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1 
Managing Material and Financial Flows in Supply ChainsLuo, Wei January 2013 (has links)
<p>This dissertation studies the integration of material and financial flows in supply chains, with the goal of examining the impact of cash flows on the individual firm's decision making and the overall supply chain efficiency. We develop analytical models to provide effective policy recommendations and derive managerial insights.</p><p>We first consider a creditconstrained firm that orders inventory to satisfy stochastic demand in a finite horizon. The firm provides trade credit to the customer and receives it from the supplier. A default penalty is incurred on the unfulfilled payment to the supplier. We utilize an accounting concept of working capital to obtain optimal and nearoptimal inventory policies. The model enables us to suggest an acceptable purchasing price offered in the supplier's trade credit contract, and to demonstrate how liquidity provision can mitigate the bullwhip effect. We then study a joint inventory and cash management problem for a multidivisional supply chain. We consider different levels of cash concentration: cash pooling and transfer pricing. We develop the optimal joint inventory replenishment and cash retention policy for the cash pooling model, and construct cost lower bounds for the transfer pricing model. The comparison between these two models shows the value of cash pooling, although a big portion of this benefit may be recovered through optimal transfer pricing schemes. Finally, we build a supply chain model to investigate the material flow variability without cash constraint. Our analytical results provide conditions under which the material bullwhip effect exists. These results can be extended to explain the similar effect when financial flows are involved. In sum, this dissertation demonstrates the importance of working capital and financial integration in supply chain management.</p> / Dissertation

2 
New Markov Decision Process Formulations and Optimal Policy Structure for AssembletoOrder and New Product Development ProblemsNadar, Emre 09 May 2012 (has links)
This thesis examines two complex, dynamic problems by employing the theory of Markov
Decision Processes (MDPs). Chapters 2 and 3 consider assembletoorder (ATO) inventory systems. An ATO system consists of several components and several products, and assembles products as demand is realized; it is becoming increasingly popular since it provides greater flexibility in manufacturing at a reasonable cost. This work contributes to the ATO research stream by characterizing optimal inventory replenishment and allocation policies. Chapter 4 examines the new product development (NPD) process with scarce resources and many projects in parallel, each lasting several periods, in the face of uncertainty. This study advances the NPD literature by revealing that optimal project selection and resource allocation decisions are congestiondependent. Below, I elaborate on the novel optimal policies and structural results I obtain using MDP formulations, which is the overarching theme of the thesis.
In Chapter 2, I consider generalized ATO “Msystems" with multiple components and multiple products. These systems involve a single “master" product which uses multiple units from each component, and multiple individual products each of which consumes multiple units from a different component. Such systems are common for manufacturers selling an assembled product as well as individual spare parts.
I model these systems as infinitehorizon MDPs under the discounted cost criterion. Each component is produced in batches of fixe size in a maketo stock fashion; batch sizes are determined by individual product sizes. Production times are independent and exponentially distributed. Demand for each product arrives as an independent Poisson process. If not satisfied immediately upon arrival, these demands are lost. Therefore the state of the system can be described by component inventory levels.
A control policy specifies when a batch of components should be produced (i.e., inventory replenishment), and whether an arriving demand for each product should be satisfied (i.e.,inventory allocation). The convexity property that has been largely used to characterize optimal policies in the MDP literature may fail to hold in our case. Therefore I introduce new functional characterizations for submodularity and supermodularity restricted to certain lattices of the state space. The optimal cost function satisfies these new characterizations: The state space of the problem can be partitioned into disjoint lattices such that, on each lattice, (a) it is optimal to produce a batch of a particular component if and only if the state vectors less than a certain threshold associated with that component, and (b) it is optimal to fulfill a demand of a particular product if and only if the state vector is greater than or equal to a certain threshold associated with that product. I refer to this policy as a latticedependent basestock and latticedependent rationing (LBLR) policy. I also show that if the optimization criterion is modified to the average cost rate, LBLR remains optimal.
