VTechWorks Repository :: Browsing by Author "Taaffe, Michael R."

Browsing by Author "Taaffe, Michael R."

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Adaptive Sampling Line Search for Simulation Optimization
Ragavan, Prasanna Kumar (Virginia Tech, 2017-03-08)
This thesis is concerned with the development of algorithms for simulation optimization (SO), a special case of stochastic optimization where the objective function can only be evaluated through noisy observations from a simulation. Deterministic techniques, when directly applied to simulation optimization problems fail to converge due to their inability to handle randomness thus requiring sophisticated algorithms. However, many existing algorithms dedicated for simulation optimization often show poor performance on implementation as they require extensive parameter tuning. To overcome these shortfalls with existing SO algorithms, we develop ADALINE, a line search based algorithm that eliminates the need for any user defined parameters. ADALINE is designed to identify a local minimum on continuous and integer ordered feasible sets. ADALINE on a continuous feasible set mimics deterministic line search algorithms, while it iterates between a line search and an enumeration procedure on integer ordered feasible sets in its quest to identify a local minimum. ADALINE improves upon many of the existing SO algorithms by determining the sample size adaptively as a trade-off between the error due to estimation and the optimization error, that is, the algorithm expends simulation effort proportional to the quality of the incumbent solution. We also show that ADALINE converges ``almost surely'' to the set of local minima. Finally, our numerical results suggest that ADALINE converges to a local minimum faster, outperforming other advanced SO algorithms that utilize variable sampling strategies. To demonstrate the performance of our algorithm on a practical problem, we apply ADALINE in solving a surgery rescheduling problem. In the rescheduling problem, the objective is to minimize the cost of disruptions to an existing schedule shared between multiple surgical specialties while accommodating semi-urgent surgeries that require expedited intervention. The disruptions to the schedule are determined using a threshold based heuristic and ADALINE identifies the best threshold levels for various surgical specialties that minimizes the expected total cost of disruption. A comparison of the solutions obtained using a Sample Average Approximation (SAA) approach, and ADALINE is provided. We find that the adaptive sampling strategy in ADALINE identifies a better solution quickly than SAA.
Algorithms and Architectures for UWB Receiver Design
Ibrahim, Jihad E. (Virginia Tech, 2007-01-25)
Impulse-based Ultra Wideband (UWB) radio technology has recently gained significant research attention for various indoor ranging, sensing and communications applications due to the large amount of allocated bandwidth and desirable properties of UWB signals (e.g., improved timing resolution or multipath fading mitigation). However, most of the applications have focused on indoor environments where the UWB channel is characterized by tens to hundreds of resolvable multipath components. Such environments introduce tremendous complexity challenges to traditional radio designs in terms of signal detection and synchronization. Additionally, the extremely wide bandwidth and shared nature of the medium means that UWB receivers must contend with a variety of interference sources. Traditional interference mitigation techniques are not amenable to UWB due to the complexity of straight-forward translations to UWB bandwidths. Thus, signal detection, synchronization and interference mitigation are open research issues that must be met in order to exploit the potential benefits of UWB systems. This thesis seeks to address each of these three challenges by first examining and accurately characterizing common approaches borrowed from spread spectrum and then proposing new methods which provide an improved trade-off between complexity and performance.
Analysis and Approximations of Time Dependent Queueing Models
Nasr, Walid (Virginia Tech, 2008-01-18)
Developing equations to compute congestion measures for the general G/G/s/c queueing model and networks of such nodes has always been a challenge. One approach to analyzing such systems is to approximate the model-specified general input processes and distributions by processes and distributions from the more computationally friendly family of phase-type processes and distributions. We develop numerical approximation methods for analysis of general time-dependent queueing nodes by introducing new approximations for the time-dependent first two moments of the number-in-system and departure-count processes.
Approximating Deterministic Changes to Ph(t)/Ph(t)/1/c and Ph(t)/M(t)/s/c Queueing Models
Kulkarni, Aditya Umesh (Virginia Tech, 2012-05-25)
A deterministic change to a time-varying queueing model is described as either changing the number of entities, the queue capacity, or the number of servers in the system at selected times. We use a surrogate distribution for N(t), the number of entities in the system at time t, to approximate deterministic changes to the Ph(t)/Ph(t)/1/c and the Ph(t)/M(t)/s/c queueing models. We develop a solution technique to minimize the number of state probabilities to be approximated.
