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Simulation-based optimization methodologies are widely applied in real world optimization problems. In developing these methodologies, beside simulation models, algorithms play a critical role. One example is an evolutionary multi objective optimization algorithm known as Reference point-based Non-dominated Sorting Genetic Algorithm-II (R-NSGA-II), which has shown to have some promising results in this regard. Its successor, R-NSGA-II-adaptive diversity control (hereafter Adaptive Epsilon-R-NSGA-II (AE-R-NSGA-II) algorithm) is one of the latest proposed extensions of the R-NSGA-II algorithm and in the early stages of its development. So far, little research exists on its applicability and usefulness, especially in real world optimization problems. This thesis evaluates behavior and performance of AE-R-NSGA-II, and to the best of our knowledge is one of its kind. To this aim, we have investigated the algorithm in two experiments, using two benchmark functions, 10 performance measures, and a behavioral characteristics analysis method. The experiments are designed to (i) assess behavior and performance of AE-R-NSGA-II, (ii) and facilitate efficient use of the algorithm in real world optimization problems. This is achieved through the algorithm parameter configuration (parametric study) according to the problem characteristics. The behavior and performance of the algorithm in terms of diversity of the solutions obtained, and their convergence to the optimal Pareto front is studied in the first experiment through manipulating a parameter of the algorithm referred to as Adaptive epsilon coefficient value (C), and in the second experiment through manipulating the Reference point (R) according to the distance between the reference point and the global Pareto front. Therefore, as one contribution of this study two new diversity performance measures (called Modified spread, and Population diversity), and the behavioral characteristics analysis method called R-NSGA-II adaptive epsilon value have been introduced and applied. They can be modified and applied for the evaluation of any reference point based algorithm such as the AE-R-NSGA-II. Additionally, this project contributed to improving the Benchmark software, for instance by identifying new features that can facilitate future research in this area. Some of the findings of the study are as follows: (i) systematic changes of C and R parameters influence the diversity and convergence of the obtained solutions (to the optimal Pareto front and to the reference point), (ii) there is a tradeoff between the diversity and convergence speed, according to the systematic changes in the settings, (iii) the proposed diversity measures and the method are applicable and useful in combination with other performance measures. Moreover, we realized that because of the unexpected abnormal behaviors of the algorithm, in some cases the results are conflicting, therefore, impossible to interpret. This shows that still further research is required to verify the applicability and usefulness of AE-R-NSGA-II in practice. The knowledge gained in this study helps improving the algorithm.

Hedging has been one of the most important topics in finance. How to effectively hedge the exposed risk draws significant interest from both academicians and practitioners. In a complete financial market, every contingent claim can be hedged perfectly. In an incomplete market, the investor can eliminate his risk exposure by superhedging. However, both perfect hedging and superhedging usually call for a high cost. In some situations, the investor does not have enough capital or is not willing to spend that much to achieve a zero risk position. This brings us to the topic of partial hedging. In this thesis, we establish the risk measure based partial hedging model and study the optimal partial hedging strategies under various criteria. First, we consider two of the most common risk measures known as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). We derive the analytical forms of optimal partial hedging strategies under the criterion of minimizing VaR of the investor's total risk exposure. The knock-out call hedging strategy and the bull call spread hedging strategy are shown to be optimal among two admissible sets of hedging strategies. Since VaR risk measure has some undesired properties, we consider the CVaR risk measure and show that bull call spread hedging strategy is optimal under the criterion of minimizing CVaR of the investor's total risk exposure. The comparison between our proposed partial hedging strategies and some other partial hedging strategies, including the well-known quantile hedging strategy, is provided and the advantages of our proposed partial hedging strategies are highlighted. Then we apply the similar approaches in the context of reinsurance. The VaR-based optimal reinsurance strategies are derived under various constraints. Then we study the optimal partial hedging strategies under general risk measures. We provide the necessary and sufficient optimality conditions and use these conditions to study some specific hedging strategies. The robustness of our proposed CVaR-based optimal partial hedging strategy is also discussed in this part. Last but not least, we propose a new method, simulation-based approach, to formulate the optimal partial hedging models. By using the simulation-based approach, we can numerically obtain the optimal partial hedging strategy under various constraints and criteria. The numerical results in the examples in this part coincide with the theoretical results.