Chapter 2 makes three important contributions. First, this is the first study that establishes the optimal inventory replenishment and allocation policies for Msystems. Second, this study is the first to characterize the optimal policies for any ATO problem when different products may use the same component in different quantities. Third, I introduce new functional characterizations restricted to certain lattices of the state space, giving rise to an LBLR policy.
In Chapter 3, I evaluate the use of an LBLR policy for general ATO systems as a heuristic.
I numerically compare the globally optimal policy to LBLR and two other heuristics from the literature: a statedependent basestock and statedependent rationing (SBSR) policy, and a fixed basestock and fixed rationing (FBFR) policy. Taking the average cost rate as the performance criterion, I develop a linear program to find the globally optimal cost, and Mixed Integer Programming formulations to find the optimal cost within each heuristic class. I generate more than 1800 instances for the general ATO problem, not restricted to the assumptions of Chapter 2, such as the Msystem product structure. Interestingly, LBLR yields the globally optimal cost in all instances, while SBSR and FBFR provide solutions within 2.7% and 4.8% of the globally optimal cost, respectively. These numerical results also provide several insights into the performance of LBLR relative to other heuristics: LBLR and SBSR perform significantly better than FBFR when replenishment batch sizes imperfectly match the component requirements of the most valuable or most highly demanded product. In addition,
LBLR substantially outperforms SBSR if it is crucial to hold a significant amount of inventory that must be rationed.
Based on the numerical findings in Chapter 3, future research could investigate the optimality of LBLR for ATO systems with general product structures. However, as I construct counterexamples showing that submodularity and supermodularity { which are used to prove the optimality of LBLR in Chapter 2 { need not hold for general ATO systems, showing the optimality of LBLR for general ATO systems will likely require alternate proof techniques.
In Chapter 4, I study the problem of project selection and resource allocation in a multistage new product development (NPD) process with stagedependent resource constraints.
As in Chapters 2 and 3, I model the problem as an infinitehorizon MDP, specifically under the discounted cost criterion. Each NPD project undergoes a different experiment at each stage of the NPD process; these experiments generate signals about the true nature of the project. Experimentation times are independent and exponentially distributed. Beliefs about the ultimate outcome of each project are updated after each experiment according to a Bayesian rule. Projects thus become differentiated through their signals, and all available signals for a project determine its category. The state of the system is described by the numbers of projects in each category. A control policy specifies, given the system state, how to utilize the resources at each stage, i.e., the projects (i) to experiment at each stage, and (ii) to terminate.
I characterize the optimal control policy as following a new type of strategy, statedependent oncongestive promotion (SDNCP), for two different special cases of the general problem: (a)when there is a single informative experiment and projects are not terminated, or (b) when there are multiple uninformative experiments. An SDNCP policy implies that, at each stage, it is optimal to advance a project with the highest expected reward to the next stage if and only if the number of projects in each successor category is less than a statedependent threshold. In addition, I show that threshold values decrease in a nonstrict sense as a later stage becomes more congested or as an earlier stage becomes less congested. (A stage becomes “more congested" with an increase in the number of projects at this stage or with an increase in the expected reward of any project at this stage.) An SDNCP policy can be used as a heuristic for the general problem. I support the outstanding performance of an SDNCP policy in the general case through a numerical study. These findings highlight the importance of taking into account congestion in optimal portfolio strategies.

3 
Managing Windbased Electricity Generation and StorageZhou, Yangfang 01 November 2012 (has links)
Among the many issues that profoundly affect the world economy every day, energy is one of the most prominent. Countries such as the U.S. strive to reduce reliance on the import of fossil fuels, and to meet increasing electricity demand without harming the environment.
