Relevant bibliographies by topics / Monte Carlo simulation Optimization - OvSMC

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Monte Carlo simulation Optimization - OvSMC'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Monte Carlo simulation Optimization - OvSMC"

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Takaya, Keisuke, and Norio Hibiki. "DYNAMIC PORTFOLIO OPTIMIZATION USING MONTE CARLO SIMULATION." Transactions of the Operations Research Society of Japan 55 (2012): 84–109. http://dx.doi.org/10.15807/torsj.55.84.

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Hesselbo, Bobby, and R. B. Stinchcombe. "Monte Carlo Simulation and Global Optimization without Parameters." Physical Review Letters 74, no. 12 (1995): 2151–55. http://dx.doi.org/10.1103/physrevlett.74.2151.

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Conley, William. "Simulation optimization and correlation with multi stage Monte Carlo optimization." International Journal of Systems Science 38, no. 12 (2007): 1013–19. http://dx.doi.org/10.1080/00207720701595104.

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Norman, M. J., and R. Y. Rubinstein. "Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks." Journal of the Operational Research Society 38, no. 9 (1987): 863. http://dx.doi.org/10.2307/2582332.

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Norman, M. J. "Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks." Journal of the Operational Research Society 38, no. 9 (1987): 863. http://dx.doi.org/10.1057/jors.1987.145.

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Angus, john E. "Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks." Technometrics 30, no. 4 (1988): 465–67. http://dx.doi.org/10.1080/00401706.1988.10488460.

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Lynn, Peter, and Reuven Y. Rubinstein. "Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks." Statistician 36, no. 4 (1987): 421. http://dx.doi.org/10.2307/2348850.

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Teghem, J. "Monte Carlo optimization, simulation and sensitivity of queueing networks." European Journal of Operational Research 36, no. 2 (1988): 273–74. http://dx.doi.org/10.1016/0377-2217(88)90441-9.

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Coughlan, L., M. Basil, and P. Cox. "System Uncertainty Modelling Using Monte Carlo Simulation." Measurement and Control 33, no. 3 (2000): 78–81. http://dx.doi.org/10.1177/002029400003300304.

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Baharom, Nuridawati, and Pa’ezah Hamzah. "Inventory Optimization using Simulation Approach." Journal of Computing Research and Innovation 3, no. 2 (2018): 38–47. http://dx.doi.org/10.24191/jcrinn.v3i2.93.

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Inventory creates a significant cost to a firm in the form of the ordering cost, shortage cost, holding cost and also the cost of the goods itself. Managing inventory is always a big challenge for firms in order to balance these operating costs and maintain customer’s service. In this paper, a case study of an electronics manufacturing firm was used to illustrate the use of the Monte Carlo simulation to improve the current inventory system for sensor cable. A simulation model mimicking the current inventory system was developed, and used to study the current system and alternative reorder point policies. Various reorder points were experimented to determine the reorder policy that results in the lowest average total inventory cost per week. The simulation experiments allow the decision maker to make good purchasing decisions in order to avoid ordering excessive raw materials which lead to higher inventory cost to the company. 
 
 Keywords: inventory, optimization, Monte Carlo Simulation
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Dissertations / Theses on the topic "Monte Carlo simulation Optimization - OvSMC"

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Oliveira, José Benedito da Silva. "Combinação de técnicas de delineamento de experimentos e elementos finitos com a otimização via simulação Monte Carlo /." Guaratinguetá, 2019. http://hdl.handle.net/11449/183380.

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Orientador: Aneirson Francisco da Silva<br>Resumo: A Estampagem a Frio é um processo de conformação plástica de chapas metálicas, que possibilita, por meio de ferramentas específicas, obter componentes com boas propriedades mecânicas, geometrias e espessuras variadas, diferentes especificações de materiais e com boa vantagem econômica. A multiplicidade destas variáveis gera a necessidade de utilização de técnicas estatísticas e de simulação numérica, que suportem a sua análise e adequada tomada de decisão na elaboração do projeto das ferramentas de conformação. Este trabalho foi desenvolvido em uma empresa brasileira multinacional de grande porte que atua no setor de autopeças, em seu departamento de engenharia de projetos de ferramentas, com o propósito de reduzir o estiramento e a ocorrência de trincas em uma travessa de 6,8 [mm] de aço LNE 380. A metodologia proposta obtém os valores dos fatores de entrada e sua influência na variável resposta com o uso de técnicas de Delineamento de Experimentos (DOE) e simulação pelo método de Elementos Finitos (FE). Uma Função Empírica é desenvolvida a partir desses dados, com o uso da técnica de regressão, obtendo-se a variável resposta y (espessura na região crítica), em função dos fatores influentes xi do processo. Com a Otimização via Simulação Monte Carlo (OvSMC) insere-se a incerteza nos coeficientes desta Função Empírica, sendo esta a principal contribuição deste trabalho, pois é o que ocorre, por via de regra, na prática com problemas experimentais. Simulando-se por FE as ferram... (Resumo completo, clicar acesso eletrônico abaixo)<br>Mestre
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Bryskhe, Henrik. "Optimization of Monte Carlo simulations." Thesis, Uppsala University, Department of Information Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121843.