Consistency and Uniform Bounds for Heteroscedastic Simulation Metamodeling and Their Applications
Zhang, Yutong (Virginia Tech, 2023-09-05)
Heteroscedastic metamodeling has gained popularity as an effective tool for analyzing and optimizing complex stochastic systems. A heteroscedastic metamodel provides an accurate approximation of the input-output relationship implied by a stochastic simulation experiment whose output is subject to input-dependent noise variance. Several challenges remain unsolved in this field. First, in-depth investigations into the consistency of heteroscedastic metamodeling techniques, particularly from the sequential prediction perspective, are lacking. Second, sequential heteroscedastic metamodel-based level-set estimation (LSE) methods are scarce. Third, the increasingly high computational cost required by heteroscedastic Gaussian process-based LSE methods in the sequential sampling setting is a concern. Additionally, when constructing a valid uniform bound for a heteroscedastic metamodel, the impact of noise variance estimation is not adequately addressed. This dissertation aims to tackle these challenges and provide promising solutions. First, we investigate the information consistency of a widely used heteroscedastic metamodeling technique, stochastic kriging (SK). Second, we propose SK-based LSE methods leveraging novel uniform bounds for input-point classification. Moreover, we incorporate the Nystrom approximation and a principled budget allocation scheme to improve the computational efficiency of SK-based LSE methods. Lastly, we investigate empirical uniform bounds that take into account the impact of noise variance estimation, ensuring an adequate coverage capability.
Design Space Decomposition for Cognitive and Software Defined Radios
Fayez, Almohanad Samir (Virginia Tech, 2013-06-07)
Software Defined Radios (SDRs) lend themselves to flexibility and extensibility because they
depend on software to implement radio functionality. Cognitive Engines (CEs) introduce
intelligence to radio by monitoring radio performance through a set of meters and configuring
the underlying radio design by modifying its knobs. In Cognitive Radio (CR) applications,
CEs intelligently monitor radio performance and reconfigure them to meet it application
and RF channel needs. While the issue of introducing computational knobs and meters
is mentioned in literature, there has been little work on the practical issues involved in
introducing such computational radio controls.

This dissertation decomposes the radio definition to reactive models for the CE domain
and real-time, or dataflow models, for the SDR domain. By allowing such design space
decomposition, CEs are able to define implementation independent radio graphs and rely on
a model transformation layer to transform reactive radio models to real-time radio models
for implementation. The definition of knobs and meters in the CE domain is based on
properties of the dataflow models used in implementing SDRs. A framework for developing
this work is presented, and proof of concept radio applications are discussed to demonstrate
how CEs can gain insight into computational aspects of their radio implementation during
their reconfiguration decision process.
Designing Order Picking Systems for Distribution Centers
Parikh, Pratik J. (Virginia Tech, 2006-09-01)
This research addresses decisions involved in the design of an order picking system in a distribution center. A distribution center (DC) in a logistics system is responsible for obtaining materials from different suppliers and assembling (or sorting) them to fulfill a number of different customer orders. Order picking, which is a key activity in a DC, refers to the operation through which items are retrieved from storage locations to fulfill customer orders. Several decisions are involved when designing an order picking system (OPS). Some of these decisions include the identification of the picking-area layout, configuration of the storage system, and determination of the storage policy, picking method, picking strategy, material handling system, pick-assist technology, etc. For a given set of these parameters, the best design depends on the objective function (e.g., maximizing throughout, minimizing cost, etc.) being optimized. The overall goal of this research is to develop a set of analytical models for OPS design. The idea is to help an OPS designer to identify the best performing alternatives out of a large number of possible alternatives. Such models will complement experienced-based or simulation-based approaches, with the goal of improving the efficiency and efficacy of the design process. In this dissertation we focus on the following two key OPS design issues: configuration of the storage system and selection between batch and zone order picking strategies. Several factors