The report presents a study of the operations at Rollmek AB. Rollmek is a company that manufactures various components for Panthera wheelchairs including backrests, chassis and leg rests. The thesis aims to develop various improvement proposals for the case company's painting department with regard to increasing production capacity. To achieve the goal, a simulation model has been constructed in the simulation program FACTS Analyzer, which reflects the department in question. The simulation model is based on three different variants with five different colours. For the construction of simulation models, data collected via various qualitative and quantitative research methods, including time studies, observations, and interviews. Improvement proposals that were developed were implemented in the simulation model and experimented based on the project's requirements. The study's results have led to five specific suggestions for improvement. Through the use of simulation models, data has been generated on the number of units produced and units produced/hour for the three product variations with associated color variations. With the help of these improvement suggestions, the company could implement them in production to increase its capacity to meet future demand.

This thesis aims to fill the gaps in the literature of the theory of constraints (TOC) in publicly funded health systems. While TOC seems to be a natural fit for this resource-constrained environment, there are still no reported application of TOC’s drum-buffer-rope tool and inadequate customizations with regards to defining system-wide goal and performance measures. The “Drum-Buffer-Rope for an Outpatient Cancer Facility” chapter is a real world case study exploring the usefulness of TOC’s drum-buffer-rope scheduling technique in a publicly funded outpatient cancer facility. With the use of a discrete event simulation model populated with historical data, the drum-buffer-rope scheduling policy is compared against “high constraint utilization” and “low wait time” scenarios. Drum-buffer-rope proved to be an effective mechanism in balancing the inherent tradeoff between the two performance measures of instances of delayed treatment and average patient wait time. To find the appropriate level of compromise in one performance measure in favor of the other, the linkage of these measures to system-wide performance measures are proposed. In the “Theory of Constraints’ Performance Measures for Publicly Funded Health Systems” chapter, a system dynamics representation of the classical TOC’s system-wide goal and performance measures for publicly traded for-profit companies is developed, which forms the basis for developing a similar model for publicly funded health systems. The model is then expanded to include some of the factors that affect system performance, providing a framework to apply TOC’s process of ongoing improvement in publicly funded health systems. The “Connecting Low-Level Performance Measures to the Goal” chapter attempts to provide a framework to link the low-level performance measures with system-wide performance measures. It is claimed that until such a linkage is adequately established, TOC has not been fully transferred to publicly funded health systems.

The Swedish health care system plays a vital role in satisfying the citizens’ demands for quality health care services. To deliver the right services in time in a hospital, an efficient admission plan is required, but this can be difficult to achieve. The Department of Pulmonary Medicine at the University Hospital in Linköping needs a better admission plan for their patients. In the department, the patient demand does not match the capacity, which leads to overcrowding at the ward. The aim of this thesis is to improve the admission plan of patients for the ward at the Department of Pulmonary Medicine by using discrete event simulation. To fulfil the aim, a simulation study is performed to understand how the system is working, where the problems emerged and how to prevent overcrowding. Different experiments are performed to check what could improve the admission plan at the ward. The results from this study shows that an improvement of the admission plan could be reached by better cooperation between involved units, improved documentation at the Department, a queue system of patients based on medical priorities and changed number of care beds. These solutions can prevent overcrowding and deliver health care services in time.

Introduction: Cigarette smoking is associated with lung cancer, cardiovascular disease, and chronic respiratory conditions. It is responsible for high mortality and morbidity risk in the US population. Smokers find sudden quitting difficult and it is reported that a large number of unassisted quitting attempts are eventually unsuccessful. Electronic cigarette or e-cig is a novel battery-driven, nicotine delivery product, currently being used as a smoking cessation tool by current and former smokers. Since its resemblance to a conventional cigarette, and its non-combustible nature, e-cig use has risen exponentially in the last few years. To address such public health issues, the US FDA is working on formulating regulations to manufacture, market, and distribute e-cigs has called for research evidence on the long term use of e-cig use. Objective: The objective of this study was to develop and validate a Discrete Event Simulation model to simulate the electronic cigarette (e-cig) use behavior, and to estimate the long term e-cig use prevalence, in different groups of the US population. Methods: The model population was generated from analyzing the National Health Interview Survey data from 2011-2013. The population was categorized into current, recent former, late former and never smokers. Population birth rates and death rates were applied using the 2012 US Census Bureau data. Model parametrization, transition probabilities and e-cig related risks were obtained and applied using cross sectional survey and longitudinal e-cig studies done on US population. The model was run for the period of 15 years and e-cig use prevalence at the end of the simulation period was estimated. Each simulation was replicated 100 times using Monte Carlo simulation approach. Model validation was performed by the use of null and extreme input values (internal validation), examining programing codes (debugging), verification by tobacco science and system analysis experts (structural and technical validation), comparison of model’s first year results with CDC reports (external validation). Conclusion: Total projected e-cig prevalence in the US population at the end of simulation of period was found to be around 19%. The results showed a gradual reduction in the number of conventional cigarette smokers and an increase in the e-cig users over the simulation period. Highest e-cig users were old, male, white and had less than high school level education. Sensitivity analyses of various model parameters showed that the e-cig prevalence was most sensitive to the impact and timing of policy implementation. As a novel nicotine delivery system, e-cigs are rapidly gaining acceptance in the US and recent reports have shown an exponential rise in the popularity of e-cig among minors and young adults. Our research provides empirical evidence that can be used by the scientific community and regulatory bodies to formulate regulations for marketing and sales of e-cigs in various sections of the population, where the prevalence is expected to rise in future. Our study can also guide the policy makers to introduce relevant policies at specific time points when the e-cig use is expected to rise.

