Evacuation planning is an important disaster management tool. A large-scale evacuation of a region by automobile is a difficult task, especially as demand is often greater than supply. This is made more difficult as the imbalance of supply and demand actually reduces supply due to congestion. Currently, most of the emphasis in evacuation planning is on supply management. The purpose of this dissertation is to introduce and study sophisticated demand management tools, specifically, staging and routing of evacuees. These tools can be used to produce evacuation strategies that reduce or eliminate congestion. A strategic planning model is introduced that accounts for evacuation dynamics and the non-linearities in travel times associated with congestion, yet is tractable and can be applied to large-scale networks. Objective functions of potential interest in evacuation planning are introduced and studied in the context of this model. Insights into the use of staging and routing in evacuation management are delineated and solution techniques are developed. Two different strategic approaches are studied in the context of this model. The first strategic approach is to control the evacuation at a disaggregate level, where customized staging and routing plans are produced for each individual or family unit. The second strategic approach is to control the evacuation at a more aggregate level, where evacuation plans are developed for a larger group of evacuees, based on pre-defined geographic areas. In both approaches, shelter requirements and preferences can also be considered. Computational experience using these two strategic approaches, and their respective solution techniques, is provided using a real network pertaining to Virginia Beach, Virginia, in order to demonstrate the efficacy of the proposed methodologies. / Ph. D.

We provide a comprehensive characterization of the relationship between optimal capacity and the degree of product substitution/complementarity under price/production postponement, considering different business practices (holdback versus clearance, negative price policies) and different demand models. Specifically, we consider a firm that produces two products, which can be substitutable or complementary. The demand of each product is a linear function of the prices of both products (with the relationship depending on the substitution/complementarity structure), and is subject to an additive stochastic shock. We consider two types of linear demand functions that are commonly used in the economics and operations management literature. The firm operates in a monopolistic setting and acts as a price-setter for both products. Overall the firm needs to make three sets of decisions: capacity, production quantities, and prices. While the capacity investment decision has to be made ex-ante observation of demand curves, price and/or quantity decisions can be postponed until after demand curves are observed. We consider two postponement strategies: price and quantity postponement, and price postponement only. We characterize the optimal pricing/production/investment decisions for each postponement strategy. Using these characterizations, we show that product substitution/complementarity is a key demand characteristic, which has a large impact on the optimal capacity. Our results show that how the optimal capacity behaves in substitution/complementarity parameter is quite similar under both postponement strategies, and under holdback and clearance. However, this behavior depends highly on other underlying assumptions (i.e., whether or not negative prices are allowed) and on the demand model used. / Ph. D.

Work-related musculoskeletal disorders (WMSDs) are prevalent among health care workers worldwide and underreporting among nurses may mask the true impact of these injuries. Nursing staff are consistently among the top 10 occupations at risk for experiencing WMSDs and patient-handling tasks are the precipitating event in the majority of back injuries experienced among nursing staff. Existing research has focused on patient-handling issues within long-term care facilities, and identifying physically demanding patient-handling tasks. The first study in this dissertation (Chapter 3) was conducted to determine whether nurses in acute care facilities are exposed to the same hazards as their cohorts in long-term care. The aim was to identify the top 10 patient-handling tasks being conducted and to rank these tasks by perceived physical demand. This two-phase study consisted of a procedural task analysis of patient-handling activities, and a questionnaire to identify the characteristics of the study population and obtain a ranking of physically demanding patient-handling tasks. All nurses providing direct inpatient care were recruited to participate in both phases of this study. Compared to long-term care facilities, in which the majority of tasks have been shown to be associated with performance of ADL tasks, the most frequently observed tasks in the acute care facility were repositioning tasks. Therefore, it is important to determine the patient-handling demands and needs that are unique to each type of healthcare facility. Generalizing across facilities or units may lead to incorrect assumptions and conclusions about physical demands being placed on nurses. A laboratory simulation was used for the second study (Chapter 4). The top four physically demanding patient-handling tasks (taken from Chapter 3) were simulated to determine the effect of an assistive device and assistance from another person. Sixteen nurse volunteers were recruited and provided perceptual responses regarding exertion and injury risk. Nurses perceived that assistance decreased their physical exertion and injury risk; however they consistently perceived exertion to be relatively higher than their injury risk. The aim of the third study (Chapter 5) was to determine the level of agreement between and within different expert groups. Three groups of participants were involved, with different levels of ergonomics expertise (i.e. researchers, consultants, and graduate students). These groups viewed digitized video clips from the laboratory simulation (Chapter 4) and provided ratings of perceived exertion, perceived injury risk and common WMSD risk factors (effort, posture, and speed). The major finding from this study was that poor agreement existed between nurses and the other expert groups (researchers, consultants and students). The current research laid the groundwork for measuring the magnitude of physical exposure to injury risk in the patient-handling environment. The research supports earlier evidence that suggests nurses underreport their discomfort and injury, which, in turn, contributes to increased exposure and risk. This knowledge will enable practitioners to focus interventions and designs on those factors in the work environment that contribute significantly to increased exposure and thereby more effectively reduce WMSD risk. / Ph. D.

