This study is directed to the problem of the quantitative determination of the number of additional personnel required in an organization for servicing unscheduled priority tasks without delaying the completion of scheduled tasks. The manager of an organization which has been enlarged in order to respond adequately to the random arrival of priority requests may face criticism if the organization appears to be "overstaffed" during periods when only "routine" service requirements must be met. An audit team oriented toward accounting-type data may be reluctant to accept a manager's justification of his organization's size if the justification is based primarily on nonquantitative arguments.

Call number: LD2668 .T4 1986 O77 / Master of Science / Biological and Agricultural Engineering

Thesis (M.M. (Finance & Investment)--University of the Witwatersrand, Faculty of Commerce, Law and Management, Wits Business School, 2016 / The study is an assessment of the current treatment of business risk, as a significant risk type for financial institutions. It includes an industry analysis of the five major banks in South Africa, as well as international banks, and how these banks currently manage business risk in the Pillar 2 supervisory process. It assesses economic capital frameworks and the importance of business risk in the risk assessment and measurement process in the global and local industry. Various methodologies have been researched to assess which statistical methods are best suited in the measurement of this risk type as well as the quantification of the capital levels required. This study has compared the available statistical methodologies currently used in the industry and concludes which is best given the issues pertaining to the modelling of business risk quantification. A statistical model has been developed to quantify business risk for a specific bank using bank specific data, using a methodology which is relatively generic and could be applied widely across all financial institutions. The model serves to illustrate the principles surrounding the quantification of business risk economic capital. / GR2018

We generalize the theory of stochastic impulse control of jump diffusions introduced by Oksendal and Sulem (2004) with milder assumptions. In particular, we assume that the original process is affected by the interventions. We also generalize the optimal central bank intervention problem including market reaction introduced by Moreno (2007), allowing the exchange rate dynamic to follow a jump diffusion process. We furthermore generalize the approximation theory of stochastic impulse control problems by a sequence of iterated optimal stopping problems which is also introduced in Oksendal and Sulem (2004). We develop new results which allow us to reduce a given impulse control problem to a sequence of iterated optimal stopping problems even though the original process is affected by interventions. / by Sandun C. Perera. / Thesis (Ph.D.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

We study systems of n dimensional diffusions whose drift and dispersion coefficients depend only on the relative ranking of the processes. We consider the question of how long it takes for a particle to go from one rank to another. It is argued that as n gets large, the distribution of particles satisfies a Porous Medium Equation. Using this, we derive a deterministic limit for the system of particles. This limit allows for direct calculation of the properties of the rank traversal time. The results are extended to the case of asymmetrically colliding particles. These models are of interest in the study of financial markets and economic inequality. In particular, we derive limits for the performance of some Functionally Generated Portfolios originating from Stochastic Portfolio Theory.

This research focuses on fleet management in freight transportation systems. Effective management requires effective planning and control decisions. Plans are often generated using estimates of how the system will evolve in the future; during execution, control decisions need to be made to account for differences between actual realizations and estimates. The benefits of minimum cost plans can be negated by performing costly adjustments during the operational phase. A planning approach that permits effective control during execution is proposed in this dissertation. This approach is inspired by recent work in robust optimization, and is applied to (i) dynamic asset management and (ii) vehicle routing problems. In practice, the fleet management planning is usually decomposed in two parts; the problem of repositioning empty, and the problem of allocating units to customer demands. An alternative integrated dynamic model for asset management problems is proposed. A computational study provides evidence that operating costs and fleet sizes may be significantly reduced with the integrated approach. However, results also illustrate that not considering inherent demand uncertainty generates fragile plans with potential costly control decisions. A planning approach for the empty repositioning problem is proposed that incorporates demand and supply uncertainty using interval around nominal forecasted parameters. The intervals define the uncertainty space for which buffers need to be built into the plan in order to make it a robust plan. Computational evidence suggests that this approach is tractable. The traditional approach to address the Vehicle Routing Problem with Stochastic Demands (VRPSD) is through cost expectation minimization. Although this approach is useful for building routes with low expected cost, it does not directly consider the maximum potential cost that a vehicle might incur when traversing the tour. Our approach aims at minimizing the maximum cost. Computational experiments show that our robust optimization approach generates solutions with expected costs that compare favorably to those obtained with the traditional approach, but also that perform better in worst-case scenarios. We also show how the techniques developed for this problem can be used to address the VRPSD with duration constraints.