Two of the most promising solutions for the energy issue are to rely on renewable energy, and to develop efficient electricity storage. Renewable energy—such as wind energy and solar energy—is free, abundant, and most importantly, does not exacerbate the global warming problem. However, most renewable energy is inherently intermittent and variable, and thus can benefit greatly from coupling with electricity storage, such as gridlevel industrial batteries. Grid storage can also help match the supply and demand of an entire electricity market. In addition, electricity storage such as car batteries can help reduce dependence on oil, as it can enable the development of Plugin Hybrid Electric Vehicles, and Battery Electric Vehicles. This thesis focuses on understanding how to manage renewable energy and electricity storage properly together, and electricity storage alone.
In Chapter 2, I study how to manage renewable energy, specifically wind energy. Managing wind energy is conceptually straightforward: generate and sell as much electricity as possible when prices are positive, and do nothing otherwise. However, this leads to curtailment when wind energy exceeds the transmission capacity, and possible revenue dilution when current prices are low but are expected to increase in the future. Electricity storage is being considered as a means to alleviate these problems, and also enables buying electricity from the market for later resale. But the presence of storage complicates the management of electricity generation from wind, and the value of storage for a windbased generator is not entirely understood.
I demonstrate that for such a combined generation and storage system the optimal policy does not have any apparent structure, and that using overly simple policies can be considerably suboptimal. I thus develop and analyze a triplethreshold policy that I show to be nearoptimal. Using a financial engineering price model and calibrating it to data from the New York Independent System Operator, I show that storage can substantially increase the monetary value of a wind farm: If transmission capacity is tight, the majority of this value arises from reducing curtailment and timeshifting generation; if transmission capacity is abundant this value stems primarily from timeshifting generation and arbitrage. In addition, I find that while more storage capacity always increases the average energy sold to the market, it may actually decrease the average wind energy sold when transmission capacity is abundant.
In Chapter 3, I examine how electricity storage can be used to help match electricity supply and demand. Conventional wisdom suggests that when supply exceeds demand, any electricity surpluses should be stored for future resale. However, because electricity prices can be negative, another potential strategy of dealing with surpluses is to destroy them. Using real data, I find that for a merchant who trades electricity in a market, the strategy of destroying surpluses is potentially more valuable than the conventional strategy of storing surpluses.
In Chapter 4, I study how the operation and valuation of electricity storage facilities can be affected by their physical characteristics and operating dynamics. Examples are the degradation of energy capacity over time and the variation of roundtrip efficiency at different charging/discharging rates. These dynamics are often ignored in the literature, thus it has not been established whether it is important to model these characteristics. Specifically, it remains an open question whether modeling these dynamics might materially change the prescribed operating policy and the resulting valuation of a storage facility. I answer this question using a representative setting, in which a battery is utilized to trade electricity in an energy arbitrage market.
Using engineering models, I capture energy capacity degradation and efficiency variation explicitly, evaluating three types of batteries: lead acid, lithiumion, and Aqueous Hybrid Ion— a new commercial battery technology. I calibrate the model for each battery to manufacturers’ data and value these batteries using the same calibrated financial engineering price model as in Chapter 2. My analysis shows that: (a) it is quite suboptimal to operate each battery as if it did not degrade, particularly for lead acid and lithiumion; (b) reducing degradation and efficiency variation have a complimentary effect: the value of reducing both together is greater than the sum of the value of reducing one individually; and (c) decreasing degradation may have a bigger effect than decreasing efficiency variation.

4 
Cognitive Style Diversity in TeamsAggarwal, Ishani 01 January 2013 (has links)
In this dissertation, I undertake the study of cognitive styles in teams in three papers. Cognitive styles are psychological dimensions that represent consistencies in how individuals acquire and process information, and guide their performance on information processing, decision making, problem solving, and creativity tasks. In addition, they distinguish between individuals from different educational and functional areas. They constitute an important, though largely underrepresented, area of team research. I investigate the relationship between cognitive style diversity and team performance on tasks that impose different demands on teams execution and creativity. Across the three papers, I identify important processes such as strategic focus, strategic consensus, transactive memory, and learning that further explicate this relationship. The studies move the ongoing debate about whether and how diversity is beneficial and detrimental to team performance forward by exploring task contexts that benefit from diversity, and those that do not. In the final paper, I highlight one effective way to optimize the opposing forces that make diversity a challenging phenomenon to study, thus attempting to move the debate toward a resolution.