that affect these decisions are identified in this dissertation; a common factor amongst these being picker blocking. We first develop models to estimate picker blocking (Contribution 1) and use the picker blocking estimates in addressing the two OPS design issues, presented as Contributions 2 and 3. In Contribution 1 we develop analytical models using discrete-time Markov chains to estimate pick-face blocking in wide-aisle OPSs. Pick-face blocking refers to the blocking experienced by a picker at a pick-face when another picker is already picking at that pick-face. We observe that for the case when pickers may pick only one item at a pick-face, similar to in-the-aisle blocking, pick-face blocking first increases with an increase in pick-density and then decreases. Moreover, pick-face blocking increases with an increase in the number of pickers and pick to walk time ratio, while it decreases with an increase in the number of pick-faces. For the case when pickers may pick multiple items at a pick-face, pick-face blocking increases monotonically with an increase in the pick-density. These blocking estimates are used in addressing the two OPS design issues, which are presented as Contributions 2 and 3. In Contribution 2 we address the issue of configuring the storage system for order picking. A storage system, typically comprised of racks, is used to store pallet-loads of various stock keeping units (SKU) --- a SKU is a unique identifier of products or items that are stored in a DC. The design question we address is related to identifying the optimal height (i.e., number of storage levels), and thus length, of a one-pallet-deep storage system. We develop a cost-based optimization model in which the number of storage levels is the decision variable and satisfying system throughput is the constraint. The objective of the model is to minimize the system cost, which is comprised of the cost of labor and space. To estimate the cost of labor we first develop a travel-time model for a person-aboard storage/retrieval (S/R) machine performing Tchebyshev travel as it travels in the aisle. Then, using this travel-time model we estimate the throughput of each picker, which helps us estimate the number of pickers required to satisfy the system throughput for a given number of storage levels. An estimation of the cost of space is also modeled to complete the total cost model. Results from an experimental study suggest that a low (in height) and long (in length) storage system tends to be optimal for situations where there is a relatively low number of storage locations and a relatively high throughput requirement; this is in contrast with common industry perception of the higher the better. The primary reason for this contrast is because the industry does not consider picker blocking and vertical travel of the S/R machine. On the other hand, results from the same optimization model suggest that a manual OPS should, in almost all situations, employ a high (in height) and short (in length) storage system; a result that is consistent with industry practice. This consistency is expected as picker blocking and vertical travel, ignored in industry, are not a factor in a manual OPS. In Contribution 3 we address the issue of selecting between batch and zone picking strategies. A picking strategy defines the manner in which the pickers navigate the picking aisles of a storage area to pick the required items. Our aim is to help the designer in identifying the least expensive picking strategy to be employed that meets the system throughput requirements. Consequently, we develop a cost model to estimate the system cost of a picking system that employs either a batch or a zone picking strategy. System cost includes the cost of pickers, equipment, imbalance, sorting system, and packers. Although all elements are modeled, we highlight the development of models to estimate the cost of imbalance and sorting system. Imbalance cost refers to the cost of fulfilling the left-over items (in customer orders) due to workload-imbalance amongst pickers. To estimate the imbalance cost we develop order batching models, the solving of which helps in identifying the number of items unfulfilled. We also develop a comprehensive cost model to estimate the cost of an automated sorting system. To demonstrate the use of our models we present an illustrative example that compares a sort-while-pick batch picking system with a simultaneous zone picking system. To summarize, the overall goal of our research is to develop a set of analytical models to help the designer in designing order picking systems in a distribution center. In this research we focused on two key design issues and addressed them through analytical approaches. Our future research will focus on addressing other design issues and incorporating them in a decision support system.
