchange programs. In particular, we are interested in policies that KPD programs use to determine which exchanges to perform, which we refer to as matching policies. First, we devise a new algorithm using integer programming to maximize the number of transplants performed on a fixed pool of patients, demonstrating that matching policies which must solve this problem are implementable. Second, we evaluate the long run implications of various matching policies, both through high fidelity simulations and analytic models. Most importantly, we find that: (1) using long chains results in more transplants and reduced waiting time, and (2) the policy of maximizing the number of transplants performed each day is as good as any batching policy. Our theoretical results are based on introducing a novel model of a dynamically evolving random graph. The analysis of this model uses classical techniques from Erdos-Renyi random graph theory as well as tools from queueing theory including Lyapunov functions and Little's Law. In the second half of this thesis, we consider the problem of how hospitals should design schedules for their medical residents. These schedules must have capacity to treat all incoming patients, provide quality care, and comply with regulations restricting shift lengths. In 2011, the Accreditation Council for Graduate Medical Education (ACGME) instituted a new set of regulations on duty hours that restrict shift lengths for medical residents. We consider two operational questions for hospitals in light of these new regulations: will there be sufficient staff to admit all incoming patients, and how will the continuity of patient care be affected, particularly in a first day of a patients hospital stay, when such continuity is critical? To address these questions, we built a discrete event simulation tool using historical data from a major academic hospital, and compared several policies relying on both long and short shifts. The simulation tool was used to inform staffing level decisions at the hospital, which was transitioning away from long shifts. Use of the tool led to the following strategic insights. We found that schedules based on shorter more frequent shifts actually led to a larger admitting capacity. At the same time, such schedules generally reduce the continuity of care by most metrics when the departments operate at normal loads. However, in departments which operate at the critical capacity regime, we found that even the continuity of care improved in some metrics for schedules based on shorter shifts, due to a reduction in the use of overtime doctors. We develop an analytically tractable queueing model to capture these insights. The analysis of this model requires analyzing the steady-state behavior of the fluid limit of a queueing system, and proving a so called "interchange of limits" result.
by Ross Michael Anderson.
Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 185-188).
Maintenance scheduling is an integral part of many complex systems. For instance, without effective maintenance scheduling, the combined effects of preventative and corrective maintenance can have severe impacts on the availability of those systems. Based on current Air Force trends including maintenance manpower, dispersed aircraft basing, and increased complexity, there has been a renewed focus on preventative maintenance. To address these concerns, this thesis develops two models for preventative maintenance scheduling for complex systems, the first of interest in the system concept development and design phase, and the second of interest during operations. Both models are highly complex and intractable to solve in their original forms. For the first model, we develop approximation algorithms that yield high quality and easily implementable solutions. To address the second model, we propose a decomposition strategy that produces submodels that can be solved via existing algorithms or via specialized algorithms we develop. While much of the literature has examined stochastically failing systems, preventative maintenance of usage limited systems has received less attention. Of particular interest is the design of modular systems whose components must be repaired/replaced to prevent a failure. By making cost tradeoffs early in development, program managers, designers, engineers, and test conductors can better balance the up front costs associated with system design and testing with the long term cost of maintenance. To facilitate such a tradeoff, the Modular Maintenance Scheduling Problem provides a framework for design teams to evaluate different design and operations concepts and then evaluate the long term costs. While the general Modular Maintenance Scheduling Problem does not require maintenance schedules with specific structure, operational considerations push us to consider cyclic schedules in which components are maintained at a fixed frequency. In order to efficiently find cyclic schedules, we propose the Cycle Rounding algorithm, which has an approximation guarantee of 2, and a family of Shifted Power-of-Two algorithms, which have an approximation guarantee of 1/ ln(2) ~ 1.4427. Computational results indicate that both algorithms perform much better than their associated performance guarantees providing solutions within 15%-25% of a lower bound. Once a modular system has moved into operations, manpower and transportation scheduling become important considerations when developing maintenance schedules. To address the operations phase, we develop the Modular Maintenance and System Assembly Model to balance the tradeoffs between inventory, maintenance capacity, and transportation resources. This model explicitly captures the risk-pooling effects of a central repair facility while also modeling the interaction between repair actions at such a facility. The full model is intractable for all but the smallest instances. Accordingly, we decompose the problem into two parts, the system assembly portion and module repair portion. Finally, we tie together the Modular Maintenance and System Assembly Model with key concepts from the Modular Maintenance Scheduling Problem to propose an integrated methodology for design and operation.
by Eric Jack Zarybnisky.
Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 243-247).
Our work is motivated by real-world planning challenges faced by a manufacturer of industrial products. In the first part of the thesis, we study a multi-product serial-flow production line that operates in a low-volume, long lead-time environment. The objective is to minimize variable operating costs, in the face of forecast uncertainty, raw material arrival uncertainty and in-process failure. We develop a dynamic-programming-based tactical model to capture the key uncertainties and trade-offs, and to determine the minimum-cost operating tactics. The tactics include smoothing production to reduce production-related costs, and segmenting the serial-flow line with decoupling buffers to protect against variance propagation. For each segment, we specify a work release policy and a production control policy to manage the work-in-process inventory within the segment and to maintain the inventory targets in the downstream buffer. We also optimize the raw material ordering policy with fixed ordering times, long lead-times and staggered deliveries. In the second part of the thesis, we examine a multi-product assembly system that operates in a high-volume, short lead- time environment. The operating tactics used here include determining a fixed-length cyclic schedule to control production, in addition to smoothing production and segmenting the system with decoupling buffers. We develop another dynamic-programming-based tactical model that determines optimal policies for production planning and scheduling, inventory, and raw material ordering; these policies minimize the operating cost for the system in the face of forecast and raw material arrival uncertainty. We tested these models on both hypothetical and actual factory scenarios. The results confirmed our intuition and also helped develop new managerial insights on the application of these operating tactics. Moreover, the tactical model's factory performance predictions were found to be within 10% of simulation results for the testbed systems, thus validating the models.
by Pallav Chhaochhria.
Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 199-204).
We consider a retailer offering an assortment of differentiated substitutable products to price-sensitive customers. Prices are chosen to maximize profit, subject to inventory/ capacity constraints, as well as more general constraints. The profit is not even a quasi-concave function of the prices under the basic multinomial logit (MNL) demand model. Linear constraints can induce a non-convex feasible region. Nevertheless, we show how to efficiently solve the pricing problem under three important, more general families of demand models. Generalized attraction (GA) models broaden the range of nonlinear responses to changes in price. We propose a reformulation of the pricing problem over demands (instead of prices) which is convex. We show that the constrained problem under MNL models can be solved in a polynomial number of Newton iterations. In experiments, our reformulation is solved in seconds rather than days by commercial software. For nested-logit (NL) demand models, we show that the profit is concave in the demands (market shares) when all the price-sensitivity parameters are sufficiently close. The closed-form expressions for the Hessian of the profit that we derive can be used with general-purpose nonlinear solvers. For the special (unconstrained) case already considered in the literature, we devise an algorithm that requires no assumptions on the problem parameters. The class of generalized extreme value (GEV) models includes the NL as well as the cross-nested logit (CNL) model. There is generally no closed form expression for the profit in terms of the demands. We nevertheless how the gradient and Hessian can be computed for use with general-purpose solvers. We show that the objective of a transformed problem is nearly concave when all the price sensitivities are close. For the unconstrained case, we develop a simple and surprisingly efficient first-order method. Our experiments suggest that it always finds a global optimum, for any model parameters. We apply the method to mixed logit (MMNL) models, by showing that they can be approximated with CNL models. With an appropriate sequence of parameter scalings, we conjecture that the solution found is also globally optimal.
by Philipp Wilhelm Keller.
Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 213-229) and index.