Lagegeregelte Bewegungsachsen finden sich in nahezu allen aktuellen Werkzeug‐ und Produktionsmaschinen. Um eine hohe Qualität der hergestellten Produkte bei gleichzeitig möglichst hoher Effizienz sicherzustellen muss die Regelung der Bewegungsachsen genau auf das Regelstreckenverhalten abgestimmt sein. Informationen über die Regelstrecken können mit Verfahren der Identifikation ermittelt werden. Neben der Reglerparametrierung sind diese Informationen ebenfalls für Mechanismen der Maschinenüberwachung relevant, bei denen Fehlerzustände in den Maschinen und Anlagen (modellbasiert) frühzeitig erkannt werden sollen. Neben den Identifikationsmechanismen, welche derzeit im Bereich der Werkzeugmaschinen vorherrschend sind und meist auf Testsignalen basieren, können mit nichtinvasive Ansätzen wesentliche Parameter bestimmt werden, ohne in den Produktionsprozess einzugreifen zu müssen. Im vorliegend Beitrag wird dargestellt, wie derartige Verfahren im Umfeld der Werkzeugmaschine Anwendung finden können. Nach einer Einleitung wird im Beitrag der Stand der Technik zur Identifikation von Regelstreckenparametern an Bewegungsachsen beleuchtet. Das Hauptaugenmerk liegt dabei in der Unterscheidung von invasiven (Verfahren mit Testsignalen) und nichtinvasiven Identifikationsverfahren (ohne Testsignale). Aufbauend auf den Erkenntnissen der Literaturrecherche wird das eigene Vorgehen hinsichtlich Wahl der Modellstruktur und des Identifikationsverfahrens erläutert. Ausführungen zur Anregungsdetektion, gezielten Einflussnahe auf die Parameterschätzung, Fehlerbewertung und dem Einsatz der simulationsbasierten Optimierung schließen die theoretischen Betrachtungen zum Forschungsthema ab. Die erzielten Ergebnisse werden in einem weiteren Kapitel anhand eines Laborversuchsstandes und einer Werkzeugmaschine illustriert. Der letzte Abschnitt gibt eine Zusammenfassung des Forschungsvorhabens und erläutert zukünftige Schritte auf diesem Gebiet.

In today’s world, simulation and optimization are playing a vital role in reducing the time, cost and preserving resources. In manufacturing industries, there are ample amount of problems that go on with the expansion of the industry. In such cases, to tackle these problems simulation can be helpful to check whether any change in the current situation makes any effect on the current efficiency of the overall plant. In the presented case study, a solution to the problem of internal and external logistics has been designed by using simulation and optimization to improve part of a material flow of an organization. Basically, the organization whose major production is established in the south of Sweden deals with the manufacturing and assembly of equipment. Before the dispatch, all of them go to the painting section which is the expansion of the present shop floor. However, the design and analysis of the material handling system to feed the new painting line which is going to be established by the organization is the aim of this case study. While achieving this aim the literature regarding the discrete event simulation, Lean and Simulation-Based optimization related to the material handling system has been done. Furthermore, the appropriate material handling systems along with the different scenarios were suggested to reduce the cost and the lead times between the production line and the new painting line. To support this process a methodology combining simulation, optimization and lean production has been implemented under the framework of the design and creation research strategy. In the Kaizen workshop organized at a company with managers and stakeholders, the designed scenarios were presented and after some discussion one of them was chosen and the selected scenario was designed and optimized. Moreover, the Simulation-Based multi-objective optimization has been helpful for the optimization of the designed model proposed as a final solution.

The aim of this thesis is to examine optimization sensitivity in SCORE to the accuracy of particular input data models used in a simulation model of a production line. The purpose is to evaluate if it is sufficient to model input data using sample mean and default distributions instead of fitted distributions. An existing production line has been modeled for the simulation study. SCORE is based on maximizing any key performance measure of the production line while simultaneously minimizing the number of improvements necessary to achieve maximum performance. The sensitivity to the input models should become apparent the more changes required. The experiments concluded that the optimization struggles to obtain convergence when fitted distribution models were used. Configuring the input parameters to the optimization might yield better optimization result. The final conclusion is that the optimization is sensitive to what input data models are used in the simulation model.

