University of Macau / Faculty of Science and Technology / Department of Civil and Environmental Engineering

In order to achieve stable and sustainable systems for recycling post-consumer goods, it is frequently necessary to concentrate the flows from many collection points to meet the volume requirements for the recycler. This motivates the importance of growing the collection network over time to both meet volume targets and keep costs to a minimum. This research addresses a complex and interconnected set of strategic and tactical decisions that guide the growth of reverse supply chain networks over time. This dissertation has two major components: a tactical recruitment model and a strategic investment model. These capture the two major decision levels for the system, the former for the regional collector who is responsible for recruiting material sources to the network, the latter for the processor who needs to allocate his scarce resources over time and to regions to enable the recruitment to be effective. The recruitment model is posed as a stochastic dynamic programming problem. An exact method and two heuristics are developed to solve this problem. A numerical study of the solution approaches is also performed. The second component involves a key set of decisions on how to allocate resources effectively to grow the network to meet long term collection targets and collection cost constraints. The recruitment problem appears as a sub-problem for the strategic model and this leads to a multi-time scale Markov decision problem. A heuristic approach which decomposes the strategic problem is proposed to solve realistically sized problems. The numerical valuations of the heuristic approach for small and realistically sized problems are then investigated.

There has been an almost 60 percent increase in health care expenditures in the US in the past seven years. Employer-sponsored health coverage premiums have increased significantly (87 percent) in this same period. Besides the cost of care for chronic conditions such as migraine, arthritis and diabetes, absenteeism linked to these diseases also adds financial strain. Current health financial models focus on past spending instead of modeling based on current health burdens and future trends. This approach leads to suboptimal health maintenance and cost management. Identifying the diseases which affect the most employees and are also the most costly (in terms of productivity, work-loss-days, treatment etc) is necessary, since this allows the employer to identify which combination of policies may best address the health burdens. The current predictive health model limits the amount of diseases it models since it ignores incomplete data sets. This research investigated if by using Bayesian methodology it will be possible to create a comprehensive predictive model of the health burdens being faced by corporations, allowing for health decision makers to have comprehensive information when choosing policies. The first specific aim was to identify which diseases were the most costly to employers both directly and indirectly, and the pathogenesis of these diseases. Co-morbidity of diseases was also taken into account as in many cases these diseases are not treated independently. This information was taken into account when designing the models as the inference was disease specific. One of the contributions of this thesis is coherent incorporation of prior information into the proposed expert model. The Bayesian models were able to estimate the predicted disease burdens for corporations, including predicting the percentage of individuals with multiple diseases. The model was also comparable to, or better than current estimators on the market with limited input. The outputs of the model were also able to give further insight into the disease interactions which creates an avenue for further research in disease management.

This thesis work describes the formulation of a Bayesian approach along with new tools to systematically reduce uncertainty in Research&Development (R&D) alternatives. During the initial stages of R&D many alternatives are considered and high uncertainty exists for all the alternatives. The ideal approach in addressing the many R&D alternatives is to find the one alternative which is stochastically dominant i.e. the alternative which is better in all possible scenarios of uncertainty. Often a stochastically dominant alternative does not exist. This leaves the R&D manager with two alternatives, either to make a selection based on user defined utility function or to gather more information in order to reduce uncertainty in the various alternatives. From the decision makers perspective the second alternative has more intrinsic value, since reduction of uncertainty will improve the confidence in the selection and further reduce the high downside risk involved with the decisions made under high uncertainty. The motivation for this work is derived from our preliminary work on the evaluation of biorefiney alternatives, which brought into limelight the key challenges and opportunities in the evaluation of R&D alternatives. The primary challenge in the evaluation of many R&D alternatives was the presence of uncertainty in the many unit operations within each and every alternative. Additionally, limited or non-existent experimental data made it infeasible to quantify the uncertainty and lead to inability to develop an even simple systematic strategy to reduce it. Moreover, even if the uncertainty could be quantified, the traditional approaches (scenario analysis or stochastic analysis), lacked the ability to evaluate the key group of uncertainty contributors. Lastly, the traditional design of experiment approaches focus towards reduction in uncertainty in the parameter estimates of the model, whereas what is required is a design of experiment approach which focuses on the decision (selection of the key alternative). In order to tackle all the above mentioned challenges a Bayesian framework along with two new tools is proposed. The Bayesian framework consists of three main steps: a. Quantification of uncertainty b. Evaluation of key uncertainty contributors c. Design of experiment strategies, focussed on decision making rather than the traditional parameter uncertainty reduction To quantify technical uncertainty using expert knowledge, existing elicitation methods in the literature (outside chemical engineering domain) are used. To illustrate the importance of quantifying technical uncertainty, a bio-refinery case study is considered. The case study is an alternative for producing ethanol as a value added product in a Kraft mill producing pulp from softwood. To produce ethanol, a hot water pre-extraction of hemi-cellulose is considered, prior to the pulping stage. Using this case study, the methodology to quantify technical uncertainty using experts' knowledge is demonstrated. To limit the cost of R&D investment for selection or rejection of an R&D alternative, it is essential to evaluate the key uncertainty contributors. Global sensitivity analysis (GSA) is a tool which can be used to evaluate the key uncertainties. But quite often global sensitivity analysis fails to differentiate between the uncertainties and assigns them equal global sensitivity index. To counter this failing of GSA, a new method conditional global sensitivity (c-GSA) is presented, which is able to differentiate between the uncertainties even when GSA fails to do so. To demonstrate the value of c-GSA many small examples are presented. The third and the last key method in the Bayesian framework is the decision oriented design of experiment. Traditional 'Design of Experiment' (DOE) approaches focus on minimization of parameter error variance. In this work, a new "decision-oriented" DOE approach is proposed that takes into account how the generated data, and subsequently, the model developed based on them will be used in decision making. By doing so, the parameter variances get distributed in a manner such that its adverse impact on the targeted decision making is minimal. Results show that the new decision-oriented DOE approach significantly outperforms the standard D-optimal design approach. The new design method should be a valuable tool when experiments are conducted for the purpose of making R&D decisions. Finally, to demonstrate the importance of the overall Bayesian framework a bio-refinery case study is considered. The case study consists of the alternative to introduce a hemi-cellulose pre-extraction stage prior to pulping in a thermo-mechanical pulp mill. Application of the Bayesian framework to address this alternative, results in significant improvement in the prediction of the true potential value of the alternative.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 75-77). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 97-98). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 122-127). Also available on the Internet.

Originally presented as the author's thesis (Ph. D.)--WHU, Otto-Beisheim School of Management, Vallendar, Germany. / Description based on print version record. Includes bibliographical references (p. 195-205) and index.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 122-127). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 97-98). Also available on the Internet.