Many models of optimization, statistics, social organizations and machine learning capture local dependencies by means of a network that describes the interconnections and interactions of different components. However, in most cases, optimization or inference on these models is hard due to the dimensionality of the networks. This is so even when using algorithms that take advantage of the underlying graphical structure. Approximate methods are therefore needed. The aim of this thesis is to study such large-scale systems, focusing on the question of how randomness affects the complexity of optimizing in a graph; of particular interest is the study of a phenomenon known as correlation decay, namely, the phenomenon where the influence of a node on another node of the network decreases quickly as the distance between them grows. In the first part of this thesis, we develop a new message-passing algorithm for optimization in graphical models. We formally prove a connection between the correlation decay property and (i) the near-optimality of this algorithm, as well as (ii) the decentralized nature of optimal solutions. In the context of discrete optimization with random costs, we develop a technique for establishing that a system exhibits correlation decay. We illustrate the applicability of the method by giving concrete results for the cases of uniform and Gaussian distributed cost coefficients in networks with bounded connectivity. In the second part, we pursue similar questions in a combinatorial optimization setting: we consider the problem of finding a maximum weight independent set in a bounded degree graph, when the node weights are i.i.d. random variables.
(cont.) Surprisingly, we discover that the problem becomes tractable for certain distributions. Specifically, we construct a PTAS for the case of exponentially distributed weights and arbitrary graphs with degree at most 3, and obtain generalizations for higher degrees and different distributions. At the same time we prove that no PTAS exists for the case of exponentially distributed weights for graphs with sufficiently large but bounded degree, unless P=NP. Next, we shift our focus to graphical games, which are a game-theoretic analog of graphical models. We establish a connection between the problem of finding an approximate Nash equilibrium in a graphical game and the problem of optimization in graphical models. We use this connection to re-derive NashProp, a message-passing algorithm which computes Nash equilibria for graphical games on trees; we also suggest several new search algorithms for graphical games in general networks. Finally, we propose a definition of correlation decay in graphical games, and establish that the property holds in a restricted family of graphical games. The last part of the thesis is devoted to a particular application of graphical models and message-passing algorithms to the problem of early prediction of Alzheimer's disease. To this end, we develop a new measure of synchronicity between different parts of the brain, and apply it to electroencephalogram data. We show that the resulting prediction method outperforms a vast number of other EEG-based measures in the task of predicting the onset of Alzheimer's disease.
by Théophane Weber.
Ph.D.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006.
Includes bibliographical references (p. 105-107).
Many strategies for asset allocation involve the computation of expected returns and the covariance or correlation matrix of financial instruments returns. How much of each instrument to own is determined by an attempt to minimize risk (the variance of linear combinations of investments in these financial assets) subject to various constraints such as a given level of return, concentration limits, etc. The expected returns and the covariance matrix contain many parameters to estimate and two main problems arise. First, the data will very likely have outliers that will seriously affect the covariance matrix. Second, with so many parameters to estimate, a large number of observations are required and the nature of markets may change substantially over such a long period. In this thesis we use robust covariance procedures, such as FAST-MCD, quadrant-correlation-based covariance and 2D-Huber-based covariance, to address the first problem and regularization (Bayesian) methods that fully utilize the market weights of all assets for the second. High breakdown affine equivariant robust methods are effective, but tend to be costly when cross-validation is required to determine regularization parameters.
(cont.) We, therefore, also consider non-affine invariant robust covariance estimation. When back-tested on market data, these methods appear to be effective in improving portfolio performance. In conclusion, robust asset allocation methods have great potential to improve risk-adjusted portfolio returns and therefore deserve further exploration in investment management research.
by Xinfeng Zhou.
S.M.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004.
Includes bibliographical references (p. 103-107).
