Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd W. Easton / Integer programs (IPs) are mathematical models that can provide organizations with the ability to optimally obtain their goals through appropriate utilization and allocation of available resources. Unfortunately, IPs are NP-complete in the strong sense, and many integer programs cannot be solved. Introduced by Gomory, lifting is a technique that takes a valid inequality and strengthens it. Lifting can result in facet defining inequalities, which are the theoretically strongest inequalities; because of this, lifting techniques are commonly used in commercial IP software to reduce the time required to solve an IP. This thesis introduces two new algorithms for exact simultaneous up lifting multiple sets into binary knapsack problems and introduces sequential simultaneous lifting. The Dynamic Programming Multiple Lifting Set Algorithm (DPMLSA) is a pseudopolynomial time algorithm bounded by O(nb) effort that can exactly uplift an arbitrary number of sets. The Three Set Simultaneous Lifting Algorithm (TSSLA) is a polynomial time algorithm bounded by O(n2) and can exact simultaneously up lift three sets. The simultaneously lifted inequalities generated by the DPMLSA and the TSSLA can be facet defining, and neither algorithm requires starting with a minimal cover. A brief computational study shows that the DPMLSA is fast and required an average of only 0.070 seconds. The computational study also shows these sequential simultaneously lifted inequalities are useful, as the solution time decreased by an overall average of 18.4%. Therefore, implementing the DPMLSA should be beneficial for large IPs.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd W. Easton / Epidemics have been modeled mathematically as a way to safely understand them. For many of these mathematical models, the underlying assumptions they make provide excellent mathematical results, but are unrealistic for practical use. This research branches out from previous work by providing a model of the spread of infectious diseases and a model of quarantining this disease without the limiting assumptions of previous research. One of the main results of this thesis was the development of a core simulation that rapidly simulates the spread of an epidemic on a contact network. This simulation can be easily adapted to any disease through the adjustment of many parameters. This research provides the first definition for a quarantine cut and an ellipsoidal geographic network. This thesis uses the ellipsoidal geographic network to determine what is, and what is not, a feasible quarantine region. The quarantine cut is a new approach to partitioning quarantined and saved individuals in an optimized way. To achieve an optimal quarantine cut, an integer program was developed. Although this integer program runs in polynomial time, the preparation required to execute this algorithm is unrealistic in a disease outbreak scenario. To provide implementable results, a heuristic and some general theory are provided. In a study, the heuristic performed within 10% of the optimal quarantine cut, which shows that the theory developed in this thesis can be successfully used in a disease outbreak scenario.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd W. Easton / With the present threat of bioterrorist attacks and new natural disease strains developing, efficient and effective countermeasures must be in place in case of an epidemic outbreak. The best strategy is to stop the attack or natural phenomenon before it happens, but governments and individual citizens must have measures in place to limit the spread of a biological threat or infectious disease if it is ever introduced into society. The objective of this research is to know, before an outbreak, the best quarantine areas. Quarantines force similar individuals together and can be mathematically modeled as clustering people into distinct groups. In order to effectively determine the clustering solution to use as a quarantine plan, this research developed a simulation core that is highly adaptable to different disease types and different contact networks. The input needed for the simulation core is the characteristics of the disease as well as the contact network of the area to be modeled. Clustering is a mathematical problem that groups entities based on their similarities while keeping dissimilar entities in separate groups. Clustering has been widely used by civilian and military researchers to provide quality solutions to numerous problems. This research builds a mathematical model to find clusters from a community’s contact network. These clusters are then the preplanned quarantine areas. To find quality clusters a Clustering Heuristic using Integer Programming (CHIP) is developed. CHIP is a large neighborhood, hill-climbing heuristic and some computational results verify that it quickly generates good clustering solutions. CHIP is an effective heuristic to group people into clusters to be used as quarantine areas prior to the development of a disease or biological attack. Through a small computational study, CHIP is shown to produce clustering solutions that are about 25% better than the commonly used K-means clustering heuristic. CHIP provides an effective tool to combat the spread of an infectious disease or a biological terroristic attack and serves as a potential deterrent to possible terrorist attacks due to the fact that it would limit their destructive power. CHIP leads to the next level of preparation that could save countless lives in the event of an epidemic.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Chih-Hang Wu / The recent economic crisis has been partially blamed on the decline in the housing market. This decline in the housing market resulted in an estimated 87% decline in value of collateralized debt obligations (CDOs) between 2007 and 2008. This drastic decline in home values was sudden and unanticipated, thus it was incomprehensible for many investors how this would affect CDOs. This shows that while analytical techniques can be used to price CDOs, these techniques cannot be used to demonstrate the behavior of CDOs under radically different economic circumstances. To better understand the behavior of CDOs under different economic circumstances, numerical techniques such as Monte Carlo simulation can be used instead of analytical techniques to price CDOs. Andersen et al (2005) proposed a method for calculating the probability of defaults that could then be used in the Monte Carlo simulation to price the collateralized debt obligation. The research proposed by Andersen et al (2005) demonstrates the process of calculating correlated probability of defaults for a group of obligors. This calculation is based on the correlations between the obligors using copulas. Using this probability of default, the price of a collateralized debt obligation can be evaluated using Monte Carlo simulation. Monte Carlo simulation provides a more simple yet effective approach compared to analytical pricing techniques. Simulation also allows investors to have a better understanding of the behaviors of CDOs compared to analytical pricing techniques. By analyzing the various behaviors under uncertainty, it can be observed how a downturn in the economy could affect CDOs. This thesis extends on the use of copulas to simulate the correlation between obligors. Copulas allow for the creation of one joint distribution using a set of independent distributions thus allowing for an efficient way of modeling the correlation between obligors. The research contained within this thesis demonstrates how Monte Carlo simulation can be used to effectively price collateralized debt obligations. It also shows how the use of copulas can be used to accurately characterize the correlation between obligor defaults for pricing collateralized debt obligations. Numerical examples for both the obligor defaults and the price of collateralized debt obligations are presented to demonstrate the results using Monte Carlo simulation.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd W. Easton / Integer programming (IP) is a powerful technique used by many companies and organizations to determine optimal strategies for making decisions and managing resources to achieve their goals. One class of IP problems is the multiple knapsack (MK) problem. However, MK and other IP problems, are extremely complicated since they are ${\cal NP}$-hard problems. Furthermore, there exist numerous instances that can not be solved. One technique commonly used to reduce the solution time for IP problems is lifting. This method, introduced by Gomory, takes an existing valid inequality and strengthens it. Lifting has the potential to form facet defining inequalities, which are the strongest inequalities to solve an IP problem. As a result, lifting is frequently used in integer programming applications. This research takes a broad approach to simultaneous lifting and provides its theoretical background for. The underlying hypergraphic structure for simultaneous lifting in an MK problem is identified and called a constellation. A constellation contains two hypercliques and multiple hyperstars from various conflict hypergraphs. Theoretical results demonstrate that a constellation induces valid inequalities that could be obtained by simultaneous lifting. Moreover, these constellation inequalities can be facet defining. The primary advancements, constellations and the associated valid inequalities, of this thesis are theoretical in nature. By providing the theory behind simultaneous lifting, researchers should be able to apply this knowledge to develop new algorithms that enable simultaneous lifting to be performed faster and over more complex integer programs.

