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Predictive decision theory explains how humans should make decisions in practice given that they are not always perfect rational decision makers. Based on the prescriptive decision theory, this study addresses criticism of universal decision making models for supplier selections which have the notion of being used uniquely in all types of supplier choice situations. Issues raised are whether this performance is correct and how a dynamic decision-making model could create a more customized decision-making model which does not add much responsibility to the decision maker's judgment. A case study at Logosol, a small industrial company identifies by process mapping a complex supplier selection process consisting of three steps; prototype creation, null-series and production, based on test manufacturing. The supplier selection process is used as the basis for the creation of a dynamic decision making model. Dynamic decision-making models have the basic principle of learning from a decision and using that information in the next decision, which is considered useful in the business case as they not only use test manufacturing for product testing but also for collecting information about the supplier. Finally, the created dynamic decision model shows that universal decision-making models cannot be used in many complex supplier selection processes involving a plurality of steps. The model must be adapted to the company's individual process, however the identification method or some parts of the model used in this study may be used to create a dynamic decision model for other companies or organizations. / Preskriptiv beslutsteori säger hur människan borde fatta beslut i praktiken givet att de inte alltid är perfekt rationella beslutsfattare. Utifrån den preskriptiva beslutsteorin riktas i denna studie kritik mot universella beslutsmodeller för leverantörsval vilka har föreställningen om sig att användas kunna användas universellt i alla olika typer av leverantörsvalssituationer. Frågeställningar som väckts är om denna föreställning stämmer, samt hur en dynamisk beslutsmodell skulle kunna skapa en mer anpassad beslutsmodell vilken inte lägger lite mycket ansvar på beslutsfattarens omdöme. Genom en fallstudie hos Logosol ett litet industriföretag identifieras med hjälp av processkartläggning en komplex leverantörsvalsprocess innefattande tre steg; prototypskapande, nollserieskapande samt produktion utifrån testtillverkning. Fallföretagets leverantörsvalsprocess används som grund för skapande av en dynamisk beslutsmodell. Dynamiska beslutsmodeller har den grundläggande principen att medta lärdom från ett beslut in i nästa, vilket anses användbart för fallföretagets leverantörsvalsprocess då dom inte bara använder testtillverkning för test av produkt utan även för insamling av information om leverantören. Studien visar att föreställningen stämmer, den skapade dynamiska beslutsmodellen visar att universella beslutsmodeller inte är särskilt användbara i många komplexa leverantörsvalsprocesser innefattande ett flertal steg. Modellen måste anpassas för företagets individuella process, dock kan identifieringsmetoden eller vissa delar av modellen som använts i denna studie möjligtvis användas för att skapa en dynamisk beslutsmodell för andra företag eller organisationer.

Title from PDF of title page. / Document formatted into pages; contains 104 pages. / Thesis (M.S.I.E.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Medical decision problems are extremely complex owing to their dynamic nature, large number of variable factors, and the associated uncertainty. Decision support technology entered the medical field long after other areas such as the airline industry and the manufacturing industry. Yet, it is rapidly becoming an indispensable tool in medical decision making problems including the class of sequential decision problems. In these problems, physicians decide on a treatment plan that optimizes a benefit measure such as the treatment cost, and the quality of life of the patient. The last decade saw the emergence of many decision support applications in medicine. However, the existing models have limited applications to decision problems with very few states and actions. An urgent need is being felt by the medical research community to expand the applications to more complex dynamic problems with large state and action spaces. / ABSTRACT: This thesis proposes a methodology which models the class of sequential medical decision problems as a Markov decision process, and solves the model using a simulation based reinforcement learning (RL) algorithm. Such a methodology is capable of obtaining near optimal treatment strategies for problems with large state and action spaces. This methodology overcomes, to a large extent, the computational complexity of the value-iteration and policy-iteration algorithms of dynamic programming. An average reward reinforcement-learning algorithm is developed. The algorithm is applied on a sample problem of treating hereditary spherocytosis. The application demonstrates the ability of the proposed methodology to obtain effective treatment strategies for sequential medical decision problems. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

The aim of this thesis is partly to create a dynamic decision model for Sandvik Machining Solutions distribution flows and partly to evaluate how the stock transfer system logic handle four specific exceptional situations. The purpose is to reduce the total costs while keeping or improving the service level. The thesis presents a total cost model and guidelines for the planning function when deciding the main supplier in the distribution. The thesis also presents a system logic evaluation of the stock transfer logic used by Sandvik Machining Solutions.