The current airline practice is to assign aircraft capacity to scheduled flights well in advance of departure. At such an early stage in this process, the high uncertainty of demand poses a major impediment for airlines to best match the airplane capacities with the final demand. However, the accuracy of the demand forecast improves markedly over time, and revisions to the initial fleet assignment become naturally pertinent when the observed demand considerably differs from the assigned aircraft capacity. The Demand Driven Re-fleeting (DDR) approach proposed in this thesis offers a dynamic re-assignment of aircraft capacity to the flight network, as and when improved demand forecasts become available, so as to maximize the total revenue. Because of the need to preserve the initial crew schedule, this re-assignment approach is limited within a single family of aircraft and to the flights assigned to this particular family. This restriction significantly reduces the problem size. As a result, it becomes computationally tractable to include path level demand information into the DDR model, although the problem size can then get very large because of the numerous combinations of composing paths from legs. As an extension, models considering path-class level differences, day-of-week demand variations, and re-capture effects are also presented. The DDR model for a single family with path level demand considerations is formulated as a mixed-integer programming problem. The model's polyhedral structure is studied to explore ways for tightening its representation and for deriving certain classes of valid inequalities. Various approaches for implementing such reformulation techniques are investigated and tested. The best of these procedures for solving large-scale challenging instances of the problem turns out to be an integrated approach that uses certain selected model augmentations and valid inequalities generated via a suitable separation routine and a partial convex hull construction process. Using this strategy in concert with properly selected CPLEX options reduces the CPU time by an average factor of 7.48 over an initial model for a test-bed of problems each having 200 flights in total. Prompted by this integrated heuristic approach, a procedure for finding solutions within a prescribed limit of optimality is suggested. To demonstrate the effectiveness of these developed methodologies, we also solved two large-scale practical-sized networks that respectively involve 800 and 1060 flights, and 18196 and 33105 paths in total, with 300 and 396 flights belonging to the designated family. These problems were typically solved within 6 hours on a SUN Ultra 1 Workstation having 260 MB RAM and a clock-speed of 167 MHz, with one exception that required 14 hours of CPU time. This level of computational effort is acceptable considering that such models are solved at a planning stage in the decision process. / Master of Science

We consider a continuous time dynamic pricing model where a seller needs to sell a single item over a finite time horizon. Customers arrive in accordance with a Poisson process. Upon arrival, a customer either purchases the item if the posted price is lower than his/her reservation price, or leaves empty-handed. After purchasing the item, some customers, however, will return the item to the seller at an exponential rate for a full refund. We assume that a returned item is in mint condition and the seller can resell it to future customers. The objective of the seller is to dynamically adjust the price in order to maximize the expected total revenue when the sale horizon ends. We formulate the dynamic pricing problem as a dynamic programming model and derive the structural properties of the optimal policy and the optimal value function. For cases in which the customer's reservation price is exponentially distributed, we derive the optimal policy in a closed form. For general reservation price distribution, we consider an approximation of the original model by discretizing both time and the allowable price set. We then present an algorithm for numerically computing the optimal policy in this discrete time model. Numerical examples show that if the discrete price set is carefully chosen, the expected total revenue is nearly the same as that when the allowable price set is continuous. / Master of Science

Realistic mobility and cellular traffic modeling is key to various wireless networking applications and have a significant impact on network performance. Planning and design, network resource allocation and performance evaluation in cellular networks require realistic traffic modeling. We propose a Discrete Event Simulation framework, Diamond - (Discrete Event Simulation of Mobility and Spatio-Temporal Spectrum Demand) to model and analyze realistic activity based mobility and spectrum demand patterns. The framework can be used for spatio-temporal estimation of load, in deciding location of a new base station, contingency planning, and estimating the resilience of the existing infrastructure. The novelty of this framework lies in its ability to capture a variety of complex, realistic and dynamically changing events effectively. Our initial results show that the framework can be instrumental in contingency planning and dynamic spectrum allocation. / Master of Science

The financial and environmental effects of traffic congestion and automobile-centric air pollution continue to be problems that must be addressed within the United States. In response, travel demand management (TDM) has emerged as a potential way to reduce automobile-based travel in order to minimize these effects. TDM strategies are highly dependent on specific urban form characteristics such as bicycle lanes, sidewalks, or transit facilities. A current gap exists in the analytical tools available to transportation planners when evaluating TDM projects. The standard transportation models do not take into account urban form characteristics in a systematic way. These characteristics play an import role in an individual's selection of walking, bicycling, or transit based travel modes. This gap needs to be filled in order to evaluate TDM projects with the same decision-making rigor that is applied to road expansion projects. The purpose of this project is to develop an enhanced transportation mode choice model that presents a systematic approach for incorporating urban form characteristics. This approach determines which elements of urban form have the strongest influence on transportation mode choice behavior. This work is being done in conjunction with the Roanoke Valley Allegheny Metropolitan Planning Organization as a way to evaluate the potential of TDM projects in promoting non-automobile forms of travel within the Roanoke region. This approach to developing an enhanced transportation mode choice model is a step forward in address the gap between TDM strategies and the tools needed to evaluate them. / Master of Urban and Regional Planning

