In practice, optimization problems are often multiple criteria. The criteria are usually contradictory, so the final decision depends on a decision maker. When the problem is solved interactively, the decision maker can change his/her preferences in decision process. Moreover, it is important to obtain solutions from the whole Pareto front. A decision support system adapted to the specific of the problem is essential for solving multiple criteria optimization problems interactively. The objects of research are multiple criteria optimization problems, interactive methods for solving these problems, interactive decision support systems, and application of parallel computing in decision support systems. Multiple criteria optimization methods are analyzed in the dissertation. The focus of attention is the methods for a uniform distribution of solutions on the Pareto front as well as the interactive methods. An interactive way for solving multicriteria optimization problems, which finds alternative solutions uniformly distributed on the Pareto front is proposed and investigated in this dissertation. An interactive decision support system which integrates the created interactive solving way, the decision process visualization and parallelization for multiple criteria optimization is developed. The solving strategies, when a multiple criteria optimization problem is solved interactively, using a computer cluster are developed and compared experimentally. The time required for a... [to full text] / Praktikoje dažnai tenka spręsti sudėtingus daugiakriterinius optimizavimo uždavinius, kai kriterijai būna prieštaringi, o galutinis apsisprendimas priklauso nuo sprendimų priėmėjo. Kai sprendimų priėmėjas dalyvauja sprendimo procese interaktyviai, tai jis gali koreguoti prioritetus ir siekiamus tikslus uždavinio sprendimo eigoje, kas įgalina spęsti uždavinius, turinčius daug kriterijų ir apribojimų. Be to, sprendimo priėmėjui svarbu gauti sprendinius iš visos Pareto aibės. Interaktyviam uždavinių sprendimui būtina sprendimų paramos sistema, kurios grafinė sąsaja yra pritaikyta sprendžiamam uždaviniui. Šio darbo tyrimų sritis yra interaktyvus daugiakriterinių optimizavimo uždavinių sprendimas bei sprendimų paramos sistemos. Disertacijoje nagrinėjant daugiakriterinio optimizavimo metodus, didesnis dėmesys skirtas metodams, užtikrinantiems gaunamų sprendinių tolygų pasiskirstymą Pareto aibėje bei interaktyviems metodams. Pasiūlytas ir ištirtas daugiakriterinių optimizavimo uždavinių sprendimo būdas, leidžiantis spręsti daugiakriterinius optimizavimo uždavinius interaktyviai ir užtikrinantis gaunamų sprendinių tolygų pasiskirstymą Pareto aibėje. Sukurta ir ištirta interaktyvi daugiakriterinių optimizavimo uždavinių sprendimų paramos sistemą, apjungianti pasiūlytą optimizavimo uždavinių sprendimo būdą, sprendimo proceso vizualizavimą ir jo lygiagretinimą. Taip pat pasiūlyta sprendimo strategija, pagal kurią sprendžiant daugiakriterinį optimizavimo uždavinį pasitelkiamas... [toliau žr. visą tekstą]

Context: Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making. Objectives: The main objectives of this thesis are to provide: • A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner; • A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and • A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context Method: We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our approach by performing a systematic literature review, by assessing it against Zellner’ mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail). Results: The two examples show that through the framework’s iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN provided in this thesis as well as the discussed improvement patterns.