This thesis focuses on decision support for regional LTL carriers. The basic operating characteristics of regional LTL carriers are similar to those of national LTL carriers, i.e., they operate linehaul networks with satellites, breakbulks, and relays to consolidate freight so as to be able to cost-effectively serve their customers. However, there are also key differences. Most importantly, because the area covered by a regional carrier is smaller, a regional carrier handles less freight (sometimes significantly less) and therefore typically has fewer consolidation opportunities, which results in higher handling and transportation costs per unit of freight. Consequently, competing with national carriers on price is difficult. Therefore, to gain or maintain market share, regional carriers have to provide better service. To be able to provide better service, regional carriers have to be more dynamic, e.g., they have to be able to deviate from their load plan when appropriate, which creates challenges for decision makers. Regional carriers deliver about 60% of their shipments within a day and almost all of their shipments within two days. Furthermore, most drivers get back to their domicile at the end of each day. Therefore, the focus of the thesis is the development of effective and efficient decision models supporting daily operations of regional LTL carriers which provide excellent service at low cost. This thesis presents an effective solution approach based on two optimization models: a dynamic load planning model and a driver assignment model. The dynamic load planning model consists of two parts: an integer program to generate the best paths for daily origin-destination freight volumes and an integer program to pack freight into trailers and trailers into loads, and to determine dispatch times for these loads. Techniques to efficiently solve these integer program solution are discussed in detail. The driver assignment model is solved in multiple stages, each stage requiring the solution of a set packing models in which columns represent driver duties. Each stages determines admissible driver duties. The quality and efficiency of the solution approach are demonstrated through a computational study with real-life data from one of the largest regional LTL carriers in the country. An important "technique" for reducing driver requirements is the use of meet-and-turn operations. A basic meet-and-turn operation involves two drivers meeting at a location in between terminals and exchange trucks. A parking lot or a rest area suffices as a meet-and-turn location. This ensures that drivers return to the terminal where they started. More sophisticated meet-and-turn operations also exist, often called drop and hook operations. In this case, drivers do not exchange trucks, but one of their trailers. The motivation in this case is not to get drivers back to their domicile, but to reduce load- miles. The thesis presents analytical results quantifying the maximum benefits of using meet and turn operations and optimization techniques for identifying profitable meet-and-turn opportunities.

The modern business environment requires managers to make decisions in a dynamic and uncertain world. In the current study, experimenters investigated the effects of a brief training aimed at improving dynamic decision making (DDM) skills on individual performance in a virtual DDM task. During the training, experimenters explained the DDM process, stressed the importance of self-reflection in DDM, and provided 3 selfreflective questions to guide participants during the task. Additionally, experimenters explored whether participants low or high in self-reflection would perform better in the task and whether participants low or high in self-reflection would benefit more from the training. Participants were 68 graduate business students. They individually managed a computer-simulated chocolate production company called CHOCO FINE and answered surveys to assess self-reflection and demographics. Results showed that students trained in DDM made decisions leading to better management performance in CHOCO FINE compared to untrained students. Self-reflection scores also predicted performance in this virtual business, and participants low in self-reflection benefitted the most from training. Organizations could use DDM training to establish and promote a culture that values selfreflective decision making.

The current study examined the problem solving behaviors of novices and experts in a complex computer simulation. Dynamic decision-making and complex problem solving abilities were analyzed to investigate if experts are the most successful of all participants when simulating the role of CEO of a chocolate factory, CHOCO FINE. Participants included novices, business undergraduate students and psychology undergraduate students, and experts, small business owners. Results revealed that small business owners engaged in the most successful dynamic decision-making strategies. Experts compared to novices had more total monies at the end of the simulation, spent more time in the first two months of twenty-four months, spent less money on information collection overall, made the most changes in representatives and advertising, and less changes in market research. This study addressed the differences between novices and experts not only in performance, but also in behavior in a complex and uncertain situation. The findings from this research enhance the dearth of research in addressing the relationship between behavior strategy and performance specifically in the area of expertise. The research at hand extends the previous literature within the domain of decision-making and provides insight for the differences in behavior strategies between novice and expert subjects.