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<p>This thesis considers several different techniques for optimizing Monte Carlo simulations. The Monte Carlo system used is Penelope but most of the techniques are applicable to other systems. The two mayor techniques are the usage of the graphics card to do geometry calculations, and raytracing. Using graphics card provides a very efficient way to do fast ray and triangle intersections. Raytracing provides an approximation of Monte Carlo simulation but is much faster to perform. A program was also written in order to have a platform for Monte Carlo simulations where the different techniques were implemented and tested. The program also provides an overview of the simulation setup, were the user can easily verify that everything has been setup correctly. The thesis also covers an attempt to rewrite Penelope from FORTAN to C. The new version is significantly faster and can be used on more systems. A distribution package was also added to the new Penelope version. Since Monte Carlo simulations are easily distributed, running this type of simulations on ten computers yields ten times the speedup. Combining the different techniques in the platform provides an easy to use and at the same time efficient way of performing Monte Carlo simulations.</p>
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Armour, Jessica D. "On the Gap-Tooth direct simulation Monte Carlo method." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/72863.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, February 2012.<br>"February 2012." Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. [73]-74).<br>This thesis develops and evaluates Gap-tooth DSMC (GT-DSMC), a direct Monte Carlo simulation procedure for dilute gases combined with the Gap-tooth method of Gear, Li, and Kevrekidis. The latter was proposed as a means of reducing the computational cost of microscopic (e.g. molecular) simulation methods using simulation particles only in small regions of space (teeth) surrounded by (ideally) large gaps. This scheme requires an algorithm for transporting particles between teeth. Such an algorithm can be readily developed and implemented within direct Monte Carlo simulations of dilute gases due to the non-interacting nature of the particle-simulators. The present work develops and evaluates particle treatment at the boundaries associated with diffuse-wall boundary conditions and investigates the drawbacks associated with GT-DSMC implementations which detract from the theoretically large computational benefit associated with this algorithm (the cost reduction is linear in the gap-to-tooth ratio). Particular attention is paid to the additional numerical error introduced by the gap-tooth algorithm as well as the additional statistical uncertainty introduced by the smaller number of particles. We find the numerical error introduced by transporting particles to adjacent teeth to be considerable. Moreover, we find that due to the reduced number of particles in the simulation domain, correlations persist longer, and thus statistical uncertainties are larger than DSMC for the same number of particles per cell. This considerably reduces the computational benefit associated with the GT-DSMC algorithm. We conclude that the GT-DSMC method requires more development, particularly in the area of error and uncertainty reduction, before it can be used as an effective simulation method.<br>by Jessica D. Armour.<br>S.M.
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Homem, de Mello Tito. "Simulation-based methods for stochastic optimization." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/24846.

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Greberg, Felix. "Debt Portfolio Optimization at the Swedish National Debt Office: : A Monte Carlo Simulation Model." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275679.