Development of Wastewater Pipe Performance Index and Performance Prediction Model
Angkasuwansiri, Thiti (Virginia Tech, 2013-06-11)
Water plays a critical role in every aspect of civilization: agriculture, industry, economy, environment, recreation, transportation, culture, and health. Much of America's drinking water and wastewater infrastructure; however, is old and deteriorating. A crisis looms as demands on these systems increase. The costs associated with renewal of these aging systems are staggering. There is a critical disconnect between the methodological remedies for infrastructure renewal problems and the current sequential or isolated manner of renewal analysis and execution. This points to the need for a holistic systems perspective to address the renewal problem. Therefore, new tools are needed to provide support for wastewater infrastructure decisions. Such decisions are necessary to sustain economic growth, environmental quality, and improved societal benefits. Accurate prediction of wastewater pipe structural and functional deterioration plays an essential role in asset management and capital improvement planning. The key to implementing an asset management strategy is a comprehensive understanding of asset condition, performance, and risk profile. The primary objective of this research is therefore to develop protocols and methods for evaluating the wastewater pipe performance. This research presents the life cycle of wastewater pipeline identifying the causes of pipe failure in different phases including design, manufacture, construction, operation and maintenance, and repair/rehabilitation/replacement. Various modes and mechanisms of pipe failure in wastewater pipes were identified for different pipe material which completed with results from extensive literature reviews, and interviews with utilities and pipe associations. After reviewing all relevant reports and utility databases, a set of standard pipe parameter list (data structure) and a pipe data collection methodology were developed. These parameters includes physical/structural, operational/functional, environmental and other parameters, for not only the pipe, but also the entire pipe system. This research presents a development of a performance index for wastewater pipes. The performance index evaluates each parameter and combines them mathematically through a weighted summation and a fuzzy inference system that reflects the importance of the various factors. The performance index were evaluated based on artificial data and field data to ensure that the index could be implemented to real scenarios. Developing a performance index led to the development of a probabilistic performance prediction model for wastewater pipes. A framework would enable effective and systematic wastewater pipe performance evaluation and prediction in asset management programs.
A Dual Metamodeling Perspective for Design and Analysis of Stochastic Simulation Experiments
Wang, Wenjing (Virginia Tech, 2019-07-17)
Fueled by a growing number of applications in science and engineering, the development of stochastic simulation metamodeling methodologies has gained momentum in recent years. A majority of the existing methods, such as stochastic kriging (SK), only focus on efficiently metamodeling the mean response surface implied by a stochastic simulation experiment. As the simulation outputs are stochastic with the simulation variance varying significantly across the design space, suitable methods for variance modeling are required. This thesis takes a dual metamodeling perspective and aims at exploiting the benefits of fitting the mean and variance functions simultaneously for achieving an improved predictive performance. We first explore the effects of replacing the sample variances with various smoothed variance estimates on the performance of SK and propose a dual metamodeling approach to obtain an efficient simulation budget allocation rule. Second, we articulate the links between SK and least-square support vector regression and propose to use a ``dense and shallow'' initial design to facilitate selection of important design points and efficient allocation of the computational budget. Third, we propose a variational Bayesian inference-based Gaussian process (VBGP) metamodeling approach to accommodate the situation where either one or multiple simulation replications are available at every design point. VBGP can fit the mean and variance response surfaces simultaneously, while taking into full account the uncertainty in the heteroscedastic variance. Lastly, we generalize VBGP for handling large-scale heteroscedastic datasets based on the idea of ``transductive combination of GP experts.''
The Effect of Icing on the Dispatch Reliability of Small Aircraft
Gates, Melinda M. (Virginia Tech, 2004-10-16)
In 2000, the National Aeronautics and Space Administration (NASA) initiated a program to promote the use of small aircraft as an additional option for national public transportation. The Small Aircraft Transportation System (SATS) asserted the idea of everyday individuals piloting themselves on trips, within a specified distance range, using a small (4 person), piston powered, un-pressurized aircraft and small airports in close proximity to their origin and destination. This thesis investigates how one weather phenomenon, in-flight icing, affects the dispatch reliability of this transportation system. Specifically, this research presumes that a route is considered a "no-go" for low time pilots in a small, piston powered aircraft if any icing conditions are forecast along the route at the altitude of the flight during the time the traveler desires to make the trip. This thesis evaluates direct flights between Cleveland and Boston; Boston and Washington, D.C.; and Washington, D.C. and Cleveland during the months of November through May for the years 2001 to 2003 at maximum cruising altitudes of 6,000 feet, 8,000 feet, 10,000 feet, and 12,000 feet above mean sea level (MSL). It was found that the overall probability of a "no-go" for all three flight paths at the normal cruising altitude of 12,000 feet is 56.8%. When the cruising altitude is reduced to 10,000 feet, 8,000 feet, and 6,000 feet the probability of a "no-go" for all three flight paths reduces to 54.6%, 48.5%, and 43.7% respectively.
