Alexander Shapiro · 1996
In this talk we consider a problem of optimizing an expected value function by Monte Carlo simulation methods. We discuss, somewhat in details, the stochastic counterpart (sample path) method where a relatively large sample is generated and the expected value function is approximated by the corresponding average function. Consequently the obtained approximation problem is solved by deterministic methods of nonlinear programming. One of advantages of this approach, compared with the classical stochastic approximation method, is that a statistical inference can be incorporated into optimization algorithms. This allows to develop a validation analysis, stopping rules and variance reduction techniques which in some cases considerably enhance numerical performance of the stochastic counterpart method.
Cited by 22Open access
A.M. Law, M.G. McComas · 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165) · 2002
We present an introduction to simulation-based optimization, which is, perhaps, the "hottest" topic in discrete-event simulation today. We give a precise statement of the problem being addressed and also experimental results for two commercial optimization packages applied to a manufacturing example with seven decision variables.
Cited by 38
L. Jeff Hong, Xiaowei Zhang · arXiv (Cornell University) · 2021
Simulation models are widely used in practice to facilitate decision-making in a complex, dynamic and stochastic environment. But they are computationally expensive to execute and optimize, due to lack of analytical tractability. Simulation optimization is concerned with developing efficient sampling schemes -- subject to a computational budget -- to solve such optimization problems. To mitigate the computational burden, surrogates are often constructed using simulation outputs to approximate the response surface of the simulation model. In this tutorial, we provide an up-to-date overview of surrogate-based methods for simulation optimization with continuous decision variables. Typical surrogates, including linear basis function models and Gaussian processes, are introduced. Surrogates can be used either as a local approximation or a global approximation. Depending on the choice, one may develop algorithms that converge to either a local optimum or a global optimum. Representative examples are presented for each category. Recent advances in large-scale computation for Gaussian processes are also discussed.
Cited by 2Open access
Sujin Kim · 2006
We present a review of methods for simulation optimization. In particular, we focus on gradient-based techniques for continuous optimization. We demonstrate the main concepts using as an example the multidimensional newsvendor problem. We also discuss mathematical techniques and results that are useful in verifying and analyzing the simulation optimization procedures
Cited by 28
Thomas Bartz–Beielstein, Bogdan Filipič, Peter Korošec et al. · Studies in computational intelligence · 2019
Cited by 46
Yuh-Chuyn Luo, Chun‐Hung Chen, E. Yucesan et al. · 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165) · 2002
Web technology is having a significant impact on computer simulation. Most of the effort in Web based simulation is aimed at modeling, particularly at building simulation languages and at creating model libraries that can be assembled and executed over the Web. We focus on the efficiency of simulation experimentation for optimization. We introduce a framework for combining the statistical efficiency of simulation optimization techniques with the effectiveness of parallel execution algorithms. In particular, the Optimal Computing Budget Allocation (OCBA) algorithm is implemented in a Web based environment for low-cost parallel and distributed simulation experimentation. A prototype implementation with some experimental results is presented.
Cited by 4
Michael Wetter, Prof Alice, M. Agogino et al. · 2004
Cited by 0
Bartz-Beielstein, Thomas, Filipič, Bogdan, Korosec, Peter et al. · 2019
Cited by 0
Raphael Strickling, Faizan Habib Vance, Arne Scholtissek · Figshare · 2026
Schematic overview of a simulation-based shape optimization toolchain utilizing surrogate-based optimization. The toolchain is divided into two parts: the optimization framework (left) and the simulation framework (right). In this case, the toolchain is applied to optimize the internal geometry of a hydrogen burner nozzle, where large eddy simulations are employed to resolve the internal mixture formation and evaluate the generated nozzle designs based on the resulting mixture field at the nozzle outlet.
Cited by 0Open access
João Victor Soares do Amaral, Rafael de Carvalho Miranda, José Arnaldo Barra Montevechi et al. · SSRN Electronic Journal · 2023
Cited by 0Open access
Julien Gacon, Christa Zoufal, Stefan Woerner · arXiv (Cornell University) · 2020
In this paper, we introduce a quantum-enhanced algorithm for simulation-based optimization. Simulation-based optimization seeks to optimize an objective function that is computationally expensive to evaluate analytically, and thus, is approximated using simulation techniques such as Monte Carlo simulation. Quantum Amplitude Estimation (QAE) can achieve a quadratic speed-up over classical Monte Carlo simulation. Hence, in many cases, it can achieve a speed-up for simulation-based optimization as well. Combining QAE with ideas from quantum combinatorial optimization, we show how this can be used not only for continuous but also for discrete optimization problems. Furthermore, the algorithm is demonstrated on illustrative problems such as portfolio optimization with a Value at Risk constraint and inventory management.