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It can be difficult for a sovereign debt manager to see the implications on expected costs and risk of a specific debt management strategy, a simulation model can therefore be a valuable tool. This study investigates how future economic data such as yield curves, foreign exchange rates and CPI can be simulated and how a portfolio optimization model can be used for a sovereign debt office that mainly uses financial derivatives to alter its strategy. The programming language R is used to develop a bespoke software for the Swedish National Debt Office, however, the method that is used can be useful for any debt manager. The model performs well when calculating risk implications of different strategies but debt managers that use this software to find optimal strategies must understand the model's limitations in calculating expected costs. The part of the code that simulates economic data is developed as a separate module and can thus be used for other studies, key parts of the code are available in the appendix of this paper. Foreign currency exposure is the factor that had the largest effect on both expected cost and risk, moreover, the model does not find any cost advantage of issuing inflation-protected debt. The opinions expressed in this thesis are the sole responsibility of the author and should not be interpreted as reflecting the views of the Swedish National Debt Office.<br>Det kan vara svårt för en statsskuldsförvaltare att se påverkan på förväntade kostnader och risk när en skuldförvaltningsstrategi väljs, en simuleringsmodell kan därför vara ett värdefullt verktyg. Den här studien undersöker hur framtida ekonomiska data som räntekurvor, växelkurser ock KPI kan simuleras och hur en portföljoptimeringsmodell kan användas av ett skuldkontor som främst använder finansiella derivat för att ändra sin strategi. Programmeringsspråket R används för att utveckla en specifik mjukvara åt Riksgälden, men metoden som används kan vara användbar för andra skuldförvaltare. Modellen fungerar väl när den beräknar risk i olika portföljer men skuldförvaltare som använder modellen för att hitta optimala strategier måste förstå modellens begränsningar i att beräkna förväntade kostnader. Delen av koden som simulerar ekonomiska data utvecklas som en separat modul och kan därför användas för andra studier, de viktigaste delarna av koden finns som en bilaga till den här rapporten. Valutaexponering är den faktor som hade störst påverkan på både förväntade kostnader och risk och modellen hittar ingen kostnadsfördel med att ge ut inflationsskyddade lån. Åsikterna som uttrycks i den här uppsatsen är författarens egna ansvar och ska inte tolkas som att de reflekterar Riksgäldens syn.
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Bolin, Christopher E. (Christopher Eric). "Iterative uncertainty reduction via Monte Carlo simulation : a streamlined life cycle assessment case study." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82189.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2013.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>"June 2013." Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 97-103).<br>Life cycle assessment (LCA) is one methodology for assessing a product's impact on the environment. LCA has grown in popularity recently as consumers and governments request more information concerning the environmental consequences of goods and services. In many cases, however, carrying out a complete LCA is prohibitively expensive, demanding large investments of time and money to collect and analyze data. This thesis aims to address the complexity of LCA by highlighting important product parameters, thereby guiding data collection. LCA streamlining is the process of reducing the necessary effort to produce acceptable analyses. Many methods of LCA streamlining are unfortunately vague and rely on engineering intuition. While they can be effective, the reduction in effort is often accompanied by a commensurate increase in the uncertainty of the results. One nascent streamlining method aims to reduce uncertainty by generating random simulations of the target product's environmental impact. In these random Monte Carlo simulations the product's attributes are varied, producing a range of impacts. Parameters that contribute significantly to the uncertainty of the overall impact are targeted for resolution. To resolve a parameter, data must be collected to more precisely define its value. This research project performs a streamlined LCA case study in collaboration with a diesel engine manufacturer. A specific engine is selected and a complex model of its production and manufacturing energy use is created. The model, consisting of 184 parameters, is then sampled randomly to determine key parameters for resolution. Parameters are resolved progressively and the resulting decrease in uncertainty is examined. The primary metric for evaluating model uncertainty is False Error Rate (FSR), defined here as the confusion between two engines that differ in energy use by 10%. Initially the FSR is 21%, dropping to 6.1% after 20 parameters are resolved, and stabilizing at 5.8% after 39 parameters are resolved. The case study illustrates that, if properly planned, a streamlined LCA can be performed that achieves desired resolution while vastly reducing the data collection burden.<br>by Christopher E. Bolin.<br>S.M.
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Dugan, Nazim. "Structural Properties Of Homonuclear And Heteronuclear Atomic Clusters: Monte Carlo Simulation Study." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607475/index.pdf.

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In this thesis study, a new method for finding the optimum geometries of atomic nanoparticles has been developed by modifying the well known diffusion Monte Carlo method which is used for electronic structure calculations of quantum mechanical systems. This method has been applied to homonuclear and heteronuclear atomic clusters with the aim of both testing the method and studying various properties of atomic clusters such as radial distribution of atoms and coordination numbers. Obtained results have been compared with the results obtained by other methods such as classical Monte Carlo and molecular dynamics. It has been realized that this new method usually finds local minima when it is applied alone and some techniques to escape from local minima on the potential energy surface have been developed. It has been concluded that these techniques of escaping from local minima are key factors in the global optimization procedure.
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Yao, Min. "Computed radiography system modeling, simulation and optimization." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0128/document.