Efficient Resource Allocation Schemes for Wireless Networks with with Diverse Quality-of-Service Requirements
Kumar, Akshay (Virginia Tech, 2016-08-16)
Quality-of-Service (QoS) to users is a critical requirement of resource allocation in wireless networks and has drawn significant research attention over a long time. However, the QoS requirements differ vastly based on the wireless network paradigm. At one extreme, we have a millimeter wave small-cell network for streaming data that requires very high throughput and low latency. At the other end, we have Machine-to-Machine (M2M) uplink traffic with low throughput and low latency. In this dissertation, we investigate and solve QoS-aware resource allocation problems for diverse wireless paradigms. We first study cross-layer dynamic spectrum allocation in a LTE macro-cellular network with fractional frequency reuse to improve the spectral efficiency for cell-edge users. We show that the resultant optimization problem is NP-hard and propose a low-complexity layered spectrum allocation heuristic that strikes a balance between rate maximization and fairness of allocation. Next, we develop an energy efficient downlink power control scheme in a energy harvesting small-cell base station equipped with local cache and wireless backhaul. We also study the tradeoff between the cache size and the energy harvesting capabilities. We next analyzed the file read latency in Distributed Storage Systems (DSS). We propose a heterogeneous DSS model wherein the stored data is categorized into multiple classes based on arrival rate of read requests, fault-tolerance for storage etc. Using a queuing theoretic approach, we establish bounds on the average read latency for different scheduling policies. We also show that erasure coding in DSS serves the dual purpose of reducing read latency and increasing the energy efficiency. Lastly, we investigate the problem of delay-efficient packet scheduling in M2M uplink with heterogeneous traffic characteristics. We classify the uplink traffic into multiple classes and propose a proportionally-fair delay-efficient heuristic packet scheduler. Using a queuing theoretic approach, we next develop a delay optimal multiclass packet scheduler and later extend it to joint medium access control and packet scheduling for M2M uplink. Using extensive simulations, we show that the proposed schedulers perform better than state-of-the-art schedulers in terms of average delay and packet delay jitter.
Essays in Revenue Management and Dynamic Pricing
Yousef-Sibdari, Soheil (Virginia Tech, 2005-03-17)
In this dissertation, I study two topics in the context of revenue management. The First topic involves building a mathematical model to analyze the competition between many retailers who can change the price of their respective products in real time. I develop a game-theoretic model for the dynamic price competition where each retailer's objective is to maximize its own expected total revenue. I use the Nash equilibrium to predict market equilibrium and provide managerial insights into how each retailer should take into account its competitors' behavior when setting the price. The second topic involves working with Amtrak, the national railroad passenger corporation, to develop a revenue management model. The revenue management department of Amtrak provides the sales data of Auto Train, a service of Amtrak that allows passengers to bring their vehicles on the train. I analyze the demand structure from sales data and build a mathematical model to describe the sales process for Auto Train. I further develop an algorithm to calculate the optimal pricing strategy that yields the maximum revenue. Because of the distinctive service provided by Auto Train, my findings make important contribution to the revenue management literature.
Firm's Optimal Resource Portfolio under Consumer Choice, and Supply and Demand Risks
Chen, Weiping (Virginia Tech, 2007-07-20)
We study the optimal resource portfolio for a price-setter firm under a consumer choice model with supply and demand risks. The firm sells two products that are vertically differentiated, and has the option to invest in both dedicated and flexible resources. Our objective is to understand the effectiveness of the two hedging mechanisms, resource flexibility and demand management through production differentiation, under demand and supply risks. We show that the presence of consumer-driven substitution does not always reduce the need for the firm to offer differentiated products. In particular, when the firm faces demand risk and differential production costs, it might invest in the flexible resource and offer differentiated products for a wider range of parameters. Interestingly, more uncertainty (in the form of additional supply risk) does not always make the firm more eager to adopt a hedging mechanism. This depends on the relationship between resource risks, product attributes, and resource investment costs. On the other hand, when the firm invests in the flexible resource, this never completely replaces the dedicated resources, and always results in a "diverse" resource portfolio. While this happens in the supply risk setting mainly due to resource diversification advantage, it also happens in the demand risk setting due to the vertical differentiation between the products. Finally, in the absence of differential production costs, demand management by itself (without resource flexibility) becomes powerful enough to hedge against the demand risk, but not the supply risk, due to the additional resource diversification benefit of the flexible resource in the latter setting.