Discrete-event simulations are increasingly being used to solve problems and to aid in decision making which are proving useful in the manufacturing industry. The main aim for this thesis was to compare the current production line and how implementing changes for a future state as a supporting basis for making the decision. The theoretical framework focused on the Lean philosophy merged with simulation-based methods. The simulation model was build using the collected data. A time study was conducted in order to verify the process and setup times since these were only estimated at the time. Two simulation models were built for the current production line and the future state. The future state was based on the current one since no changes in the process and setup times would be made during the two simulation-model. Experiments were then done to compare the different states, one with batch and the other single-piece flow. The parameters were set on equal terms and the compared values were throughput (TH), lead-time (LT) and work in process (WIP). The conclusion drawn from the results is that the future state would be more efficient.

A presente pesquisa aborda o problema de dimensionamento de frota de navios rebocadores do tipo AHTS, que são utilizados essencialmente nas tarefas de operações de apoio à exploração e produção de petróleo offshore (em alto mar). Essas atividades se caracterizam pela requisição simultânea de múltiplos navios de classes diferentes, e possuem parâmetros como: compatibilidade de classes de navios com as tarefas, duração em dias, local de execução e instante desejado de atendimento. Para representar este problema foi desenvolvido um modelo de simulação com parâmetros estocásticos, cuja programação é orientada para minimização dos custos totais da operação, que englobam custos fixos, custos de penalidade por atraso no atendimento das tarefas, e penalidade por falta de cumprimento de tarefas. A abordagem de solução do modelo é a busca exaustiva onde são comparados cenários de simulação de eventos discretos. Adicionalmente, foram comparadas 2 modos de escolhas de tarefas na fila de tarefas, o primeiro é o modo FIFO (First In First Out), o segundo modo é a priorização de tarefas com maior custo de penalidade associado para o dimensionamento de frota. / This research addresses a fleet sizing problem of anchor and handling and tug supply vessels (AHTS), which support the exploration and production of oil at the sea. The support activities are characterized by simultaneous request of multiple vessels of one or more classes. Other characteristics of the research problem are:: the compatibility between vessels and tasks, task duration (in days), a place of execution the task and a desired instant to be attended. A simulation model with stochastic parameters was developed to represent this problem, aiming to minimize the total operational cost that includes fixed costs,penalty costs if tasks are delayed and penalty costs with not completed tasks. The strategy to solve this problem was the exhausted search through discrete-event simulation. Aditionally, 2 methods of approach for the queue were analyzed: the first one is the FIFO (First In First Out) and the second one is the priority according the highest penalty cost to size the fleet.

O objetivo deste trabalho é propor um procedimento híbrido para a solução do problema de formação de células de manufatura com réplicas de máquinas. Constrói-se um modelo matemático de otimização multiobjetivo cujos valores das funções-objetivo são obtidos por meio da execução de um modelo de simulação de eventos discretos, o qual representa um sistema de manufatura celular. Em seguida, geram-se soluções eficientes segundo o conceito de otimalidade de Pareto através de um processo de busca por valores ótimos executado por um algoritmo genético. Três funções-objetivo conflitantes são consideradas: inventário em processo, movimentação intercelular e investimento total em máquinas. Um algoritmo de análise de agrupamento é utilizado para a redução do conjunto final de soluções. A eficácia do procedimento é avaliada mediante a aplicação a dois casos da literatura. Os resultados obtidos são analisados e comentados. Conclui-se, por fim, que o procedimento é capaz de gerar um conjunto de configurações sub-ótimas equivalentes para as células de manufatura, representando aproximadamente os trade-offs entre as três funções-objetivo. / The purpose of this work is to propose a hybrid procedure for solving the manufacturing cell formation problem. A multiobjective optimization model is built whose objective function values are realized by running a discrete-event simulation model, which represents a cellular manufacturing system. Thereafter, efficient solutions are generated following the Pareto optimality concept through a search for optimum values carried out by a genetic algorithm. Three conflicting objective functions are considered, namely, work-in-process, intercell moves and total machine investment. A clustering algorithm is applied to the final solution set so as to reduce it. The procedure efficacy is evaluated via its application to two cases from the literature. The obtained results are analyzed and commented. Finally, it is concluded that the procedure is capable of generating a set of equivalent sub-optimal manufacturing cell configurations, representing approximately the trade-offs betvveen the objective functions adopted.

