Browsing by Subject "Sampling (Statistics)."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Open Access Analysis of variance reduction techniques in various systems(Bilkent University, 2003) Çelik, SabriIn this thesis, we consider four different Variance Reduction Techniques (VRTs): Antithetic Variates (AV), Latin Hypercube Sampling (LHS), Control Variates (CV), and Poststratified Sampling (PS). These methods individually or in combination are applied to the steady state simulation of three well-studied systems. These systems are M/M/1 Queuing System, a Serial Line Production System, and an (s,S) Inventory Policy. Our results indicate that there is no guarantee of a reduction in variance or an improvement in precision in estimates. The performance of VRTs totally depends on the system characteristics. Nevertheless, CV performs better than PS, AV and LHS on the average. Therefore, instead of altering the input part of the simulation, extracting more information by CV should be more effective. However, if any extra information about the system is not available, AV or LHS can be favored since they do not require additional knowledge about the system. Furthermore, since the analysis of output data through CV or PS requires a negligible time compared to the simulation run time, applying CV and PS at all possible cases and then selecting the best one can be the best strategy in the variance reduction. The use of the combination of methods provides more improvement on the average.Item Open Access A hybrid model for designing attributes sampling plans(Bilkent University, 1994) Sanin, M. BaşarIn single sampling plans by attributes, statistical and economical considerations have traditionally been discussed separately. An approach taking into account both considerations simultaneously would be more useful in terms o f quality assurance. The suggested model involves minimization o f the expected total cost comprising the inspection cost, the annoyance cost o f rejecting a lot and the cost o f outgoing defective items, while the producer's risk and consumer's risk are not allowed to be greater than specified values. To find the optimal sample size and the optimal acceptance number, a two stage solution method is proposed. The accuracy and the efficiency o f the solution procedure are tested on randomly generated problems, by comparing the solutions obtained by the proposed method to those obtained by enumeration. Sensitivity o f the model is discussed by analyzing the effects o f parameters on the optimal sampling plans.