Input–output uncertainty comparisons for discrete optimization via simulation

When input distributions to a simulation model are estimated from real-world data, they naturally have estimation error causing input uncertainty in the simulation output. If an optimization via simulation (OvS) method is applied that treats the input distributions as “correct,” then there is a risk of making a suboptimal decision for the real world, which we call input model risk. This paper addresses a discrete OvS (DOvS) problem of selecting the realworld optimal from among a finite number of systems when all of them share the same input distributions estimated from common input data. Because input uncertainty cannot be reduced without collecting additional real-world data—which may be expensive or impossible—a DOvS procedure should reflect the limited resolution provided by the simulation model in distinguishing the real-world optimal solution from the others. In light of this, our input–output uncertainty comparisons (IOU-C) procedure focuses on comparisons rather than selection: it provides simultaneous confidence intervals for the difference between each system’s real-world mean and the best mean of the rest with any desired probability, while accounting for both stochastic and input uncertainty. To make the resolution as high as possible (intervals as short as possible) we exploit the common input data effect to reduce uncertainty in the estimated differences. Under mild conditions we prove that the IOU-C procedure provides the desired statistical guarantee asymptotically as the real-world sample size and simulation effort increase, but it is designed to be effective in finite samples

Optimization under uncertainty with application to data clustering

A new optimization technique with uncertainty that extends the pure nested partition (NP) algorithm is presented in this thesis. This method is called the nested partition with inheritance. The basic idea of a NP algorithm is very simple. At each iteration, the most promising region is partitioned and the performance of the partitioned region is evaluated using sampling. Based on the performance evaluation, the most promising region is chosen for the next iteration. These procedures are repeated until it satisfies the termination condition.;Even though the pure NP method guarantees the convergence to the optimal solution, it has several shortcomings. To handle these shortcomings, two extensions to the pure NP are suggested. To rigorously determine the required sample effort, some statistical selection methods are implemented, which include the Nelson Matejcik procedure, the Rinott procedure, and the Dudewicz and Dalal procedure, as well as a subset procedure. In addition, Genetic Algorithms (GAs) are used to speed convergence and to overcome the difficulty in the backtracking stage of the NP algorithm.;As an application of the new methodology, this work also suggests the methods to be applied to a data clustering problem. This is a very hard problem with two of the main difficulties being lack of scalability with respect to amount of data and problems with high dimensionality. The new algorithms are found to be effective for solving this problem. Random sampling enhances scalability and the iterative partitioning addresses the dimensionality

Tabu search with fully sequential procedure for simulation optimization

Cataloged from PDF version of article.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.Çevik, SavaşM.S