This work investigates new approaches to the container loading problem which address the issue of how to load three-dimensional, rectangular items (e.g. boxes) into the container in such a way that maximum utilisation is made of the container space. This problem occurs in several industry sectors where the loading approach places cargo effectively into aeroplanes, ships, trailers or trucks in order to save considerable cost. In carrying out this work, the investigation starts by developing a new heuristic approach to the two-dimensional bin packing problem, which has lower complexity than container loading in the aspects of constraints and geometry. A novel approach, including the heuristic strategies and handling method for remaining areas, is developed that can produce good results when testing with benchmark and real world data. Based on the research for two-dimensional bin packing, a novel heuristic approach is developed to deal with the container loading problem with some practical constraints. The heuristic approach to container loading also includes heuristic strategies and the handling of remaining spaces. The heuristic strategies construct effective loading arrangements where combinations of identical or different box types are loaded in blocks. The handling method for remaining spaces further improves the loading arrangements through the representation, partitioning and merging of remaining spaces. The heuristic approach obtains better volume utilisation and the highest stability compared with other published heuristic approaches. However, it does not achieve as high a volume utilisation as metaheuristic approaches, e.g. genetic algorithms and tabu search.To improve volume utilisation, a new hybrid heuristic approach to the container loading problem is further developed based on the tabu search technique which covers the encoding, evaluation criterion and configuration of neighbourhood and candidate solutions. The heuristic strategies as well as the handling method for remaining spaces developed in the heuristic approach are used in this new hybrid tabu search approach. It is shown that the hybrid approach has better volume utilisation than the published approaches under the condition that all loaded boxes with one hundred per cent support from below. In addition, the experimental results show that both the heuristic and hybrid tabu search approaches can also be applied to the multiple container loading problem.