Fitting the Ph-t/M-t/s/c Time-Dependent Departure Process for Use in Tandem Queueing Networks
Nasr, W. W.; Taaffe, Michael R. (INFORMS, 2013)
This paper considers time-dependent Ph-t/M-t/s/c queueing nodes and small tandem networks of such nodes. We examine characteristics of the departure processes from a multiserver queueing node; in particular, we focus on solving for the first two time-dependent moments of the departure-count process. A finite set of partial moment differential equations is developed to numerically solve for the departure-count moments over specified intervals of time [t(i), t(i) + tau(i)). We also present a distribution fitting algorithm to match these key characteristics with a (Ph-t) over tilde process serving as the approximate departure process. A distribution fitting algorithm is presented for time-dependent point processes where a two-level balanced mixture of Erlang distribution is used to serve as the approximating process. We then use the (Ph-t) over tilde approximating departure process as the approximate composite arrival process to downstream node(s) in a network of tandem queues.
A Heuristic Approach to Solve Air Taxi Scheduling Problem
Chavan, Harish Dnyandeo (Virginia Tech, 2003-09-18)
All passengers travel at the hour most convenient to them. But it is not always possible to find a flight at the right time to fly them to their destination. In the case where service in any one time period is insufficient to meet air travel demanded, it may be expected that some unfilled demand passengers will either delay their flight or will advance it, thus adding to the effective demand of the adjoining time periods.The obvious alternate means of travel is a rental car. It takes a lot more time than flight, but it is readily available at any given time. This brings us to think of an airline system that will work in a similar fashion; A system that can be named an "Air Taxi System." This would mean a virtual highway in air space leading to a vast network. The network would be served by small aircraft flying from one city to another loading and unloading passengers. Such a large network having dynamic demand will have many issues to resolve before successfully launching a Small Aircraft Transportation System. One of the most important problems to solve is scheduling of aircraft for such a stochastic demand flow. The objective of the research is to study a given set of airports with dynamic demand and known aircraft type. The major task will be to analyze the flow of passengers between each origin-destination pair and then schedule flights. The research will be to develop a schedule for a fixed set of airports with dynamic demand and known type of aircraft. The main objective is to maximize demand satisfaction. The study will also analyze the number of aircraft required for a given set of airports and find a method to schedule them.
Modeling, Analysis, and Algorithmic Development of Some Scheduling and Logistics Problems Arising in Biomass Supply Chain, Hybrid Flow Shops, and Assembly Job Shops
Singh, Sanchit (Virginia Tech, 2019-07-15)
In this work, we address a variety of problems with applications to `ethanol production from biomass', `agile manufacturing' and `mass customization' domains. Our motivation stems from the potential use of biomass as an alternative to non-renewable fuels, the prevalence of `flexible manufacturing systems', and the popularity of `mass customization' in today's highly competitive markets. Production scheduling and design and optimization of logistics network mark the underlying topics of our work. In particular, we address three problems, Biomass Logistics Problem, Hybrid Flow Shop Scheduling Problem, and Stochastic Demand Assembly Job Scheduling Problem. The Biomass Logistics Problem is a strategic cost analysis for setup and operation of a biomass supply chain network that is aimed at the production of ethanol from switchgrass. We discuss the structural components and operations for such a network. We incorporate real-life GIS data of a geographical region in a model that captures this problem. Consequently, we develop and demonstrate the effectiveness of a `Nested Benders' based algorithm for an efficient solution to this problem. The Hybrid Flow Shop Scheduling Problem concerns with production scheduling of a lot over a two-stage hybrid flow shop configuration of machines, and is often encountered in `flexible manufacturing systems'. We incorporate the use of `lot-streaming' in order to minimize the makespan value. Although a general case of this problem is NP-hard, we develop a pseudo-polynomial time algorithm for a special case of this problem when the sublot sizes are treated to be continuous. The case of discrete sublot sizes is also discussed for which we develop a branch-and-bound-based method and experimentally demonstrate its effectiveness in obtaining a near-optimal solution. The Stochastic Demand Assembly Job Scheduling Problem deals with the scheduling of a set of products in a production setting where manufacturers seek to fulfill multiple objectives such as `economy of scale' together with achieving the flexibility to produce a variety of products for their customers while minimizing delivery lead times. We design a novel methodology that is geared towards these objectives and propose a Lagrangian relaxation-based algorithm for efficient computation.