In the first part of the thesis we combine ideas from cutting plane and interior point methods to solve variational inequality problems efficiently. In particular, we introduce "smarter" cuts into two general methods for solving these problems. These cuts utilize second order information on the problem through the use of a gap function. We establish convergence results for both methods, as well as complexity results for one of the methods. Finally, we compare the performance of an approach that combines affine scaling and cutting plane methods with other methods for solving variational inequalities. The second part of the thesis considers a supply chain setting where several capacitated suppliers compete for orders from a single retailer in a multi-period environment. At each period the retailer places orders to the suppliers in response to the prices and capacities they announce. Our model allows the retailer to carry inventory. Furthermore, suppliers can expand their capacity at an additional cost; the retailer faces exogenous, price-dependent, stochastic demand. We analyze discrete as well as continuous time versions of the model: (i) we illustrate the existence of equilibrium policies; (ii) we characterize the structure of these policies; (iii) we consider coordination mechanisms; and (iv) we present some computational results. We also consider a modified model that uses option contracts and finally present some extensions.
by Marina Zaretsky.
Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2009.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (leaves 157-165).
Efficient allocation of scarce resources in networks is an important problem worldwide. In this thesis, we focus on resource allocation problems in a network of congested airports. The increasing demand for access to the world's major commercial airports combined with the limited operational capacity at many of these airports have led to growing air traffic congestion resulting in several billion dollars of delay cost every year. In this thesis, we study two demand-management techniques -- strategic and operational approaches -- to mitigate airport congestion. As a strategic initiative, auctions have been proposed to allocate runway slot capacity. We focus on two elements in the design of such slot auctions -- airline valuations and activity rules. An aspect of airport slot market environments, which we argue must be considered in auction design, is the fact that the participating airlines are budget-constrained. -- The problem of finding the best bundle of slots on which to bid in an iterative combinatorial auction, also called the preference elicitation problem, is a particularly hard problem, even more in the case of airlines in a slot auction. We propose a valuation model, called the Aggregated Integrated Airline Scheduling and Fleet Assignment Model, to help airlines understand the true value of the different bundles of slots in the auction. This model is efficient and was found to be robust to data uncertainty in our experimental simulations.
(cont.) -- Activity rules are checks made by the auctioneer at the end of every round to suppress strategic behavior by bidders and to promote consistent, continual preference elicitation. These rules find applications in several real world scenarios including slot auctions. We show that the commonly used activity rules are not applicable for slot auctions as they prevent straightforward behavior by budget-constrained bidders. We propose the notion of a strong activity rule which characterizes straightforward bidding strategies. We then show how a strong activity rule in the context of budget-constrained bidders (and quasilinear bidders) can be expressed as a linear feasibility problem. This work on activity rules also applies to more general iterative combinatorial auctions.We also study operational (real-time) demand-management initiatives that are used when there are sudden drops in capacity at airports due to various uncertainties, such as bad-weather. We propose a system design that integrates the capacity allocation, airline recovery and inter-airline slot exchange procedures, and suggest metrics to evaluate the different approaches to fair allocations.
by Pavithra Harsha.
Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 97-103).
This thesis presents new methods for exploring data using visualization techniques. The first part of the thesis develops a procedure for visualizing the sampling variability of a plot. The motivation behind this development is that reporting a single plot of a sample of data without a description of its sampling variability can be uninformative and misleading in the same way that reporting a sample mean without a confidence interval can be. Next, the thesis develops a method for simplifying large scatter plot matrices, using similar techniques as the above procedure. The second part of the thesis introduces a new diagnostic method for regression called backward selection search. Backward selection search identifies a relevant feature set and a set of influential observations with good accuracy, given the difficulty of the problem, and additionally provides a description, in the form of a set of plots, of how the regression inferences would be affected with other model choices, which are close to optimal. This description is useful, because an observation, that one analyst identifies as an outlier, could be identified as the most important observation in the data set by another analyst. The key idea behind backward selection search has implications for methodology improvements beyond the realm of visualization. This is described following the presentation of backward selection search. Real and simulated examples, provided throughout the thesis, demonstrate that the methods developed in the first part of the thesis will improve the effectiveness and validity of data visualization, while the methods developed in the second half of the thesis will improve analysts' abilities to select robust models.
by Rajiv Menjoge.
Ph.D.