In the first paper, I investigate the effect of members’ cognitive styles on team processes that affect errors in execution tasks. In two laboratory studies, I investigate how a team’s composition (members’ cognitive styles related to object and spatial visualization) affects the team’s strategic focus and strategic consensus, and how those affect the team’s commission of errors. Errors have crucial implications for many reallife organizational teams carrying out execution tasks. Study 1, conducted with 70 dyads performing a navigation and identification task, established that teams high in spatial visualization are more processfocused than teams high in object visualization. Process focus, which pertains to a team’s attention to the details of conducting a task, is associated with fewer errors. Study 2, conducted with 64 teams performing a building task, established that heterogeneity in cognitive style is negatively associated with the formation of a strategic consensus, which has a direct and mediating relationship with errors.
In the second paper, I investigate the effect of team members’ cognitive style composition, and related team processes, on creativity. Creativity encompasses the processes leading to the generation of novel and useful ideas. In a study with 112 graduatestudent teams working on a semesterlong project, I explore the effect of the team’s cognitive style composition on its transactive memory and strategic consensus, and find that it influences both these processes. Furthermore, I find that team’s transactive memory is positively related to two aspects of creativity: flexibility and fluency. Originality, the third aspect of creativity is influenced by the team’s strategic consensus and strategic focus. The study provides a nuanced understanding of how diverse inputs, but integrating processes, benefit team creativity.
In the third paper I highlight that cognitive diversity in teams is associated with both benefits and costs, and increasing the benefits linked with having a greater wealth of human resources without increasing the associated coordination costs is a challenge. In this paper, I provide a new lens for looking at team composition in terms of this costbenefit tradeoff, and propose one way to optimize it. I study how cognitive resources are distributed in teams, emphasizing both breadth and depth, and investigate the influence of versatile team members, or members who encompass depth in a breadth of domains. In two studies, I find evidence for the proposition that the number of cognitively versatile members in the team is positively associated with team performance in execution tasks, explaining variance above and beyond standard and nonstandard ways of capturing diversity. Interestingly, I find that while there is generally a curvilinear (inverted Ushaped) relationship between team size and team performance, there is a positive linear relationship between size and performance in teams that have cognitively versatile members. I also find that the positive impact of cognitively versatile members on performance in execution tasks is facilitated by process learning. I discuss the implications of this alternative way of viewing diversity.
Taken together, this dissertation explores team composition using deeplevel diversity variables that directly relate to functional areas of individuals in organizations. The three papers contribute to an underrepresented area of organizational research, and establish the importance of the team’s cognitive style composition to team performance. Also, by addressing many calls in the groups and teams research literature, this dissertation aims at providing a nuanced understanding of composition, processes and performance in teams, revealing the complexity of teamwork.

5 
Understanding the Strength of GeneralPurpose Cutting PlanesMolinaro, Marco 01 January 2013 (has links)
Cutting planes for a mixedinteger program are linear inequalities which are satisfied by all feasible solutions of the latter. These are fundamental objects in mixedinteger programming that are critical for solving largescale problems in practice. One of the main challenge in employing them is that there are limitless possibilities for generating cutting planes; the selection of the strongest ones is crucial for their effective use. In this thesis, we provide a principled study of the strength of generalpurpose cutting planes, giving a better understanding of the relationship between the different families of cuts available and analyzing the properties and limitations of our current methods for deriving cuts.