The diagnostic imaging (DI) literature identifies that unnecessary examinations are occurring. However, there is a gap in the research literature: little is known about how physicians order DI examinations and what efforts need to be undertaken to reduce the number of inappropriate orders made by physicians. Such research is needed in order to promote patient safety and improve utilization of limited health care resources Purpose: The purpose of this study is to explore how physicians order DI services, and what efforts could be made to reduce inappropriate DI ordering. Participants: 12 English speaking, non-radiologist physicians (general practitioners and specialists) participated in this study. Methods: Semi-structured key informant interviews were conducted with participants. Data from these interviews were analyzed using a grounded theory approach. Results: DI ordering practices (both appropriate and inappropriate) emerged as the dominant theme in this research, specifically in the context of prevalence, decision-making, information support, contributing factors, and solutions. Particularly, the majority of participants felt that DI is overused in the medical field and identified contacting physicians (colleagues, specialists, or radiologists) and consulting the literature (using UpToDate® or Google Scholar) as their top methods of information support used in challenging clinical scenarios. Meanwhile, participants suggested factors that contribute to inappropriate ordering: patient demand, legal liability, and duplicate ordering. The majority of participants believed education could reduce inappropriate ordering. Participants also identified increasing communication about requisitions and restricting DI ordering authority as potential solutions to reduce inappropriate ordering. Conclusion: From the interviews, ordering (both appropriate and inappropriate ordering) emerged as the overarching theme. Findings were compared and contrasted to the current literature. Overall, this study revealed how human factors, such as patient demand, influence how a physician orders DI. As well, the majority of participants relied on the patient to recall patient DI history; however, literature suggests this method is unreliable. This study also offers unique insight into the physician’s perspective of what would be effective for reducing inappropriate ordering. These findings contribute to the field of health informatics as any technology developed to reduce inappropriate ordering (such as a clinical decision support system) needs to consider these human factors to support user acceptance. Through findings from this study, further research gaps emerged that can guide future research. / Graduate

Companies struggle against complex and dynamic conditions in order to survive in their business settings. Being in the knowledge era, learning has been accepted as one of the main sources of sustainable competitive advantage. Organizational learning (OL) is a set of activities to obtain organizational memory (OM) by acquiring, sharing, interpreting, integrating and institutionalizing knowledge. OM is exploited by the companies in strategic decision-making process, which makes OL a critical concept. The major objective of this study is to explore how construction companies create OM and how they exploit this asset in strategic decision-making process. In this context, an interview study is conducted with eight large Turkish construction companies and OM perceptions of each company are presented as case studies. This survey revealed the strengths and weaknesses in terms of OL competence. One of the key outputs is that companies are successful at acquiring and storing knowledge but they are not familiar with decision support systems (DSSs) that benefit from OM. Such systems enable OL by assisting decision makers in processing, assessing, integrating and organizing knowledge. To meet the requirements of the industry, a DSS is proposed to aid construction companies in international market entry decisions. This tool is generated under a software package by adopting case-based reasoning (CBR) as the problem solving approach, which finds solutions to new problems based on the past experiences. The model is developed by the acquisition of past real international project data as input information. The model produces two outputs that are indicators of attractiveness of a project and competitiveness of a company, which are the key decision criteria in international market entry problem.

As more information is brought into early stages of the design, more pressure is put on engineers to produce a reliable, high quality, and financially sustainable product. Unfortunately, requirements established at the beginning of a new project, and the environment that surrounds it, continue to change in some unpredictable ways. The risk of designing a system that may become obsolete during early stages of production is currently tackled by the use of robust design simulations, a method that allows to simultaneously explore a plethora of design alternatives and requirements with the intention of accounting for uncertain factors in the future. Whereas this design technique has proven to be quite an improvement in design methods, under certain conditions, it fails to consider the intrinsic value embedded in the system when certain design features are activated. This thesis introduces the concepts of adaptability and real options to manage risk foreseen in the face of uncertainty at early design stages. The method described herein allows decision-makers to foresee the financial impact of their decisions at the design level, as well as the exposure to risk. This thesis contains two relevant examples regarding the decision of introducing new technologies. First, the case study of Southwest Airlines, and the decision it took to retrofit blended winglets technology in its already delivered Boeing 737-700, is introduced to validate the proposed technique. In the second example, the manufacturer evaluates whether technologies should be included in a new aircraft engine design, left out, or offered as an option to retrofit in the future. This case demonstrates the benefits of each of these actions and the monetary value of offering retrofitting options as upgrades to the airlines when the value of the technology fluctuates considerably. The results obtained in both exercises show the benefits of real options analysis during the design process of aerospace systems. These include: a better management of design features over time, a better picture of uncertainty around future technology economics, a good understanding of adaptability value over time, and a consistent risk reduction with respect to alternatives in which flexibility was not embedded.