Technological interventions are being considered to alleviate congestion and to improve the quality of driving on our nation's highways. These new technology interventions will be capable of increasing the amount of information provided to the driver; therefore, steps must be taken to ensure they do not require a high attention demand. (Limited attention resources can be diverted from the primary task of driving to a secondary in-vehicle task). The attention demand required as part of the process of extracting information has been studied relatively extensively. However, the processing required to make complex decisions is not well understood and provides cause for concern. This study investigated the attention demand required to perform several types of tasks, such as selecting a route, selecting the cheapest route, and selecting the fastest route. The three objectives of this study were: 1) To investigate driver performance during IVIS tasks that required additional processing of information after the extraction of information from a visual display. 2) To develop a method for evaluating driver performance with regard to safety. This task was accomplished by performing an extensive review of the literature, and developing two composite measures. 3) To provide descriptive data on the proportion of drivers who exceeded a threshold of driver performance for each of the different IVIS tasks. An instrumented vehicle, equipped with cameras and sensors, was used to investigate on-road driver behavior on a four-lane divided road with good visibility. A confederate vehicle was driven in front of the instrumented vehicle to create a vehicle following situation. Thirty-six drivers participated in this study. Age, presentation format, information density, and type of task were the independent variables used in this study. Results from this study indicate that a high proportion of drivers' will have substantially degraded performance performing IVIS tasks such as selecting a route or a hotel from several possibilities. Findings also indicate that tasks involving computations, such as selecting the quickest or cheapest route, require a high attention demand and consequently should not be performed by a driver when the vehicle is in motion. In addition, text-based messages in paragraph format should not be presented to the driver while the vehicle is in motion. The graphic icon format should be utilized for route planning tasks. / Ph. D.

Biological aerated filters (BAFs) are an emerging wastewater treatment technology designed for a wide range of municipal and industrial applications. BAFs utilize an inert media, either dense granular or floating, which supports biomass retention in the filter bed. BAFs offer an alternative to typical biological treatment processes; however, knowledge of the process is often limited, especially in the US market. Through various studies, process improvements were made for filter media selection, backwash protocols, and hydraulic load effects. During the summertime monitoring, seeded and unseeded nitrified effluent BOD5 samples were performed on a full-scale BAF. Discrepancies were found between seeded and unseeded samples, which warranted further investigation. Four biological treatment seeds and a commercial microbial seed were screened for appropriate seed volumes in comparison to the glucose:glutamic acid (GAA) assay, a standard for BOD5 analysis. After initial screening, a range of seed samples was applied to the BAF effluent for BOD5 and cBOD5 analysis, and to GGA and carbonaceous GGA (cGGA) analysis. A proposed seed screening protocol was developed using a ratio of measured BOD5 values in comparison to theoretical GGA standard BOD5 values. Biomass observed yield values were calculated for the full-scale BAF. Three individual mass balances were conducted to quantify the consumption of soluble COD in the filter and the mass of influent particulate matter filtered from the waste stream. Retained particulate matter is a substrate source for the biomass; however, the particles must be hydrolyzed into metabolizable monomers before being consumed by the biomass. A bench-scale BAF was designed and constructed to investigate the degree to which particle hydrolysis occurred in the full-scale system. Additionally, fluorescein diacetate was used during one of the experiments as a model particulate substrate to quantify the activity associated with hydrolytic enzymes in the bulk-liquid. Hydrolytic activity by cell-free extracellular enzymes in the bulk-liquid increased when particle substrate was present. Therefore, it appears that cell-free extracellular enzymes participate in the hydrolytic mechanism for particle degradation. Biomass observed yields were calculated for the full-scale BAF using full-scale mass balances and bench-scale particle hydrolysis experiments. / Master of Science

Yes / Multi-Stage Flash (MSF) desalination process has been used for decades for making fresh water from seawater and is the largest sector in desalination industries. In this work, dynamic optimisation of MSF desalination is carried out using powerful and robust dynamic simulation and optimisation software called gPROMS model builder. For a fixed freshwater demand, a number of optimal combinations of the factors such as heat transfer area, brine flow rate, cooling water flow rate, steam flow in brine heater, Top Brine Temperature, the number of stages, etc. are determined with the objective of maximising the performance ratio of the process (defined as the amount of fresh water produced per unit of energy input) considering the seasonal variations. An attempt has been made to develop an operational schedule for a particular day using dynamic optimisation.