No description available.

An important requirement of a rational policy for provision of outdoor recreation opportunities is some understanding of natural processes and public concern and /or preferences. Computerized land use suitability mapping is a technique which can help find the best location for a variety of developmental actions given a set of goals and other criteria. Over the past two decades, the methods and techniques of land use planning have been engaged in a revolution on at least two fronts as to shift the basic theories and attitudes of which land use decisions are based. The first of these fronts is the inclusion of environmental concerns, and the second is the application of more systematic methods or models. While these automated capabilities have shed new light on environmental issues, they, unfortunately, have failed to develop sufficient intelligence and adaptation to accurately model the dynamics of ecosystems.The work reported proceeds on the belief that neural network models can be used to assess and develop resource management strategies for Mounds State Park, Anderson, Indiana. The study combines a photographic survey technique with a geographic information system (GIS) and artificial neural networks (NN) to investigate the perceived impact of park management activities on recreation opportunities and experiences. It is unique in that it incorporates both survey data with spatial data and an optimizing technique to develop a model for predicting perceived management values for short and long term recreation management.According to Jeannette Stanley and Evan Bak (1988) a neural network is a massively parallel, dynamic systems of highly interconnected interacting parts based on neurobiological models. The behavior of the network depends heavily on the connection details. The state of the network evolves continually with time. Networks are considered clever and intuitive because they learn by example rather than following simple programming rules. They are defined by a set of rules or patterns based on expertise or perception for better decision making. With experience networks become sensitive to subtle relationships in the environment which are not obvious to humans.The model was developed as a counter-propagation network with a four layer learning network consisting of an input layer, a normalized layer, a kohonen layer, and an output layer. The counter-propagation network is a feed-forward network which combines Kohonen and Widrow-Hoff learning rules for a new type of mapping neural network. The network was trained with patterns derived by mapping five variables (slope, aspect, vegetation, soil, site features) and survey responses from three groups. The responses included, for each viewshed, the preference and management values, and three recreational activities each group associated with a given landscape. Overall the model behaves properly in learning the different rules and generalizing in cases where inputs had not been shown to the network apriori. Maps are provided to illustrate the different responses obtained from each group and simulated by the model. The study is not conclusive as to the capabilities of the combination of GIS techniques and neural networks, but it gives a good flavor of what can be achieved when accurate mapping information is used by an intelligent system for decision making. / Department of Landscape Architecture

Erratum pages inserted onto front end papers. / Bibliography: leaves 226-244. / vi, 244 leaves ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This thesis developed mathematical models of commercially exploited fish populations, addressing the question of how to harvest a predator-prey metapopulation. Optimal harvesting strategies are found using dynamic programming and Lagrange multipliers. Rules about harvesting source/sink populations, more/less vulnerable prey subpopulations and more/less efficient predator subpopulations are explored. Strategies for harvesting critical prey subpopulations are suggested. / Thesis (Ph.D.)--University of Adelaide, Dept. of Applied Mathematics, 2000?

This dissertation studies the pricing of Outside barrier call options, when their activation starts at a hitting time. The pricing of Outside barrier options when their activation starts at time zero, and the pricing of standard barrier options when their activation starts at a hitting time of a pre speci ed barrier level, have been studied previously (see [21], [24]). The new work that this dissertation will do is to price Outside barrier call options, where they will be activated when the triggering asset crosses or hits a pre speci ed barrier level, and also the pricing of Outside barrier call options where they will be activated when the triggering asset crosses or hits a sequence of two pre specifed barrier levels. Closed form solutions are derived using Girsanov's theorem and the re ection principle. Existing results are derived from the new results, and properties of the new results are illustrated numerically and discussed. / Mathematical Sciences / M. Sc. (Applied Mathematics)