This doctoral thesis investigates the application of simulation-based optimization (SBO) as an alternative to conventional optimization techniques when the inherent uncertainty and complex features of real manufacturing systems need to be considered. Inspired by a real-world production planning setting, we provide a general formulation of the situation as an extended knapsack problem. We proceed by proposing a solution approach based on single and multi-objective SBO models, which use simulation to capture the uncertainty and complexity of the manufacturing system and employ meta-heuristic optimizers to search for near-optimal solutions. Moreover, we consider the design of matheuristic approaches that combine the advantages of population-based meta-heuristics with mathematical programming techniques. More specifically, we consider the integration of mathematical programming techniques during the initialization stage of the single and multi-objective approaches as well as during the actual search process. Using data collected from a manufacturing company, we provide evidence for the advantages of our approaches over conventional methods (integer linear programming and chance-constrained programming) and highlight the synergies resulting from the combination of simulation, meta-heuristics and mathematical programming methods. In the context of the same real-world problem, we also analyse different single and multi-objective SBO models for robust optimization. We demonstrate that the choice of robustness measure and the sample size used during fitness evaluation are crucial considerations in designing an effective multi-objective model.

Introduction: Controversy exists in the simulation community as to the emotional and educational ramifications of mannequin death due to learner action or inaction. No theoretical framework to guide future investigations of learner actions currently exists. The purpose of our study was to generate a model of the learner experience of mannequin death using a mixed methods approach. Methods: The study consisted of an initial focus group phase composed of 11 learners who had previously experienced mannequin death due to action or inaction on the part of learners as defined by Leighton (Clin Simul Nurs. 2009;5(2):e59-e62). Transcripts were analyzed using grounded theory to generate a list of relevant themes that were further organized into a theoretical framework. With the use of this framework, a survey was generated and distributed to additional learners who had experienced mannequin death due to action or inaction. Results: were analyzed using a mixed methods approach. Results: Forty-one clinicians completed the survey. A correlation was found between the emotional experience of mannequin death and degree of presession anxiety (P < 0.001). Debriefing was found to significantly reduce negative emotion and enhance satisfaction. Sixty-nine percent of respondents indicated that mannequin death enhanced learning. These results were used to modify our framework. Conclusions: Using the previous approach, we created a model of the effect of mannequin death on the educational and psychological state of learners. We offer the final model as a guide to future research regarding the learner experience of mannequin death.

While the body of evidence supporting the use of simulation-based learning in the education of health professionals is growing, howor why simulation-based learning works is not yet understood. There is a clear need for evidence, grounded in contemporary educational theory, to clarify the features of simulation instructional design that optimize learning outcomes and efficiency in health care professional students. Cognitive Load Theory (CLT) is a theoretical framework focused on a learner’s working memory capacity. One principle of CLT is example based learning. While this principle has been applied in both traditional classroom and laboratory settings, and has shown positive performance and learning outcomes, example based learning has not yet been applied to the simulation setting. This study had two main objectives: to explore if the example-based learning principle could successfully be applied to the simulation learning environment, and to establish response process validation evidence for a tool designed to measure types of cognitive load. Fifty-eight novice students from nursing, podiatric medicine, physician assistant, physical and occupational therapy programs participated in a blinded randomized control study. The dependent variable was the simulation brief. Participants were randomly assigned to either a traditional brief or a facilitated tutored problem brief. Performance outcomes were measured with verbal communications skill presented in the Introduction, Situation, Background, Assessment, Recommendation (I-SBAR) format. Response process evidence was collected from cognitive interviews of 11 students. Results indicate participation in a tutored problem brief led to statistically significant differences at t(52)=-3.259, p=.002 in verbal communication performance compared to students who participated in a traditional brief. Effect size for this comparison was d=(6.06-4.61)/1.63 = .89 (95% CI 0.32-1.44). Response process evidence demonstrated that additional factors unique to the simulationlearning environment should be accounted for when measuring cognitive load in simulation based learning (SBL). This study suggests that example based learning principles can be successfully applied to SBL and result in positive performance outcomes for health professions students. Additionally, measures of cognitive load do not appear to capture all contribution toload imposed by the simulation environment.

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