Decision, Operations & Information Technologies Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2761
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Item Heuristics for Solving Three Routing Problems: Close-Enough Traveling Salesman Problem, Close-Enough Vehicle Routing Problem, Sequence-Dependent Team Orienteering Problem(2009) Mennell, William Kenneth; Golden, Bruce L.; Wasil, Edward A.; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we examine three important routing problems. In the second chapter we investigate the Close-Enough Traveling Salesman Problem (CETSP) in which a salesman must get within a specified radius of each node to visit it. The third chapter studies the multi-vehicle extension of the CETSP, the Close-Enough Vehicle Routing Problem (CEVRP). In the fourth chapter, we develop a post-processor to improve the accuracy of our heuristics for solving the CETSP and CEVRP. In the fifth chapter, we solve the Sequence-Dependent Team Orienteering Problem (SDTOP) in which the profit received for each node visited is dependent on the sequence in which all the nodes are visited. We summarize the dissertation in the final chapter. The CETSP, CEVRP, and SDTOP have application in aerial reconnaissance route planning. We formulate each problem as a mathematical program and apply heuristic and combinatorial optimization techniques to solve them. We present the results of extensive computational experiments that show that our methods produce high-quality solutions quickly.Item ROBUST REVENUE MANAGEMENT WITH LIMITED INFORMATION : THEORY AND EXPERIMENTS(2009) Lan, Yingjie; Ball, Michael O; Karaesmen, Itir A; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Revenue management (RM) problems with full probabilistic information are well studied. However, as RM practice spreads to new businesses and industries, there are more and more applications where no or only limited information is available. In that respect, it is highly desirable to develop models and methods that rely on less information, and make fewer assumptions about the underlying uncertainty. On the other hand, a decision maker may not only lack data and accurate forecasting in a new application, but he may have objectives (e.g. guarantees on worst-case profits) other than maximizing the average performance of a system. This dissertation focuses on the multi-fare single resource (leg) RM problem with limited information. We only use lower and upper bounds (i.e. a parameter range), instead of any particular probability distribution or random process to characterize an uncertain parameter. We build models that guarantee a certain performance level under all possible realizations within the given bounds. Our methods are based on the regret criterion, where a decision maker compares his performance to a perfect hindsight (offline) performance. We use competitive analysis of online algorithms to derive optimal static booking control policies that either (i) maximize the competitive ratio (equivalent to minimizing the maximum regret) or (ii) minimize the maximum absolute regret. Under either criterion, we obtain closed-form solutions and investigate the properties of optimal policies. We first investigate the basic multi-fare model for booking control, assuming advance reservations are not cancelled and do not become no-shows. The uncertainty in this problem is in the demand for each fare class. We use information on lower and upper bounds of demand for each fare class. We determine optimal static booking policies whose booking limits remain constant throughout the whole booking horizon. We also show how dynamic policies, by adjusting the booking limits at any time based on the bookings already on hand, can be obtained. Then, we integrate overbooking decisions to the basic model. We consider two different models for overbooking. The first one uses limited information on no-shows; again the information being the lower and upper bound on the no-show rate. This is appropriate for situations where there is not enough historical data, e.g. in a new business. The second model differs from the first by assuming the no-show process can be fully characterized with a probabilistic model. If a decision-maker has uncensored historical data, which is often the case in reality, he/she can accurately estimate the probability distribution of no-shows. The overbooking and booking control decisions are made simultaneously in both extended models. We derive static overbooking and booking limits policies in either case. Extensive computational experiments show that the proposed methods that use limited information are very effective and provide consistent and robust results. We also show that the policies produced by our models can be used in combination with traditional ones to enhance the system performance.Item Industrial Flexibility in Theory and Practice(2009) Reindorp, Matthew; Fu, Michael; Goyal, Manu; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)At the heart of any decision problem is some degree of "flexibility" in how to act. Most often, we aim to extract greatest possible value from this inherent flexibility. The three essays compiled here are aligned with this same general aim, but we have an important secondary concern: to highlight the value of flexibility itself in the various situations we study. In the first essay, we consider the timing of an action: when to replace obsolete subsystems within an extensive, complex infrastructure. Such replacement action, known as capital renewal, must balance uncertainty about future profitability against uncertainty about future renewal costs. Treating renewal investments as real options, we derive the unique, closed-form optimal solution to the infinite horizon version of this problem and determine the total present value of an institution's capital renewal options. We investigate the sensitivity of the solution to variations in key problem parameters. The second essay addresses the promising of lead times in a make-to-order environment, complicated by the need to serve multiple customer classes with differing priority levels. We tackle this problem with a "model free" approach: after preparing a discrete-event simulation of a make-to-order production system, we determine a policy for lead time promising through application of a reinforcement learning algorithm. The third essay presents an empirical analysis of new