In recent years, the importance of incorporating uncertainty into planning models for logistics and transportation systems has been widely recognized in the Operations Research and transportation science communities. Maritime transportation, as a major mode of transport in the world, is subject to a wide range of disruptions at the strategic, tactical and operational levels. This thesis is mainly concerned with the development of robustness planning strategies that can mitigate the effects of some major types of disruptions for an important class of optimization problems in the shipping industry. Such problems arise in the creation and negotiation of long-term delivery contracts with customers who require on-time deliveries of high-value goods throughout the year. In this thesis, we consider the disruptions that can increase travel times between ports and ultimately affect one or more scheduled deliveries to the customers. Computational results show that our integrated solution procedure and robustness planning strategies can generate delivery plans that are both economical as well as robust against uncertain disruptions.

published_or_final_version / abstract / Mathematics / Master / Master of Philosophy

The term "codependency" is emerging in the mental health field on a daily basis. However, along with this term comes confusion regarding what "codependency" is and how to effectively treat it. Since the term originated within the addiction studies arena, Twelve-Step programs have taken on the challenge of treating the fast-growing population of "codependents". The purpose of the present study was twofold. First, it investigated personality characteristics and traits shared by individuals having been treated for codependency issues. And second, it investigated the effects of a Twelve-Step, intensive, residential treatment program on these individuals. The subjects used in this study were identified as "codependents" due to significant, dysfunctional relationships within their lives. The research questions investigated by this study included the following: (1) What are the common demographic, personality or behavioral characteristics seen in individuals with codependency issues? And are there other identifiable factors considered contributory to treatment outcome? (2) Is intensive, short-term, residential treatment based on the Twelve-Step model, an effective approach with individuals dealing with codependency issues? (3) Do changes made during the course of treatment remain stable over time? The instruments utilized in this study to answer the above research questions included the MMPI and a Self-Evaluation Questionnaire developed by the investigator. Individuals who had been out of treatment for at least three months were solicited for their participation. Fifty subjects were used. Results showed that individuals treated for codependency tend to have low self-esteem, self-deprecation, stress, rebellion and anger, physical complaints, depression, high dependency needs, mistrust in others, and limited ego strength. They shared many diagnostic characteristics with the Self-defeating, Borderline and Dependent Personality Disorders. Treatment intervention proved to be effective in diminishing elevated levels of psychopathology. The results obtained at the time of discharge from treatment remained stable over an extended period of time.

This dissertation contains a number of varied, yet closely related, results that are relevant to the construction of mathematical and statistical models of inventory systems. Its primary focus is on the sensitivity of some specific inventory models to errors in certain modeling assumptions. Motivation for this research is provided through the development of analytical expressions that show that the deterministic economic order quantity can be quite sensitive to errors in the forecast of the demand rate whenever the lead time is non-zero. Similar results are provided for the stochastic case by means of a carefully designed experiment that shows that the specific form or "shape" of the distribution chosen to represent the stochastic behavior of the lead time demand can have a significant impact on a minimum cost (Q,R) policy. Together, these results refute the "conventional wisdom" that inventory models are generally insensitive to errors in model specification or parameter estimation. Considerable attention is also given to the postulation of a "robust" model for the lead time demand distribution (LTDD). This discussion culminates with the introduction of a new probability distribution, based on a hyperbolic cosine transformation of normal random variables, that appears to be well-suited for modeling the LTDD. Furthermore, it is concluded that the two- and three-parameter versions of the lognormal and inverse Gaussian distributions can also be considered as viable candidates to model the LTDD in a wide variety of inventory systems. A number of new algorithms for computing optimal (Q,R) policies are also introduced. These significantly reduce both the amount and complexity of computation required by the standard iterative method. Two additional sets of analytical results are chronicled in this work. The first allows the LTDD to be characterized (by its first four moments) on the basis of information about the distributions of the lead time and demand rate. The second expresses the linear loss functions (LLF's) for a number of probability distributions whose LLF's are not readily available in the inventory control literature. Complete and intuitive proofs of these results are included.