We start by analyzing the strength of disjunctive cuts that generalize the ubiquitous split cuts. We first provide a complete picture of the containment relationship of the split closure, second split closure, cross closure, crooked cross closure and tbranch split closure. In particular, we show that rank2 split cuts and crooked cross cuts are neither implied by cross cuts, which points out the limitations of the latter; these results answer questions left open in [56, 65]. Moreover, given the prominent role of relaxations and their computational advantages, we explore how strong are cross cuts obtained from basic and 2row relaxations. Unfortunately we show that not all cross cuts can be obtained as cuts based on these relaxation, answering a question left open in [56]. One positive message from this result, though, is that cross cuts do not suffer from the limitations of these relaxations.
Our second contribution is the introduction of a probabilistic model for comparing the strength of families of cuts for the continuous relaxation. We employ this model to compare the important split and triangle cuts, obtaining results that provide improved information about their behavior. More precisely, while previous works indicated that triangle cuts should be much stronger than split cuts, we provide the first theoretical support for the effect that is observed in practice: for most instances, these cuts have the same strength.
In our third contribution, we study the multidimensional infinite relaxation introduced by Gomory and Johnson in the late 60’s, which has been an important tool for analyzing and obtaining insights on cutting planes. The celebrated Gomory Johnson’s 2Slope Theorem gives a sufficient condition for a cut to be facet defining from the 1row infinite relaxation. We provide an extension of this result for the krow case, for arbitrary k, which we call the (k + 1)Slope Theorem. Despite increasing interest in understanding the multirow case, no such extension was known prior to our work. This result, together with the relevance of 2slope functions for the 1dimensional case, indicates that (k + 1)slope functions might lead to strong cuts in practice.
In our fourth contribution, we consider cuts that generalize Gomory fractional cuts but take into account upper bounds imposed on the variables. More specifically, we revisit the lopsided cuts obtained recently by Balas and Qualizza via a disjunctive procedure. We give a geometric interpretation of these cuts, viewing them as cuts for the infinite relaxation that are strengthened by a geometric lifting procedure. Using this perspective, we are able to generalize these cuts to obtain a family of cuts which has on one end the GMI cut, and on the other end the lopsided cuts. We show that all these cuts are “new”, namely they are all facets of the infinite relaxation with upper bounded basic variable. We conclude by presenting preliminary experimental results, which unfortunately shows that these cuts decrease in importance as they move away from the GMI inequality, complementing the experimental results from Balas and Qualizza.
In our final contribution, we further explore properties and characterizations of split cuts, focusing on a general model of mixedinteger corner relaxation. The backbone of this work is a description of the split cuts for this relaxation from the perspective of cutgenerating functions; this essentially establishes the equivalence of split cuts and (a generalization of) the kcuts [50]. As our previous result, this characterization is obtained using the geometric lifting idea, illustrating its flexibility as a tool for analyzing cuts. As a consequence, we show that every split cut for a corner relaxation is the restriction of a split cut for the mixedinteger infinite relaxation, which further indicates the universality of the latter. As another consequence, we construct a pureinteger set with arbitrarily weak split closure, giving a pureinteger counterpart of the mixedinteger construction from [27].

6 
A Computer Simulation Model of Municipal Resource AllocationCrecine, John P. 01 May 1966 (has links)
This report contains a description of a particular kind of governmental decision making “ the decisional behavior connected with municipal operating budgets. The theory is stated in the language of a computer program. The model of governmental problem solving is applied to the cities of Cleveland, Detroit, and Pittsburgh. The Model, with appropriate parameter estimates, is used to reproduce operating budget decisions in the three cities* In addition various naive models are tested and then compared with our decision Model (Section 6). Analysis of model residuals (Section 7) and a sensitivity analysis of some model parameters (Section 10) • is used to study "unprogrammed" aspects of budgetary behavior. The formal Model (presented in Section 4) is also related to various theories of individual and organizational behavior in Section 9. The behavioral antecedents of the model are discussed in Section 5* Implications o r the study for political research are touched on in Section 11 and our positive model forms the basis for a normative discussion of municipal resource allocation in Section 12.