Multiscale Decision Making for Multiple Decision Alternatives
Sudhaakar, Swathi Priyadarshini (Virginia Tech, 2013-01-24)
In organizations with decision makers across multiple hierarchical levels, conflicting objectives are commonly observed. The decision maker, or agent, at the highest level usually makes decisions in the interest of the organization, while a subordinate agent may have a conflict of interest between taking a course of action that is best for the organization and the course of action that is best for itself. The Multiscale Decision-Making (MSDM) model was established by Wernz (2008). The model has been developed to capture interactions in multi-agent systems, by integrating both the hierarchical and temporal scale of decisions made in organizations. This thesis contributes towards expanding the results in the hierarchical interaction domain of MSDM by extending the model to incorporate N decision alternatives and outcomes instead of two, and studying its effect on the interaction between agents. We consider decisions with uncertain outcomes, where the outcomes of the decisions made by agents lower in hierarchy affect the transition probabilities of the decisions made by agents above them in hierarchy. This leads to a game theoretic situation, where the lower-level agents need to be sufficiently incentivized in order to shift their best response strategy to one in the interest of their superior and the organization. Mathematical expressions for the optimal incentives at each hierarchical level are developed. We analyze systems with agents interacting across two and three organizational levels. We then study the effect of introducing the cost of taking an action on the optimal incentives. We discuss a health care application of MSDM.
Multiscale Views of Multi-agent Interactions in the Context Of Collective Behavior
Roy, Subhradeep (Virginia Tech, 2017-08-01)
In nature, many social species demonstrate collective behavior ranging from coordinated motion in flocks of birds and schools of fish to collective decision making in humans. Such distinct behavioral patterns at the group level are the consequence of local interactions among the individuals. We can learn from these biological systems, which have successfully evolved to operate in noisy and fault-prone environments, and understand how these complex interactions can be applied to engineered systems where robustness remains a major challenge. This dissertation addresses a two-scale approach to study these interactions- one in larger scale, where we are interested in the information exchange in a group and how it enables the group to reach a common decision, and the other in a smaller scale, where we are focused in the presence and directionality in the information exchange in a pair of individuals. To understand the interactions at large scale, we use a graph theoretic approach to study consensus or synchronization protocols over two types of biologically-inspired interaction networks. The first network captures both collaborative and antagonistic interactions and the second considers the impact of dynamic leaders in presence of purely collaborative interactions. To study the interactions at small scale, we use an information theoretic approach to understand the directionality of information transfer in a pair of individual using a real-world data-set of animal group motion. Finally, we choose the issue of same-sex marriage in the United States to demonstrate that collective opinion formation is not only a result of negotiations among the individuals, but also reflects inherent spatial and political similarities and temporal delays.