Background: Intensive care unit (ICU) admission is often life threatening, and may cause severe anxiety within the family system. Anxiety can impair decision-making ability. A majority of ICU patients cannot direct their own treatment; therefore, family members are often required to make major decisions under stressful conditions. Patient and family-centered care (PCFF) has been shown to reduce anxiety, improve decision-making, and improve outcomes for patients and their families. However, no published study has examined outcomes of a comprehensive PFCC program in the ICU. Purpose: The study purposes were to evaluate a comprehensive program to improve PFCC within an adult ICU, and to determine the usefulness of specific PFCC interventions. Methods: An exploratory comparative design was used. Data from ICU patients' family members and ICU nurses, before and after implementation of a PFCC program, were compared using the 30-item combined Critical Care Family Needs Inventory/Needs Met Inventory (CCFNI/NMI). Convenience samples of 49 adult family members of patients admitted to the ICU for at least 36 hours and 85 nurses employed in the ICU full-time for at least six months were recruited from an adult ICU in a 337-bed tertiary care hospital in the southwestern region of the United States. The program was conducted in 3 stages: baseline assessment, program development and implementation, and evaluation. Results: After the PFCC implementation statistically significant differences between nurses' and family members' responses were reported for18 items on the CCFNI and 20 items on the NMI. Five of the10 items family members ranked highest at baseline remained in the top 10 after PFCC implementation, and 3 needs ranked lowest at baseline moved up to the top 10. Conclusions: The results show that the nurses' education was likely the most efficacious program intervention. There may be a hierarchy of needs specific to ICU patients' family members, similar to those described by Maslow. Further study is needed to determine the effectiveness of the CCFNI/NIM in measuring outcomes before and after a PFCC intervention.

We study three problems on supply chain location and inventory under uncertainty. In Chapter 2, we study the inventory purchasing and allocation problem in a movie rental chain under demand uncertainty. We formulate this problem as a newsvendor-like problem with multiple rental opportunities. We study several demand and return forecasting models based on comparable films using iterative maximum likelihood estimation and Bayesian estimation via Markov chain Monte Carlo simulation. Test results on data from a large movie rental firm reveal systematic under-buying of movies purchased through revenue sharing contracts and over-buying of movies purchased through standard ones. For the movies considered, the model estimates an increase in the average profit per title for new movies by 15.5% and 2.5% for revenue sharing and standard titles, respectively. We discuss the implications of revenue sharing on the profitability of both the rental firm and the studio. In Chapter 3, we focus on the effect of travel time uncertainty on the location of facilities that provide service within a given coverage radius on the transportation network. Three models - expected covering, robust covering and expected p-robust covering - are studied; each appropriate for different types of facilities. Exact and approximate algorithms are developed. The models are used to analyze the location of fire stations in the city of Toronto. Using real traffic data we show that the current system design is quite far from optimality and provide recommendations for improving the performance. In Chapter 4, we continue our analysis in Chapter 3 to study the trade-off between adding new facilities versus relocating some existing facilities. We consider a multi-objective problem that aims at minimizing the number of facility relocations while maximizing expected and worst case network coverage. Exact and approximate algorithms are developed to solve three variations of the problem and find expected--worst case trade-off curves for any given number of relocations. The models are used to analyze the addition of four new fire stations to the city of Toronto. Our results suggest that the benefit of adding four new stations is achievable, at a lower cost, by relocating 4-5 stations.

The purpose of the study was to investigate the relationship between hypnotic suggestibility and personality among a group of delinquent and non-delinquent adolescent females. Also, an effort was made to determine if there is an adequate predictor of delinquency and non-delinquency which can be derived from the MMPI Validity, Clinical, and New Scales; or from the BSS Objective and Subjective Scales. Five hypotheses were presupposed and tested.