O objetivo deste trabalho é propor um procedimento híbrido para a solução do problema de formação de células de manufatura com réplicas de máquinas. Constrói-se um modelo matemático de otimização multiobjetivo cujos valores das funções-objetivo são obtidos por meio da execução de um modelo de simulação de eventos discretos, o qual representa um sistema de manufatura celular. Em seguida, geram-se soluções eficientes segundo o conceito de otimalidade de Pareto através de um processo de busca por valores ótimos executado por um algoritmo genético. Três funções-objetivo conflitantes são consideradas: inventário em processo, movimentação intercelular e investimento total em máquinas. Um algoritmo de análise de agrupamento é utilizado para a redução do conjunto final de soluções. A eficácia do procedimento é avaliada mediante a aplicação a dois casos da literatura. Os resultados obtidos são analisados e comentados. Conclui-se, por fim, que o procedimento é capaz de gerar um conjunto de configurações sub-ótimas equivalentes para as células de manufatura, representando aproximadamente os trade-offs entre as três funções-objetivo. / The purpose of this work is to propose a hybrid procedure for solving the manufacturing cell formation problem. A multiobjective optimization model is built whose objective function values are realized by running a discrete-event simulation model, which represents a cellular manufacturing system. Thereafter, efficient solutions are generated following the Pareto optimality concept through a search for optimum values carried out by a genetic algorithm. Three conflicting objective functions are considered, namely, work-in-process, intercell moves and total machine investment. A clustering algorithm is applied to the final solution set so as to reduce it. The procedure efficacy is evaluated via its application to two cases from the literature. The obtained results are analyzed and commented. Finally, it is concluded that the procedure is capable of generating a set of equivalent sub-optimal manufacturing cell configurations, representing approximately the trade-offs betvveen the objective functions adopted.

In this study, an approach is proposed to model and simulate the systems engineering process of design projects. One of the main aims is to model the systems engineering process, treating the process itself as a complex system. A conceptual model is developed as a result of a two-phase survey conducted with systems engineers. The conceptual model includes two levels of activity networks. Each first level systems engineering activity has its own network of second level activities. The model is then implemented in object oriented modeling language, namely SysML, using block definition diagrams and activity diagrams. Another aim is to generate a discrete event simulation model of the process for performance evaluation. For this purpose the SysML model is transformed to an Arena model using an Excel interface and VBA codes. Three deterministic and three stochastic cases are created to represent systems engineering process alternatives, which originate from the same conceptual model but possess different activity durations, resource availabilities and resource requirements. The scale of the project and the effect of uncertainty in activity durations are also considered. The proposed approach is applied to each of these six cases, developing the SysML models, transforming them to Arena models, and running the simulations. Project duration and resource utilization results are reported for these cases.

During development of a complex system, feasibility initially overshadows other concerns, in some cases leading to a design which may not be viable long-term. In particular for the case of Reusable Launch Vehicles, Operations&Maintenance comprises the majority of the vehicle's LCC, whose stochastic nature precludes direct analysis. Through the use of simulation, probabilistic methods can however provide estimates on the economic behavior of such a system as it evolves over time. Here the problem of operations optimization is examined through the use of discrete event simulation. The resulting tool built from the lessons learned in the literature review simulates a RLV or fleet of vehicles undergoing maintenance and the maintenance sites it/they visit as the campaign evolves over a period of time. The goal of this work is to develop a method for uncovering an optimal operations scheme by investigating the effect of maintenance technician skillset distributions on important metrics such as the achievable annual flight rate and maintenance man hours spent on each vehicle per flight. Using these metrics, the availability of technicians for each subsystem is optimized to levels which produce the greatest revenue from flights and minimum expenditure from maintenance.