7 
Studies of Bounded Rationality and Overconfidence in Dynamic GamesCutler, Jennifer January 2013 (has links)
<p>\abstract</p><p>Managers constantly make decisions that depend, at least in part, on what they believe their competitors (or customers, or employees) will do in response. These judgments are susceptible to error. Indeed, behavioral research suggests a widespread bias towards underestimating others. To explore the ramifications of such an intuitively irrational bias, we provide a theoretical model that compares the longrun effects of consistent underestimation bias with those of consistent overestimation bias across different competitive contexts relevant to marketing managers. In the first set of analyses, we derive analytic equations to calculate the relative expected payoffs associated with conditions of overestimation bias, underestimation bias, and no bias and discern trends as a function of environmental features including game complexity and player skill levels. In the second set of analyses, we derive equations for the relative effort costs associated with each of the three bias conditions as a function of the relevant game and player parameters. We then combine the results of the expected payoffs and effort costs to determine the relative net expected payoffs associated with each bias condition as a function of game and player parameters. In the third set of analyses, we relax many of the assumptions present in the first analysis and test the relationships of interest across additional contexts including risk aversion, power imbalance, and opponent arrogance. The results across all analyses are summarized by fifteen propositions which show that, when effort is at all costly, underestimation will provide the best net expected payoff when games are above some critical level of complexity as well as when opponents have above some critical minimum level of skill. The range of underestimation's advantage compared to overestimation can be increased in contexts with high first mover advantage, when payoffs are cumulative over time, when the player is risk averse, when there is an imbalance in power between the players, and when the opponent exhibits arrogance. Furthermore, underestimation can outperform overestimation even when effort is not costly when there is a power imbalance between the players or when the player exhibits certain risk attitudes. The results provide theoretical support for the ecological rationality of underestimation bias by showing it to be advantageous under many conditions, particularly in comparison to overestimation bias. It also provides managers with prescriptive insights regarding when any opponent skill estimation error is more vs. less harmful, and when managers may fare better in the long run if they don't spend too much time trying to think through the competition's eyes..</p> / Dissertation

8 
Identifying Search SpaceDutt, Nilanjana January 2013 (has links)
<p>This dissertation studies how organizations, when solving a specific problem, identify a set of potential solutions which we call "Search Space." By drawing from evolutionary theory and related literatures on strategic change, scholars have demonstrated differences in search mechanisms that explain how organizations choose solutions. However, we still face unanswered questions in understanding how organizations decide where to search, including how organizations identify a set of potential solutions or Search Space. This dissertation defines the concept of Search Space and identifies three factors  uncertainty, prior top managerial attention, and prior experience that drive differences in Search Space. Additionally, this dissertation starts to disentangle why some firms' top managers are predisposed to paying more attention to new strategic areas by investigating the relationship between uncertainty and top managerial attention. Hypotheses first testing the effect of uncertainty, prior top managerial attention, and prior experience on size of Search Space, and second testing the effect of uncertainty on changes in top managerial attention are tested using data describing the U.S. renewable electricity sector from 2000 to 2010. We conduct both a crosssectional analysis using data collected though a multiple respondent survey and a panel data analysis by tracking firms' memberships in renewable electricity trade groups. We find that uncertainty and prior top managerial attention increase size of Search Space, but related prior experience reduces size of Search Space. Additionally, uncertainty positively changes attention of top managers at headquarter units, but not at subsidiary units, towards renewable electricity. These results contribute to our understanding of how organizations start solving problems by deciding where to search; how the boundaries of top managerial attention direct Search Space; and how different types of top managers interpret uncertainty. Empirically, these results have important implications for how renewable policies should be structured and how firms develop new projects in the U.S. renewable electricity sector.</p> / Dissertation

9 
Antitrust practices and competition in the steel industry: a critical analysisWilson, Timothy Douglas 01 August 1965 (has links)
No description available.

10 
Inventory pricing and its influence on financial statementsYoung, Leydon A 01 August 1966 (has links)
No description available.

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