The current study explores the rates of valid and invalid MMPI-2 protocols across testing conditions, as well as the test taker's preference for each format and the amount of time spent responding to the items. Participants were 203 undergraduate students (90 men and 113 women) from a Midwestern university. Participants completed either a CC or P&P version of the MMPI-2 along with a supplemental survey to assess for participants' testing format preference. Overall, results suggest that while testing format did not affect the number of valid MMPI-2 protocols produced, the CC version was rated more favorably by participants and took significantly less time to complete. / Department of Psychological Science

This thesis describes a stochastic ship routing problem with inventory management. The problem involves finding a set of least costs routes for a fleet of ships transporting a single commodity when the demand for the commodity is uncertain. Storage at consumption and supply ports is limited and inventory levels are monitored in the model. Consumer demands are at a constant rate within each time period in the deterministic problem, and in the stochastic problem, the demand rate for a period is not known until the beginning of that period. The demand situation in each time period can be described by a scenario tree with corresponding probabilities. Several possible solution approaches for solving the problem are studied in the thesis. This problem can be formulated as a mixed integer programming (MIP) model. However solving the problem this way is very time consuming even for a deterministic problem with small problem size. In order to solve the stochastic problem, we develop a decomposition formulation and solve it using a Branch and Price framework. A master problem (set partitioning with extra inventory constraints) is built, and the subproblems, one for each ship, involve solving stochastic dynamic programming problems to generate columns for the master problem. Each column corresponds to one possible tree of schedules for one ship giving the schedule for the ship for all demand scenarios. In each branch-and-bound node, the node problem is solved by iterating between the master problem and the subproblems. Dual variables can be obtained solving the master problem and are used in the subproblems to generate the most promising columns for the master problem. Computational results are given showing that medium sized problems can be solved successfully. Several extensions to the original model are developed, including a variable speed model, a diverting model, and a model which allows ships to do extra tasks in return for a bonus. Possible solution approaches for solving the variable speed and the diverting model are presented and computational results are given.

Peatlands are unique habitats that have absorbed large amounts of carbon dioxide and locked it away as carbon buried in peat for millennia. In the UK, blanket peatlands form one of the largest terrestrial stores of carbon (Milne and Brown, 1997). Recent research suggests that the carbon sequestering potential and carbon stores of UK blanket peatlands are at risk from changes in land use practices and climate. Although, to date, little research has considered blanket peatland at a landscape scale and a comprehensive understanding of land use and degradation impact upon carbon sequestration has not been gained. This thesis presents a study of Dartmoor, a blanket peatland in south west England vulnerable to climate change (Clark et al, 2010). A landscape scale carbon inventory, using a methodology designed for blanket peatlands is presented. Nearly 1000 peat depths and 30 cores were taken using stratified sampling across Dartmoor’s landscape. Functional relationships between peat depth, bulk density and carbon content and topographic parameters were found. In arc GIS 9.3 these were used to model landscape scale carbon, this estimates that Dartmoor contained 9.7 (-2.91 + 2.97) Mt of carbon, a value similar to that of the national inventory (Bradley et al, 2005). The thesis then considers the impact of drainage and degradation on carbon accumulation. Fifteen cores were dated from a drained, degraded site with a history of burning and control site using Spheroidal Carbonaceous Particles (SCPs) and radionuclide techniques. Previous studies have raised concern surrounding accuracy dating recent peats. Results indicate that although dating was largely successful, some discrepancies existed related to poor calibration of SCPs and mobility of radionuclides. To avoid error in dating, it was concluded that multiple dates should be used per core. With consideration of this, carbon accumulation was found to be active but significantly lower in the degraded site and unchanged in the drained site. Further analysis suggested that this outcome may vary with changing management and topographic situations. Future carbon accumulation at a landscape scale was calculated under different scenarios. This found degradation could potentially reduce carbon sequestration on Dartmoor by up to 32%. Economic valuation of accumulation values was used to demonstrate how this data could be used to inform management. This thesis provides an insight into the carbon storage and threats to Dartmoor, an under investigated, yet threatened blanket peatland environment. This helps broaden the spatial