Within the artificial intelligence subfield of multiagent systems, one challenge that arises is determining how to efficiently allocate resources to all agents in a way that maximizes the overall expected utility. In this thesis, we explore a distributed solution to this problem, one in which the agents work together to coordinate their requests for resources and which is considered to be ex-ante rational: in other words, requiring agents to be willing to give up their current resources to those with greater need by reasoning about what is for the common good. Central to our solution is allowing for preemption of tasks that are currently occupying resources; this is achieved by introducing a concept from adjustable autonomy multiagent systems known as a transfer of control (TOC) strategy. In essence a TOC strategy is a plan of an agent to acquire resources at future times, and can be used as a contingency plan that an agent will execute if it loses its current resource. The inclusion of TOC strategies ultimately provides for a greater optimism among agents about their future resource acquisitions, allowing for more generous behaviours, and for agents to more frequently agree to relinquish current resources, resulting in more effective preemption policies. Three central contributions arise. The first is an improved methodology for generating transfer of control strategies efficiently, using a dynamic programming approach, which enables a more effective employment of TOCs in our resource allocation solution. The second is an important clarification of the value of integrating learning techniques in order for agents to acquire improved estimates of the costs of preemption. The last is a validation of the overall multiagent resource allocation (MARA) solution, using simulations which show quantifiable benefits of our novel approach. In particular, we consider in detail the emergency medical application of mass casualty incidents and are able to demonstrate that our approach of integrating transfer of control strategies results in effective allocation of patients to doctors: ones which in simulations re- sult in dramatically fewer patients in a critical healthstate than are produced by competing MARA algorithms. In short, we offer a principled solution to the problem of preemption, allowing the elimination of a source of inefficiencies in fully distributed multiagent resource allocation systems; a faster method for generation of transfer of control strategies; and a convincing application of the system to a real world problem where human lives are at stake.

Quality-of-service (QoS) guarantees will play a critically important role in future mobile wireless networks. In this dissertation, we study a set of QoS-driven resource allocation problems for mobile wireless communications and networks. In the first part of this dissertation, we investigate resource allocation schemes for statistical QoS provisioning. The schemes aim at maximizing the system/network throughput subject to a given queuing delay constraint. To achieve this goal, we integrate the information theory with the concept of effective capacity and develop a unified framework for resource allocation. Applying the above framework, we con-sider a number of system infrastructures, including single channel, parallel channel, cellular, and cooperative relay systems and networks, respectively. In addition, we also investigate the impact of imperfect channel-state information (CSI) on QoS pro-visioning. The resource allocation problems can be solved e±ciently by the convex optimization approach, where closed-form allocation policies are obtained for different application scenarios. Our analyses reveal an important fact that there exists a fundamental tradeoff between throughput and QoS provisioning. In particular, when the delay constraint becomes loose, the optimal resource allocation policy converges to the water-filling scheme, where ergodic capacity can be achieved. On the other hand, when the QoS constraint gets stringent, the optimal policy converges to the channel inversion scheme under which the system operates at a constant rate and the zero-outage capacity can be achieved. In the second part of this dissertation, we study adaptive antenna selection for multiple-input-multiple-output (MIMO) communication systems. System resources such as subcarriers, antennas and power are allocated dynamically to minimize the symbol-error rate (SER), which is the key QoS metric at the physical layer. We propose a selection diversity scheme for MIMO multicarrier direct-sequence code- division-multiple-access (MC DS-CDMA) systems and analyze the error performance of the system when considering CSI feedback delay and feedback errors. Moreover, we propose a joint antenna selection and power allocation scheme for space-time block code (STBC) systems. The error performance is derived when taking the CSI feedback delay into account. Our numerical results show that when feedback delay comes into play, a tradeoff between performance and robustness can be achieved by dynamically allocating power across transmit antennas.