product launches in the automotive industry, showing that manufacturing flexibility is one key indicator of superior productivity during launch. We explore the financial dimensions of the apparent productivity differences and show that the use of flexible manufacturing increases an automobile plant's likelihood of being chosen to host a new product launch.Item Simulating and Optimizing: Military Manpower Modeling and Mountain Range Options(2009) Hall, Andrew Oscar; Fu, Michael C; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation we employ two different optimization methodologies, dynamic programming and linear programming, and stochastic simulation. The first two essays are drawn from military manpower modeling and the last is an application in finance. First, we investigate two different models to explore the military manpower system. The first model describes the optimal retirement behavior for an Army officer from any point in their career. We address the optimal retirement policies for Army officers, incorporating the current retirement system, pay tables, and Army promotion opportunities. We find that the optimal policy for taste-neutral Lieutenant Colonels is to retire at 20 years. We demonstrate the value and importance of promotion signals regarding the promotion distribution to Colonel. Signaling an increased promotion opportunity from 50% to 75% for the most competitive officers switches their optimal policy at twenty years to continuing to serve and competing for promotion to Colonel. The second essay explores the attainability and sustainability of Army force profiles. We propose a new network structure that incorporates both rank and years in grade to combine cohort, rank, and specialty modeling without falling into the common pitfalls of small cell size and uncontrollable end effects. This is the first implementation of specialty modeling in a manpower model for U.S. Army officers. Previous specialty models of the U.S. Army manpower system have isolated accession planning for Second Lieutenants and the Career Field Designation process for Majors, but this is the first integration of rank and specialty modeling over the entire officer's career and development of an optimal force profile. The last application is drawn from financial engineering and explores several exotic derivatives that are collectively known Mountain Range options, employing Monte Carlo simulation to price these options and developing gradient estimates to study the sensitivities to underlying parameters, known as "the Greeks". We find that IPA and LR/SF methods are efficient methods of gradient estimation for Mountain Range products at a considerably reduced computation cost compared with the commonly used finite difference methods.Item Impact of Leadership on Continued Participation in Online Groups(2008-11-20) Johnson, Steven L.; Faraj, Samer; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Online groups formed by volunteer members are increasingly recognized as sources of innovative ideas, as producers of information goods, and as a critical component for successful product marketing. Compared to formal organizations, online groups appear as anarchic collections of individuals largely devoid of formal authority. Yet online groups develop strong group norms, successfully generate information goods, and satisfy member needs--outcomes that seem impossible without some form of leadership by influential members. Research on open-membership voluntary online groups has consistently found that contribution to online groups is dominated by a small percentage of participants. The goal of this research is to better understand the role of leadership in online groups and to evaluate the impact of leadership in maintaining online groups by supporting continued participation intentions of existing members. I explored three related questions regarding leadership in online groups. First, does member interaction with group leaders contribute to continued participation intentions over and above a model based on past participation? Second, do shared context and direct communication with leaders impact continued participation intentions? And third, do group characteristics--group psychological safety, group size, and perceived number of leaders--moderate the relationship between group members and group leaders? I collected 535 survey responses from members of thirty-three different online groups (average of sixteen members per group) and also analyzed group communication history (a total of 135,477 messages). This cross-level analysis furthers our understanding of the relationship between interaction with group leadership, psychological safety, participation role intentions, and turnover intentions. I found that leadership in online groups is a determinant of online group outcomes. Online group leaders shape the group context, including psychological safety, which encourages or discourages participation. This study shows that leadership processes, group context, and differentiation among dimensions of participation intentions are all important considerations for further understanding of online groups.Item The Work Network Model: Understanding the interplay of actor, artifact, and action in technology-based change(2008-10-29) Yeow, Yong Kwang; Faraj, Samer; Agarwal, Ritu; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Organization and IS scholars interested in the impact of IT on organizational change have acknowledged the indeterminate relationship between technological and organizational change. This reality stems from the complex interaction of the institutional context with human cognition and action that determine the path that technological change take in order to bring about organizational outcomes. Yet in this milieu there is little account for why specific context or conditions are salient. The goal of this research is to understand how technological change is related to organizational change by opening up the blackbox of the work context and analyze how the material aspects of the IT artifact relate to the actors and their actions. Specifically, I studied 1) How do the design and implementation of an EMR system impact the configuration of the system? 2) How do users and their practices interact with the configured system? 