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This dissertation set out to understand the decision processes used by decision makers in adversarial environment by setting up an adversarial decision making microworld, as an experimental platform, using a real time strategy (RTS) game called Rise of Nations (RON). The specific objectives of this dissertation were: 1.Contribute to the validation of recognition-primed decision (RPD) model in a simulated adversarial environment; 2.Explore the roles of common-sense strategies in decision making in the adversarial environment; and 3.Test the effectiveness of training recommendations based on the RPD model. Three related experimental studies were setup to investigate each of the objectives. Study 1 found that RPD model was partly valid where RPD processes were prevalently used but other decision processes were also important in an adversarial environment. A new decision model (ConPAD model) was proposed to capture the nature of decision making in the adversarial environment. It was also found that cognitive abilities might have some effects on the types of decision processes used by the decision makers. Study 2 found that common-sense strategies were prevalent in the adversarial environment where the participants were able to use all but one of the warfare related strategies extracted from literature without teaching them. The strategy familiarization training was not found to significantly improve decision making but showed that common-sense strategies were prevalent and simple familiarization training was not sufficient to produce differences in strategy usage and performances from the novice participants. Study 3 also found that RPD based training (cue-recognition and decision skill training) were not significant in producing better performance although subjective feedback found such training to be useful. However, the participants with RPD based training conditions were able to perform on the same level as the expert participants bridging the gap between novices and experts. Based on the findings, it was recommended that decision training should involve not just RPD based training, but comparisons of attributes as well. A more interactive training combining common-sense strategies, cue-recognition and decision skill training might be more useful. More theoretical experimentation would be required to validate the new decision model proposed in this dissertation. / Ph. D.

Cette thèse se focalise sur l’amélioration de l’évaluation de performance des processus métiers collaboratifs. Il s’agit de poursuivre l’évolution de la collaboration entre l’entreprise et ses partenaires. Trois niveaux d’abstraction ont été d’abord identifiés : Métier, fonctionnel et applicatif. Ensuite, nous avons développé une approche descendante allant du niveau métier au niveau applicatif. Dans le niveau métier, des différents d’indicateurs clés de performance ont été proposés à travers la méthodeECOGRAI. Dans le niveau applicatif, nous avons proposé un référentiel d’analyse contenant des indicateurs techniques fonctionnels tels que la durée, l’input, l’output, et non-fonctionnels notamment la maturité, le risque, l’interopérabilité à partir des traces d’exécution. Nous avons proposé ainsi un modèle ontologique en vue de capitaliser et enrichir la sémantique de la performance de ces processus. Nous avons proposé un modèle ascendant pour l’agrégation des indicateurs technique au niveaumétier. Le principal objectif de cette agrégation est la corrélation entre le comportement de l’application métier agrégé à partir de l’exécution et l’évolution des indicateurs métiers. Un autre modèle de gestion des événements métiers a été également proposé pour consolider le processus d’apprentissage de notre approche. Par ailleurs, pour assurer la convergence de la performance, nous avons combiné entre la gestion des traces et la gestion des évènements métiers. Cette combinaison permet d’accompagner l’évolution des processus métiers collaboratifs pendant leur exécution. L’accompagnement évoqué avant favorise l’obtention d’un diagnostic sur la performance pour servir à la prise de décision. Cette dernière est liée étroitement à la détection des alertes et particulièrement à l’anticipation des déviations de la performance le plus rapidement possible. Pour valider lacontribution scientifique de cette thèse, une étude de cas a été réalisée sur un processus de création de devis dans le cadre du projet européen FITMAN. / This thesis focuses on improving the performance evaluation of collaborative business processes. It is about pursuing the evolution of the collaboration between the company and its partners. In the beginning, three abstraction levels were identified: Business, functional and application. Then, we developed a top-down approach from the business level to the application level. In the business level, different key performance indicators have been proposed through the ECOGRAI method. In the application level, we proposed an analytical repository containing functional technical indicators such as duration, input, output, and non-functional, including maturity, risk, and interoperability based on execution traces. We have thus proposed an ontological model in order to capitalize and enrich the semantics of the performance of these processes. We proposed a bottom-up model for the aggregation of technical indicators at the business level. The main objective of this aggregation is the correlation between the behavior of the aggregated business application from the execution and the evolution of the business indicators. Another business event management model was also proposed to consolidate the learning process of our approach. Moreover, to ensure the convergence of performance, we have combined traces management and business event management. This combination allows to accompany the evolution of the collaborative business processes during their execution. The aforementionedaccompaniment favors the obtaining of a diagnosis on performance to be used for decision-making. The latter is closely linked to the detection of alerts and particularly to the anticipation of deviations in performance as quickly as possible. To validate the scientific contribution of this thesis, a case study was carried out on a process of creation of quote within the framework of the European project FITMAN.