Manufacturing companies are searching for new techniques day by day to launch qualified products with a competitive price to the market. Companies believe in the importance of teams which have members coming from different disciplines who use their knowledge, experiences, and creativity for achieving the goals of their teams. The purpose of this study is to explore the effectiveness of teamwork for successful product development process by focusing on the relationship between the team members &ndash / especially engineers&rsquo / and industrial designers&rsquo / relation. After a broad literature survey, a descriptive-survey study that aims demonstrating the relationship between engineers and industrial designers in manufacturing companies in Turkey is held in order to investigate the effectiveness of teamwork in product development process. A twelve item questionnaire which has four open-ended questions and eight Likert-scaled statements has been prepared. As the result of this study, a positive relationship is found between the performance of product development team and the effects of cooperation, social and professional communication, having clear and common goals, sharing knowledge and experiences, leadership, coordination and cooperative problem solving and decision making processes to the relationship between engineers and industrial designers.

Researchers have discussed the problems of restructuring, the methods used to measure restructuring success, the effectiveness of restructuring efforts, and prescriptions for successful restructuring among specific types of organizations (for-profit and public non-profit). While some have suggested that different challenges face for-profit sector and public non-profit sector restructuring efforts, few have compared the restructuring processes in both sectors based on the statements made by organizational representatives. This research studies both the language of restructuring as used by university and corporate leaders and the actual results of the restructuring plans presented through the theoretical frameworks of isomorphism and resource dependency. The documents collected from each organization were limited to those prepared for public consumption and reflected the language used by top management or administrators. An approach, using multiple case studies, was employed to organize and focus the data collected. The use of individual cases provided the opportunity to examine specific restructuring strategies, language, and results used by different organizations functioning in different economic sectors. The language analysis looked for the expression of different or similar organizational values expressed during the course of restructuring. The organizations studied all underwent recent restructuring efforts, and included two Research I universities and three Fortune 500 businesses. This research indicated that the public rhetoric of restructuring may not reflect the actual activities of restructuring taking place within an organization. Even though the business literature and many businesses themselves have extolled the benefits of a more collaborative management style since the early 1980's, and legislatures have encouraged public universities to be more business-like since the early 1990's, and though much of the rhetoric reflected these pressures, the actual management processes showed very little change in either group. A movement toward one another in management style was not found in these organizations. All five organizations structured rhetoric to satisfy their constituents, all five organizations maintained their traditional management and decision making styles, and, at the end of the restructuring period, all five organizations were still trying to find ways to improve their organizational outcomes.

This thesis presents a computerized nursing workload management system developed as part of the Patient Data Management System (PDMS) for the Pediatric Intensive Care Unit of the Montreal Children's Hospital. The objective of the Nursing Workload Management System (NWMS) is primarily to reduce the workload of nursing staff by automating the preparation of nursing care plans, generating schedules for the tasks in the plan of care, and providing an audit trail facility to annotate the care delivered to a patient. / The thesis begins with a literature review of contemporary computerized medical information systems followed by an overview of the PDMS. A description of the design and implementation of the Nursing Workload Management System of the PDMS is followed by evaluation and performance results of the system. The evaluation of the performance results is encouraging and further on site testing of the NWMS is recommended. Finally, an outline of future extensions for the system is included.

Zugl.: Stuttgart, Univ., Diss., 2005. / Print-Ausg. bei Jost-Jetter, Heimsheim erschienen.