Journal ArticleDOI
A New Algorithm for Stochastic Discrete Resource AllocationOptimization
TLDR
A new algorithm designed specifically to tackle stochastic discrete resource allocation problems is proposed, which combines with the Nested Partitions method, the Ordinal Optimization techniques, and an efficient simulation control technique.Abstract:
Stochastic discrete resource allocation problems are difficult to solve. In this paper, we propose a new algorithm designed specifically to tackle them. The algorithm combines with the Nested Partitions method, the Ordinal Optimization techniques, and an efficient simulation control technique. The resulting hybrid algorithm retains the global perspective of the Nested Partitions method and the fast convergence properties of the Ordinal Optimization. Numerical results demonstrate that the hybrid algorithm can be effectively used for many large-scale stochastic discrete optimization problems.read more
Citations
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Journal ArticleDOI
Simulation optimization: a comprehensive review on theory and applications
Eylem Tekin,Ihsan Sabuncuoglu +1 more
TL;DR: This paper presents a comprehensive survey on techniques for simulation optimization with emphasis given on recent developments, and classify the existing techniques according to problem characteristics such as shape of the response surface, objective functions, and parameter spaces.
Journal ArticleDOI
Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data
TL;DR: How simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems are discussed.
Journal ArticleDOI
A combined procedure for optimization via simulation
TL;DR: In this paper, an optimization-via-simulation algorithm for stochastic, discrete-event simulation is proposed for estimating performance measure via a stochastically, discrete event simulation, and the decision variables may be subject to deterministic linear integer constraints.
Journal ArticleDOI
Partially Observable Markov Decision Process Approximations for Adaptive Sensing
TL;DR: This work describes an approach to adaptive sensing based on approximately solving a partially observable Markov decision process (POMDP) formulation of the problem, and describes a variety of approximation methods.
Proceedings ArticleDOI
Some topics for simulation optimization
TL;DR: A tutorial introduction to simulation optimization is given by classifying the problem setting according to the decision variables and constraints, putting the setting in the simulation context, and then summarize the main approaches to Simulation optimization.
References
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Book
Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons
TL;DR: The Rationale of Selection, Screening and Multiple Comparisons for Normal Response Experiments is discussed in this article, where the Indifference Zone approach is used to select the best treatment in a single-factor Normal Response Experiment.
Journal ArticleDOI
Ordinal Optimization of DEDS
TL;DR: It is argued that cardinal rather than cardinal optimization, i.e., concentrating on finding good, better, or best designs rather than on estimating accurately the performance value of these designs, offers a new, efficient, and complementary approach to the performance optimization of systems.
Journal ArticleDOI
Nested Partitions Method for Global Optimization
Leyuan Shi,Sigurdur Olafsson +1 more
TL;DR: The Nested Partitions (NP) method, a new randomized method for solving global optimization problems that systematically partitions the feasible region and concentrates the search in regions that are the most promising, is proposed.