3) How do these interactions influence organizational outcomes so that one site is more "successful" than another? I explore these research questions using the perspective of work an organization is engaged in, specifically how IT artifacts are relationally linked to actors, actions and the organizational context. As my research questions deal with a process issue, I conducted a longitudinal field study of an EMR system implementation beginning from the implementation phase to deployment and use phases. I analyzed archival, interviews and observations data to develop a grounded theory of technology-based organizational change. Based on my findings I developed the Work Network Model of technology-based change. The model proposes that the main mechanism of change is the network within the context of an organization's work. It also proposed that analyzing the process of multi-level political negotiations during the configuration of a new technology allows us to understand how technology-change evolve once it is introduced in an organization. Finally it shows how institutional, infrastructural and work practices play a role both during the configuration and use phase of the new technology. Apart from its theoretical contributions, this research attempts to provide a new method to consider and design work practices with new technologies via the Work Network lens.Item ANALYSIS OF DISTRIBUTION-FREE METHODS FOR REVENUE MANAGEMENT(2008-10-14) Gao, Huina; Ball, Michael; Karaesmen, Itir; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Revenue management (RM) is one area of research and practice that has gained significant attention in the past decade. The practice originated in the airline industry, where the idea was to maximize revenues obtained from a fixed amount of resources through differentiation/segmentation and strategic use of pricing and capacity. While many of the research models take into account uncertainty, the uncertainty is modeled using random variables and known probability distributions, which is often difficult to estimate and prone to error for a variety of reasons. For instance, demand patterns can fluctuate substantially from the past,and characterizing demand from censored data is challenging. This dissertation focuses on the multifare single resource (leg) problem in RM.We consider the "limited information" case where the demand information available consists of lower and upper bounds rather than a characterization of a particular probability distribution or stochastic process. We first investigate the value of the amount and type of information used in solving the single-leg RM problem. This is done via extensive computational experiments. Our results indicate that new robust methods using limited information perform comparably to other well-known procedures. These robust policies are very effective and provide consistent results, even though they use no probabilistic information. Further, robust policies are less prone to errors in modeling demand. Results of our preliminary computations justify the use of robust methods in the multi-fare single-leg problem. We next apply this distribution-free approach to a setting where progression of demand is available through time-dependent bounds. We do not make any further assumptions about the demand or the arrival process beyond these bounds and also do not impose a risk neutrality assumption. Our analytical approach relies on competitive analysis of online algorithms, which guarantee a certain performance level under all possible realizations within the given lower and upper bounds. We extend the robust model from a problem using static information into a dynamic setting, in which time-dependent information is utilized effectively. We develop heuristic solution procedures for the dynamic problem. Extensive computational experiments show that the proposed heuristics are very effective and provide gains over static ones. The models and computations described above assume a single airline, disregarding competition. As an extension of robust decision-making, in the third part of this dissertation, we analyze a model with two airlines and two fare classes where the airlines engage in competition. The model does not use any probabilistic information and only the range of demand in each fare-class is known. We develop a game-theoretic model and use competitive analysis of online algorithms to study the model properties. We derive the booking control policies for both centralized and decentralized models and provide additional numerical results.Item Empirical Analyses on Federal Thrift Savings Plan Portfolio Optimization(2007-11-27) Nestler, Scott T; Fu, Michael C; Madan, Dilip B; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There is ample historical data to suggest that log returns of stocks and indices are not independent and identically distributed Normally, as is commonly assumed. Instead, the returns of financial assets are skewed and have higher kurtosis. To account for skewness and excess kurtosis, it is necessary to have a distribution that is more flexible than the Gaussian distribution and uses additional information that may be present in higher moments. The federal government's Thrift Savings Plan (TSP) is the largest defined contribution retirement savings and investment plan, with nearly 3.6 million participants and over $173 billion in assets. The TSP offers five assets (government bond fund, fixed income fund, large-cap stock fund, small-cap stock fund, and international stock fund) to U.S. government civilian employees and uniformed service members. The limited choice of investments, in comparison to most 401(k) plans, may be disappointing from a participant's perspective; however, it provides an attractive framework for empirical study. In this study, we investigate how the optimal choice of TSP assets changes when traditional portfolio optimization methods are replaced with newer techniques. Specifically, the following research questions are posed and answered: (1) Does use of a non-Gaussian factor model for returns, generated with independent components analysis (ICA) and following the Variance Gamma (VG) process, provide any advantage in constructing optimal TSP portfolios? (2) Can excess TSP portfolio returns be generated through rebalancing to an optimal mix? If so, what frequency of rebalancing provides benefits that offset increased computationalal and administrative burden? (3) How does the use of coherent risk and portfolio performance measures, in