A simulation optimization for breast cancer screening in Turkey

Ankara : The Department of Industrial Engineering and the Graduate School of Engineering and Science of Bilkent University, 2014.Thesis (Master's) -- Bilkent University, 2015.Includes bibliographical references leaves 69-83.Breast cancer is the most common cancer type among women in the world. 6.3 million women were diagnosed with breast cancer between 2007 - 2012 and 25% of cancers in women are breast cancer. Early diagnosis and early detection has an important role in survival from breast cancer. Mammographic screening is proved to be the only screening method that can reduce breast cancer mortality. Even though mammographic screening has this significant benefit, it is expensive and it can decrease life quality and it can generate false positive results. As a consequence, recommending an effective and costefficient mammographic screening policy in terms of starting and ending ages and screening frequencies has high importance. This study aims to optimize Ada’s Breast Cancer Simulation Model using Simulated Annealing. This model was run for Turkish women born in 1980 during their lifetime. The purpose of this study is to obtain an optimal or near optimal policy in terms of life years gained and cost for Turkish women. This study also aims to demonstrate the outcomes in terms of effectiveness and cost when different combinations of policy variables are used.Keyf, DilekM.S

Online Simulation in Semiconductor Manufacturing

In semiconductor manufacturing discrete event simulation systems are quite established to support multiple planning decisions. During the recent years, the productivity is increasing by using simulation methods. The motivation for this thesis is to use online simulation not only for planning decisions, but also for a wide range of operational decisions. Therefore an integrated online simulation system for short term forecasting has been developed. The production environment is a mature high mix logic wafer fab. It has been selected because of its vast potential for performance improvement. In this thesis several aspects of online simulation will be addressed: The first aspect is the implementation of an online simulation system in semiconductor manufacturing. The general problem is to achieve a high speed, a high level of detail, and a high forecast accuracy. To resolve these problems, an online simulation system has been created. The simulation model has a high level of detail. It is created automatically from underling fab data. To create such a simulation model from fab data, additional problems related to the underlying data arise. The major parts are the data access, the data integration, and the data quality. These problems have been solved by using an integrated data model with several data extraction, data transformation, and data cleaning steps. The second aspect is related to the accuracy of online simulation. The overall problem is to increase the forecast horizon, increase the level of detail of the forecast and reduce the forecast error. To provide useful forecast results, the simulation model contains a high level of modeling details and a proper initialization. The influences on the forecast quality will be analyzed. The results show that the simulation forecast accuracy achieves good quality to predict future fab performance. The last aspect is to find ways to use simulation forecast results to improve the fab performance. Numerous applications have been identified. For each application a description is available. It contains the requirements of such a forecast, the decision variables, and background information. An application example shows, where a performance problem exists and how online simulation is able to resolve it. To further enhance the real time capability of online simulation, a major part is to investigate new ways to connect the simulation model with the wafer fab. For fab driven simulation, the simulation model and the real wafer fab run concurrently. The wafer fab provides several events to update the simulation during runtime. So the model is always synchronized with the real fab. It becomes possible to start a simulation run in real time. There is no further delay for data extraction, data transformation and model creation. A prototype for a single work center has been implemented to show the feasibility

Using Simulation and Genetic Algorithms to Improve Cluster Tool Performance

In this paper, we present an approach to generate optimal processing sequences of lots at cluster tools. We consider the problem of sequencing n lots, where each lot can be processed by any of m available cluster tools. The proposed method combines simulation and a genetic algorithm to generate lot sequences. We show that our approach leads to a significant reduction of cycle times at cluster tools

Using Simulation and Genetic Algorithms to Improve Cluster Tool Performance

In this paper, we present an approach to generate optimal processing sequences of lots at cluster tools. We consider the problem of sequencing n lots, where each lot can be processed by any of m available cluster tools. The proposed method combines simulation and a genetic algorithm to generate lot processing sequences

Genetic Algorithms and Evolutionary Computation

Abstract Generally speaking, genetic algorithms are simulations of evolution, of what kind ever. In most cases, however, genetic algorithms are nothing else than probabilistic optimization methods which are based on the principles of evolution. Using simulation and Genetic Algorithms to improve cluster tool performance, Mooring Pattern Optimization using Genetic Algorithms. This paper is designed to cover a few important application aspects of genetic algorithm under a single umbrella