Node Selection, Synchronization and Power Allocation in Cooperative Wireless Networks
Baidas, Mohammed Wael (Virginia Tech, 2012-03-21)
Recently, there has been an increasing demand for reliable, robust and high data rate communication systems that can counteract the limitations imposed by the scarcity of two fundamental resources for communications: bandwidth and power. In turn, cooperative communications has emerged as a new communication paradigm in which network nodes share their antennas and transmission resources for distributed data exchange and processing. Recent studies have shown that cooperative communications can achieve significant performance gains in terms of signal reliability, coverage area, and power savings when compared with conventional communication schemes. However, the merits of cooperative communications can only be exploited with efficient resource allocation in terms of bandwidth utilization and power control. Additionally, the limited network resources in wireless environments can lead rational network nodes to be selfish and aim at maximizing their own benefits. Therefore, assuming fully cooperative behaviors such as unconditionally sharing of one's resources to relay for other nodes is unjustified. On the other hand, a particular network node may try to utilize resources from other nodes and also share its own resources so as to improve its own performance, which in turn may prompt other nodes to behave similarly and thus promote cooperation. This dissertation aims to answer the following three questions: ``How can bandwidth-efficient multinode cooperative communications be achieved?'', ``How can optimal power allocation be achieved in a distributed fashion?'', and finally, ``How can network nodes dynamically interact with each other so as to promote cooperation?''. In turn, this dissertation focuses on three main problems of cooperation in ad-hoc wireless networks: (i) optimal node selection in network-coded cooperative communications, (ii) auction-based distributed power allocation in single- and multi-relay cooperative networks, and finally (iii) coalitional game-theoretic analysis and modeling of the dynamic interactions among the network nodes and their coalition formations. Bi-directional relay networks are first studied in a scenario where two source nodes are communicating with each other via a set of intermediate relay nodes. The symbol error rate performance and achievable cooperative diversity orders are studied. Additionally, the effect of timing synchronization errors on the symbol error rate performance is investigated. Moreover, a sum-of-rates maximizing optimal power allocation is proposed. Relay selection is also proposed to improve the total achievable rate and mitigate the effect of timing synchronization errors. Multinode cooperative communications are then studied through the novel concept of many-to-many space-time network coding. The symbol error rate performance under perfect and imperfect timing synchronization and channel state information is theoretically analyzed and the optimal power allocation that maximizes the total network rate is derived. Optimal node selection is also proposed to fully exploit cooperative diversity and mitigate timing offsets and channel estimation errors. Further, this dissertation investigates distributed power allocation for single-relay cooperative networks. The distributed power allocation algorithm is conceived as an ascending-clock auction where multiple source nodes submit their power demands based on an announced relay price and are efficiently allocated cooperative transmit power. It is analytically and numerically shown that the proposed ascending-clock auction-based distributed algorithm leads to efficient power allocation, enforces truth-telling, and maximizes the social welfare. A distributed ascending-clock auction-based power allocation algorithm is also proposed for multi-relay cooperative networks. The proposed algorithm is shown to converge to the unique Walrasian Equilibrium allocation which maximizes the social welfare when source nodes truthfully report their cooperative power demands. The proposed algorithm achieves the same performance as could be achieved by centralized control while eliminating the need for complete channel state information and signaling overheads. Finally, the last part of the dissertation studies altruistic coalition formation and stability in cooperative wireless networks. Specifically, the aim is to study the interaction between network nodes and design a distributed coalition formation algorithm so as to promote cooperation while accounting for cooperation costs. This involves an analysis of coalitions' merge-and-split processes as well as the impact of different cooperative power allocation criteria and mobility on coalition formation and stability. A comparison with centralized power allocation and coalition formation is also considered, where the proposed distributed algorithm is shown to provide reasonable tradeoff between network sum-rate and computational complexity.
On the Impact of MIMO Implementations on Cellular Networks: An Analytical Approach from a Systems Perspective
Kim, Jong Han (Virginia Tech, 2007-03-19)
Multiple-input/multiple-output (MIMO) systems with the adaptive array processing technique, also referred to as smart antennas, have received extensive attention in wireless communications due to their ability to combat multipath fading and co-channel interference, two major channel impairments that degrade system performance. However, when smart antennas are deployed in wireless networks, careful attention is required since any defective or imperfect operation of smart antennas can severely degrade the performance of the entire network. Therefore, the evaluation of network performance under ideal and imperfect conditions is critical in the process of system design and should precede deploying smart antennas on the wireless network. This work focuses on the development of an analytical framework to evaluate the performance of wireless networks based on popular DS/CDMA cellular systems equipped with antenna arrays. Spatial diversity at both the base station (BS) and the mobile station (MS) is investigated through both analytical analysis and simulation. The main contribution of this research is to provide a comprehensive analytical framework for examining the system level performance with multiple antennas at both the BS and the MS. Using the framework developed in this research, system capacity and coverage of the uplink (or reverse link) are investigated when antenna arrays are implemented at both the BS and the MS. In addition, the system capacity and soft handoff capability of the downlink (or forward link) are examined taking into account MIMO. Furthermore, various physical and upper layer parameters that can affect the system level performance are taken into account in the analytical framework and their combined impact is evaluated. Finally, to validate the analytical analysis results, a system level simulator is developed and selective results are provided.

Browsing by Author "Taaffe, Michael R."

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