Cited by 14Open access
Abhijit Gosavi · Operations research, computer science. Interface series · 2014
Cited by 14
Raphael Strickling, Faizan Habib Vance, Arne Scholtissek · Figshare · 2026
Schematic overview of a simulation-based shape optimization toolchain utilizing surrogate-based optimization. The toolchain is divided into two parts: the optimization framework (left) and the simulation framework (right). In this case, the toolchain is applied to optimize the internal geometry of a hydrogen burner nozzle, where large eddy simulations are employed to resolve the internal mixture formation and evaluate the generated nozzle designs based on the resulting mixture field at the nozzle outlet.
Cited by 0Open access
Rina Mary Mazza · 4OR · 2011
Cited by 3
I. V. Revina, Е. Н. Трифонова · Journal of Physics Conference Series · 2021
Abstract The article presents the results of optimizing the service center operation. The IDEF3 notation was used to analyze the logical sequence of processes performed in the car center divisions. The developed discrete-event simulation model “AS-IS” made it possible to identify “bottlenecks” in the work of the car center. The involvement of experts and the use of the method of apriori ranking of factors allowed determining the impact of a number of factors on the work of the service center and the directions for improving the work. Changes in organizational, material and technical factors and the development of “TO-BE” simulation model allowed determining the optimal parameters for the service center operation. As a result of the business process reengineering, the following changes were made: staff schedule and assignment of employees to work; the number of service posts; an electronic queue and document management system. These changes allowed increasing the profit of the service center by 1.9 times.
Cited by 4Open access
Rui Jorge Rei, Mikio Kubo, João Pedro Pedroso · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2008
In many sectors of industry, manufacturers possess warehouses where finished goods are stored, awaiting to fulfill a client order. We present a situation where these items are characterized by release and due dates, i.e. warehouse arrival for storage and client delivery, respectively. The warehouse has a number of positions available, where items can be placed on top of each other, forming stacks. For item manipulation, there is a single a stacking crane, able to carry one item at a time. When in a given stack an item at the top is due at a date later than some item below it, it must be relocated to another stack, so that the item below can be delivered. In this problem the objective is to minimize the number of movements made by the crane.
Cited by 0
E. Yücesan, C. -h. Chen, J. L. Snowdon et al. · 2008
The sea scallop resource of Georges Bank supports one of the largest commercial fisheries in the United States. The objective of this research was to develop a technique to examine different management strategies for the sea scallop resource of Georges Bank and compare these strategies to the optimal. A simulation model followed the sea scallop population dynamics using information from recent photographic surveys and studies on spatial and temporal life history parameters, such as growth, natural mortality, spawning, and fishing activities. Stochastic simulation technique was used to describe the influence of the highly variable marine environment. Genetic Algorithm technique was used to develop harvest strategy in the area for optimal utilization by maximizing long term fishing yield. Simulation
Cited by 0
Yunpeng Bai, Shengdong Zhao, Antti Oulasvirta · arXiv (Cornell University) · 2026
Augmented reading systems aim to adapt text presentation to improve comprehension and task performance, yet existing approaches rely heavily on heuristics, opaque data-driven models, or repeated human involvement in the design loop. We propose framing augmented reading as a simulation-based optimization problem grounded in resource-rational models of human reading. These models instantiate a simulated reader that allocates limited cognitive resources, such as attention, memory, and time under task demands, enabling systematic evaluation of text user interfaces. We introduce two complementary optimization pipelines: an offline approach that explores design alternatives using simulated readers, and an online approach that personalizes reading interfaces in real time using ongoing interaction data. Together, this perspective enables adaptive, explainable, and scalable augmented reading design without relying solely on human testing.
Cited by 0Open access
Maximilian Bügler, André Borrmann · Civil and Environmental Engineering · 2016
Construction projects require a multitude of procedures and resources for the high diversity of building concepts. Most of such projects are unique in their design and need individual schedule planning to be realized. In order to develop the required schedules, several complex decisions need to be made and several different factors need to be taken into account, including cost, make span, safety, resource sparsity, delivery schedules and geometric constraints. The problem of scheduling the involved processes in an optimal way is called the resource constrained project scheduling problem (RCPSP) and several solution algorithms are available. In addition, simulation based techniques can be used to address more complex constraints and objectives. This chapter presents an overview of traditional optimization procedures for the RCPSP and bridge the gap to simulation based techniques, which are described in detail.
Cited by 0Open access
Rui Jorge Rei, Mikio Kubo, João Pedro Pedroso · Communications in computer and information science · 2008
Cited by 13