After surveying the state-of-the-art in evaluation of alternatives in the defense acquisition process, a methodology for the evaluation of the robustness of systems-of-systems alternatives was proposed. In the methodology, robustness is defined as the integral of the alternative s regret over the likelihood-weighted plausible scenario space. Surrogate modeling techniques were used to overcome shortcomings associated with conventional regret analysis, including the discrete nature of scenario cases and static results. The new methodology, called Global Regret Analysis, was tested using an example problem based on the air campaign over Iraq in Operation Desert Storm. The results of the testing indicate that the methodology can provide a measure of the robustness of different system-of-systems alternatives against a wide range of possible scenarios. The methodology was then demonstrated on the US Air Force s persistent, precision strike mission. The demonstration showed the ability of Global Regret Analysis to overcome issues associated with using a single or other small number of scenarios to evaluate systems-of-systems alternatives. The methodology was then compared to a variety of existing methods and found to have strength for a wide range of evaluation applications. The possibility of applying Global Regret Analysis for military mission planning and opportunities for future work were also discussed.

This research focuses on developing a crop decision planning model to help farmers make decisions for an upcoming crop year. The decisions consist of which crops to plant, the amount of land to allocate to each crop, when to grow, when to harvest, and when to sell. The objective is to maximize the overall profit subject to available resources under yield and price uncertainties. To help achieve this objective, we develop yield and price forecasting models to estimate the probable outcomes of these uncertain factors. The output from both forecasting models are incorporated into the crop decision planning model which enables the farmers to investigate and analyze the possible scenarios and eventually determine the appropriate decisions for each situation. This dissertation has three major components, yield forecasting, price forecasting, and crop decision planning. For yield forecasting, we propose a crop-weather regression model under a semiparametric framework. We use temperature and rainfall information during the cropping season and a GDP macroeconomic indicator as predictors in the model. We apply a functional principal components analysis technique to reduce the dimensionality of the model and to extract meaningful information from the predictors. We compare the prediction results from our model with a series of other yield forecasting models. For price forecasting, we develop a futures-based model which predicts a cash price from futures price and commodity basis. We focus on forecasting the commodity basis rather than the cash price because of the availability of futures price information and the low uncertainty of the commodity basis. We adopt a model-based approach to estimate the density function of the commodity basis distribution, which is further used to estimate the confidence interval of the commodity basis and the cash price. Finally, for crop decision planning, we propose a stochastic linear programming model, which provides the optimal policy. We also develop three heuristic models that generate a feasible solution at a low computational cost. We investigate the robustness of the proposed models to the uncertainties and prior probabilities. A numerical study of the developed approaches is performed for a case of a representative farmer who grows corn and soybean in Illinois.

This thesis studies two related topics in liner shipping. The first topic is the contract pricing problem for container carriers. The second part studies the interaction of the longer term contracts and the spot markets/exchanges for the same goods/services. Most containerized freight is transported under the provisions of medium term contracts between ocean carriers and shippers. One of the biggest challenges for an ocean carrier is to find optimal ways to structure the prices in those contracts. In particular, an ocean carrier would like to set the prices such that the best match between supply and demand can be obtained to maximize its profit. We propose three optimization models as decision tools that carriers can use to plan the contract price structures, as well as the anticipated freight flows and empty container flows for the period covered by the contracts. Based on the models, we propose algorithms and build decision tools that generate the following output: optimal prices to be charged for the movement of freight, the anticipated freight flows and empty flows, containers to be leased, rented and purchased, and the additional voyage capacities to be procured. The first two models are deterministic and represent the problem at different levels of detail. In addition, a three-stage stochastic model is proposed to handle uncertainties in demand rates, costs, bookings and transit times on feeder arcs. Recent developments in information technology and communication make spot transactions more economical and more convenient. Nevertheless, the incidental spot transactions still count for only a very small portion of freight transported both by the large carriers who are the leaders in implementing e-commerce and in the industry as a whole. The second part of the thesis studies models to provide insight into the effect of spot market participation rates on various economic quantities. This may have implications for freight transportation industries, such as the sea cargo industry, in which longer term contracts are still prevalent. We focus our study on the following situation. Option contracts are signed before the demand is observed. As is common in liner shipping, sellers (carriers) also sell goods/services on the spot. Buyers (shippers) may or may not buy in the spot market as a matter of policy. We investigate the effects of spot market participation on the contract market and on the surpluses of all market players. It is found that the contract market shrinks as more and more buyers participate in the spot market. However, the effects on the surpluses of different market players are much more complicated and depend on the following factors: market structure, demand variation along time, demand variation among buyers and capacity level.

