Browsing by Subject "Simulation Optimization"

Now showing 1 - 2 of 2

Open Access
A simulation optimization for breast cancer screening in Turkey
(2014) Keyf, Dilek
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.
Open Access
Tabu search with fully sequential procedure for simulation optimization
(2003) Çevik, Savaş
Simulation is a descriptive technique that is used to understand the behaviour of both conceptual and real systems. Most of the real life systems are dynamic and stochastic that it may be very difficult to derive analytical representation. Simulation can be used to model and to analyze these systems. Although simulation provides insightful information about the system behaviour, it cannot be used to optimize the system performance. With the development of the metaheuristics, the concept simulation optimization has became a reality in recent years. A simulation optimization technique uses simulation as an evaluator, and tries to optimize the systems performance by setting appropriate values of simulation input. On the other hand, statistical ranking and selection procedures are used to find the best system design among a set of alternatives with a desired confidence level. In this study, we combine these two methodologies and investigate the performance of the hybrid procedure. Tabu Search (TS) heuristic is combined with the Fully Sequential Procedure (FSP) in simulation optimization context. The performance of the combined procedure is examined in four different systems. The effectiveness of the FSP is assessed considering the computational effort and the convergence to the best (near optimal) solution.