Multichannel marketing is the practice of simultaneously offering information, goods, services, and support to customers through two or more synchronized channels. In this dissertation, I develop an integrated framework of multichannel marketing and develop models to assist managers in their marketing resource allocation decisions. In the first essay of the dissertation, I investigate the factors that drive customers multichannel shopping behavior and identify its consequences for retailers. In the second essay, I build on this work and develop a model that enables firms to optimize their allocation of marketing resources across different customer-channel segments. In the first essay, I develop a framework comprising the factors that drive consumers’ channel choice, the consequences of channel choice, and their implications for managing channel equity. The results show that customer-channel choice is driven in a nonlinear fashion by a customer demographic variable such as age and is also influenced by consumer shopping traits such as number of categories bought and the duration of relationship with a retailer. I show that by controlling for the moderating effects of channel-category associations, the influence of customers’ demographics and shopping traits on their channel choices can vary significantly across product categories. Importantly, the results show that multichannel shoppers buy more often, buy more items, and spend considerably more than single channel shoppers. The channel equity of multichannel customers is nearly twice that of the closest single channel customers (online or offline). In the second essay, I propose a model for optimal allocation of marketing efforts across multiple customer-channel segments. I first develop a set of models for consumer response to marketing efforts for each channel-customer segment. This set comprises four models, the first for purchase frequency, the second for purchase quantity, the third for product return behavior, and the fourth for contribution margin of purchase. The results show that customers’ responses to firm marketing efforts vary significantly across the customer-channel segments. They also suggest that marketing efforts influence purchase frequency, purchase quantity and monetary value in different ways. The resource allocation results show that profits can be substantially improved by reallocating marketing efforts across the different customer-channel segments.

In order to maximize the capacities of resources constraints in the multiple projects environment, it is firstly necessary to make sure where the resources constraints are, and to schedule them. And then, add a set of time buffer to protect the bottle-neck resources. For some purposes, the project schedule is not easy to be altered in enterprises. Instead of adding a set of time buffer, we use others ways to protect resource constraints and to improve capacities. 4 cases are discussed in this research respectively, and the characteristics in this research contain whether the project plan is announced at the year beginning, whether the number of team members is fixed, and whether the project is outsourced or in-house. The main purpose is to investigate how the enterprises arrange the resources in 3 different periods: the projects at the year beginning, new projects joined during a year , and new demands in the existing projects during a year. The research results show: (1) Enterprises usually recruit employees at the beginning of the year, which prevents from the unqualified human resource as the projects going. (2) The teams with fixed member are allocated members in the projects which are the same domain. It¡¦s not easy to support between projects of different domains in the same team, except IT support. It¡¦s also difficult to support between teams, because the relationship of teams is competitive. (3) In the established team in terms of projects, enterprises assign team members in project which are the same domain by the function. It¡¦s easy to support between members with the same domain. It¡¦s not easy to support between members with different domain, except IT support. (4) Carrying out outsourcing projects in the enterprises, complete project in the different period to explore the resource constraints. (5) Carrying out in-house projects in the enterprise, reduce insignificance project scope or to reschedule insignificance project to explore the resource constraints. (6) Carrying outsourcing and in-house projects in the enterprises, reduce insignificance project scope or to reschedule insignificance project to explore the resource constraints.

Thesis (M.A.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains vi, 56 p. Vita. Includes abstract. Includes bibliographical references (p. 40-43).

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis--Auburn University, 2009. / Abstract. Includes bibliographic references (p.70-72).

Unlike traditional cellular networks, where voice calls dominate the network traffic, modern mobile traffic is created by of a mixture of both voice and broadband data services. The heterogeneous mixture of voice and data services in mobile broadband networks includes voice calls, web browsing, file transfers, video streaming, and social media applications. Consequently, network planning and radio resource management strategies must be aware of the quality of experience perceived by the users using various types of applications. In this report, we explore the traffic characteristics, scheduling and resource allocation strategies, and user experience models in mobile broadband networks. / text

This dissertation consists of three chapters. Chapter one estimates the capital adjustment costs at the plant level in a model entry and exit. We find that the estimated variance of plant-specific productivity shock is larger than obtained from balanced panel estimation. Estimation using the unbalanced panel generates a larger irreversibility cost, a smaller disruption cost, and a smaller convex cost, all compared with the estimates by Cooper and Haltiwanger (2006). In chapter two, we study how much of the aggregate productivity changes can be accounted for by the capital reallocation. We also study the impact of capital reallocation on the productivity dispersion across firms. We find that the capital reallocations accounts for roughly 12 percent of the labor productivity and capital productivity are reduced as the reallocation activity increases. When the economy-wide technology has a positive change, the reallocation increases temporarily then drops to its original level. After a short transition, the economy settles down with an increased labor productivity. Chapter three further studies the quantitative role of allocation, entry and exit in the growth of aggregate productivity. We find that, without including in the model the forces that drive the entry and exit changes, the model economy has a modest increase in the aggregate productivity as a result of decrease in the fixed reallocation cost.

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