The thesis begins by examining the evolution of human performance modeling from the initial stimulus-response methods introduced during the industrial revolution to model factory worker productivity, continues with a discussion of the information processing model where human cognition was modeled as a series of actions carried out in a predefined order, and ends with the concept of cognitive control whereby cognition is not considered a context-free mental process but modeled as an individual's ability to maintain control under varying contexts and to counter the effects of disturbances. The results from a preliminary evaluation conducted to determine if CCMs could be measured and if they provided any additional insight cognitive work are presented, and reveal that CCMs could be measured and the self-assessed CCM varied as predicted. A design process is developed which utilizes the CCMs as representing specific patterns of activity, thus specifying the design requirements. Following this design process, a prototype is created and evaluated using a controlled experiment to examine the effectiveness of the CWSS. The experiment examines performance, workload, and patterns of activity, and has several interesting findings. The first is that performance was independent of the almost all of the predictors and covariates including participant's Self-assessed CCM, with the exception of CCM transitions. As in the preliminary study, participants who reported transitioning between CCMs also reported decreased performance, increased frustration and actually performed worse. Second, perceived performance varied linearly with a participant's self-assessed CCM, but not with the actual performance. Third, participants report lower levels of effort when using a CWSS DM that matched their operational CCM. Finally, the design process successfully created a CWSS with DMs which support strategic and tactical CCMs. Unfortunately, no specific performance improvements were found for cases where the participant's CCM matched the DM as hypothesized, calling into question the effectiveness of creating different design modes for performance improvement. This thesis presents two methods for measuring CCMs: one direct single scale and one indirect composite scale. The measurements correlate highly. Both have a high degree of face validity and user acceptance. In the end, the composite measure may be a more robust measure of CCM because it provides a greater degree of diagnosticity by specifically inquiring after different aspects of CCM and is less susceptible to an individual's interpretation of the relative importance of the multiple dimensions of CCMs included in the definitions.

In simulation-based design, a key challenge is to formulate and solve analysis problems efficiently to evaluate a large variety of design alternatives. The solution of analysis problems has benefited from advancements in commercial off-the-shelf math solvers and computational capabilities. However, the formulation of analysis problems is often a costly and laborious process. Traditional simulation templates used for representing analysis problems are typically brittle with respect to variations in artifact topology and the idealization decisions taken by analysts. These templates often require manual updates and "re-wiring" of the analysis knowledge embodied in them. This makes the use of traditional simulation templates ineffective for multi-disciplinary design and optimization problems. Based on these issues, this dissertation defines a special class of problems known as variable topology multi-body (VTMB) problems that characterizes the types of variations seen in design-analysis interoperability. This research thus primarily answers the following question: How can we improve the effectiveness of the analysis problem formulation process for VTMB problems? The knowledge composition methodology (KCM) presented in this dissertation answers this question by addressing the following research gaps: (1) the lack of formalization of the knowledge used by analysts in formulating simulation templates, and (2) the inability to leverage this knowledge to define model composition methods for formulating simulation templates. KCM overcomes these gaps by providing: (1) formal representation of analysis knowledge as modular, reusable, analyst-intelligible building blocks, (2) graph transformation-based methods to automatically compose simulation templates from these building blocks based on analyst idealization decisions, and (3) meta-models for representing advanced simulation templates VTMB design models, analysis models, and the idealization relationships between them. Applications of the KCM to thermo-mechanical analysis of multi-stratum printed wiring boards and multi-component chip packages demonstrate its effectiveness handling VTMB and idealization variations with significantly enhanced formulation efficiency (from several hours in existing methods to few minutes). In addition to enhancing the effectiveness of analysis problem formulation, KCM is envisioned to provide a foundational approach to model formulation for generalized variable topology problems.