Efficient Risk Estimation via Nested Sequential Simulation
Management Science
Integrating particle swarm optimization with reinforcement learning in noisy problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A Framework for Selecting a Selection Procedure
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimal base-stock policy of the assemble-to-order systems
Artificial Life and Robotics
Ranking and selection with unknown correlation structures
Proceedings of the Winter Simulation Conference
Optimal computing budget allocation for small computing budgets
Proceedings of the Winter Simulation Conference
Combining simulation allocation and optimal splitting for rare-event simulation optimization
Proceedings of the Winter Simulation Conference
Optimizing local pickup and delivery with uncertain loads
Proceedings of the Winter Simulation Conference
Guessing preferences: a new approach to multi-attribute ranking and selection
Proceedings of the Winter Simulation Conference
Information Sciences: an International Journal
Optimal computing budget allocation in particle swarm optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering systems, since closed-form analytical solutions generally do not exist for such problems. However, the added flexibility of simulation often creates models that are computationally intractable. Moreover, to obtain a sound statistical estimate at a specified level of confidence, a large number of simulation runs (or replications) is usually required for each design alternative. If the number of design alternatives is large, the total simulation cost can be very expensive. Stochastic Simulation Optimization addresses the pertinent efficiency issue via smart allocation of computing resource in the simulation experiments for optimization, and aims to provide academic researchers and industrial practitioners with a comprehensive coverage of OCBA approach for stochastic simulation optimization. Starting with an intuitive explanation of computing budget allocation and a discussion of its impact on optimization performance, a series of OCBA approaches developed for various problems are then presented, from the selection of the best design to optimization with multiple objectives. Finally, this book discusses the potential extension of OCBA notion to different applications such as data envelopment analysis, experiments of design and rare-event simulation.