place of variance as the traditional the measure for risk and Sharpe Ratio as the usual portfolio performance measure affect TSP portfolio selection? We show through simulation that some of the newer schemes should produce excess returns over conventional (mean-variance optimization with Normally-distributed returns) portfolio choice models. The use of some or all of these methods could benefit the nearly 4 million TSP participants in achieving their retirement savings and investment objectives. Furthermore, we propose two new portfolio performance measures based on recent developments in coherent measures of risk.Item Absorptive Capacity And Open Source Software Project Performance(2007-12-04) Daniel, Sherae Lee; Agarwal, Ritu; Stewart, Katherine; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The open source phenomenon is an exciting movement that is transforming traditional forms of software development. Some open source software (OSS) projects, such as Linux and Apache, are performing extremely well and rapidly replacing proprietary software in major corporations and governments. In addition to these highly publicized examples, there are legions of OSS projects that have not experienced a similar uptake. The purpose of this dissertation is to understand how and why some OSS projects are able to perform better than others. It explores antecedents of OSS project performance from a knowledge-focused perspective because software development is a knowledge-intensive activity. In particular, it examines the development and effects of absorptive capacity for an OSS project. Absorptive capacity captures the degree to which an organization is able to acquire and assimilate knowledge. In describing how OSS absorptive capacity is developed, this dissertation identifies characteristics and behaviors of project participants that indicate an OSS project's absorptive capacity. I underscore the importance of the characteristics and behaviors of two different sets of project participants in an OSS project: those in the Internet-based user community and those in the development group. To the extent that absorptive capacity influences OSS project performance, I argue that these characteristics and behaviors are critical for OSS project performance. Archival data about OSS projects that use the SourceForge platform are used to empirically test the model developed. This dissertation makes several contributions to theory and practice. The research informs project managers regarding the participants to target and behaviors to encourage that will lead to superior performance for their OSS project. In exploring the effect of absorptive capacity in an OSS project, this dissertation adds to the absorptive capacity literature by examining the interaction of two dimensions of this construct: knowledge acquisition and knowledge transfer. Finally, this dissertation extends the OSS literature by specifically exploring the effect of the Internet-based user community on OSS project performance.Item The Impact Of Online Sponsored Search Advertising On Consumer And Seller Strategies(2007-08-07) Animesh, Animesh; Agarwal, Ritu; Viswanathan, Siva; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Sponsored search advertising has emerged as an important and significant forum for advertisers, accounting for 40% of all advertising spending online. The unique features of sponsored search advertising - the nature of consumer search as well as the pricing mechanisms employed - differentiate it from traditional advertising formats, and raise many interesting questions regarding consumers' search and purchase behavior, sellers' advertising strategies, and the ensuing market dynamics. However, despite the robust growth in sponsored search advertising, research on its implications is limited. My dissertation, comprising three essays, seeks to fill this gap. In addition to examining the effects of sponsored search advertising on consumers and sellers, I also investigate the validity of theories developed for traditional media in an emerging online sponsored search context. The first essay focuses on the impact of a seller's sponsored search advertising strategies, including its rank in the sponsored listing, the unique selling proposition (USP) employed in its advertisement text/creative, and competitive market dynamics on the performance of the focal seller's advertisement. Drawing upon prior research on consumer search and directional markets, I propose a model of the consumer search process in the sponsored search context and conduct an empirical study to test the research model. The results validate the research hypothesis that the search listing can act as a consumer filtering mechanism and competitive intensity within adjacent ranks has a significant impact on the seller's performance. The second essay employs consumer search and quality signaling theories from information systems, marketing, and economics to understand the impact of the informational cues contained in the sponsored search listing about sellers' relative advertising expenditure on consumer search and purchase behavior. Contrary to conventional wisdom, I find that the unique format of the sponsored search listing significantly increases the strength of the advertising signal vis-à-vis the price signal. In addition, I find that the risk attitude of consumers has a significant impact on the valence of these different information cues in the online setting. The third essay examines market outcomes in directional markets such as sponsored search and comparison shopping advertising. Specifically, I focus on comparison shopping advertising where advertising not only informs consumers about price and quality but also directs consumer search. I find that the relationship between a firm's price, quality, and advertising intensity in this market is strikingly different from that in traditional markets, a result attributable to the differential impact of price and quality on an advertiser's conversion rates and profit margins. Overall, these studies provide crucial insights into consumer behavior in online sponsored search markets. These findings have significant implications for firms, as well as for the market makers. Insights from these studies will enable practitioners to develop appropriate advertising strategies and online intermediaries to optimize the design of online sponsored search markets.