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Depuis plus d’un siècle, la radiographie sur film est utilisée pour le contrôle non destructif (CND) de pièces industrielles. Avec l’introduction de méthodes numériques dans le domaine médical, la communauté du CND industriel a commencé à considérer également les techniques numériques alternatives au film. La radiographie numérique (en anglais Computed radiography -CR) utilisant les écrans photostimulables (en anglais imaging plate -IP) est une voie intéressante à la fois du point de vue coût et facilité d’implémentation. Le détecteur (IP) utilisé se rapproche du film car il est flexible et réutilisable. L’exposition de l’IP aux rayons X génère une image latente qui est ensuite lue et numérisée grâce à un système de balayage optique par laser. A basse énergie, les performances du système CR sont bonnes ce qui explique son utilisation importante dans le domaine médical. A haute énergie par contre, les performances du système CR se dégradent à la fois à cause de la mauvaise absorption de l’IP mais également de la présence de rayonnement diffusé par la pièce qui, étant d’énergie plus faible, est préférentiellement absorbée par l’IP. Les normes internationales préconisent l’utilisation d’écrans métalliques pour améliorer la réponse des systèmes CR à haute énergie. Néanmoins, la nature et l’épaisseur de ces écrans n’est pas clairement définie et la gamme des configurations possibles est large. La simulation est un outil utile pour prévoir les performances d’une expérience et déterminer les meilleures conditions opératoires. Les méthodes Monte Carlo sont communément admises comme étant les plus précises pour simuler les phénomènes de transport de rayonnement, et ainsi comprendre les phénomènes physiques en jeu. Cependant, le caractère probabiliste de ces méthodes implique des temps de calcul importants, voire prohibitifs pour des géométries complexes. Les méthodes déterministes au contraire, peuvent prendre en compte des géométries complexes avec des temps de calcul raisonnables, mais l’estimation du rayonnement diffusé est plus difficile. Dans ce travail de thèse, nous avons tout d’abord mené une étude de simulation Monte Carlo afin de comprendre le fonctionnement des IP avec écrans métalliques à haute énergie pour le contrôle de pièces de forte épaisseur. Nous avons notamment suivi le trajet des photons X mais également des électrons. Quelques comparaisons expérimentales ont pu être menées à l’ESRF (European Synchrotron Radiation Facility). Puis nous avons proposé une approche de simulation hybride, qui combine l'utilisation de codes déterministe et Monte Carlo pour simuler l'imagerie d'objets de forme complexe. Cette approche prend en compte la dégradation introduite par la diffusion des rayons X et la fluorescence dans l'IP ainsi que la diffusion des photons optiques dans l'IP. Les résultats de différentes configurations de simulation ont été comparés<br>For over a century, film-based radiography has been used as a nondestructive testing technique for industrial inspections. With the advent of digital techniques in the medical domain, the NDT community is also considering alternative digital techniques. Computed Radiography (CR) is a cost-efficient and easy-to-implement replacement technique because it uses equipment very similar to film radiography. This technology uses flexible and reusable imaging plates (IP) as a detector to generate a latent image during x-ray exposure. With an optical scanning system, the latent image can be readout and digitized resulting in a direct digital image. CR is widely used in the medical field since it provides good performance at low energies. For industrial inspection, CR application is limited by its poor response to high energy radiation and the presence of scattering phenomena. To completely replace film radiography by such a system, its performance still needs to be improved by either finding more appropriate IPs or by optimizing operating conditions. Guidelines have been addressed in international standards to ensure a good image quality supplied by CR system, where metallic screens are recommended for the case of using high energy sources. However, the type and thickness of such a screen are not clearly defined and a large panel of possible configurations does exist. Simulation is a very useful tool to predict experimental outcomes and determine the optimal operating conditions. The Monte Carlo (MC) methods are widely accepted as the most accurate method to simulate radiation transport problems. It can give insight about physical phenomena, but due to its random nature, a large amount of computational time is required, especially for simulations involving complex geometries. Deterministic methods, on the other hand, can handle easily complex geometry, and are quite efficient. However, the estimation of scattering effects is more difficult with deterministic methods. In this thesis work, we have started with a Monte Carlo simulation study in order to investigate the physical phenomena involved in IP and in metallic screens at high energies. In particular we have studied separately the behavior of X-ray photons and electrons. Some experimental comparisons have been carried out at the European Synchrotron Radiation Facility. Then, we have proposed a hybrid simulation approach, combining the use of deterministic and Monte Carlo code, for simulating the imaging of complex shapes objects. This approach takes into account degradation introduced by X-ray scattering and fluorescence inside IP, as well as optical photons scattering during readout process. Different simulation configurations have been compared
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Kim, Yoon Hyung. "Three Essays on Application of Optimization Modeling and Monte Carlo Simulation to Consumer Demand and Carbon Sequestration." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275275175.

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Bergman, Alanah Mary. "Monte Carlo simulation of x-ray dose distributions for direct aperture optimization of intensity modulated treatment fields." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/30720.

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This thesis investigates methods of reducing radiation dose calculation errors as applied to a specialized x-ray therapy called intensity modulated radiation therapy (IMRT). There are three major areas of investigation. First, limits of the popular 2D pencil beam kernel (PBK) dose calculation algorithm are explored. The ability to resolve high dose gradients is partly related to the shape of the PBK. Improvements to the spatial resolution can be achieved by modifying the dose kernel shapes already present in the clinical treatment planning system. Optimization of the PBK shape based on measured-to-calculated test pattern dose comparisons reduces the impact of some limitations of this algorithm. However, other limitations remain (e.g. assuming spatial invariance, no modeling of extra-focal radiation, and no modeling of lateral electron transport). These limitations directed this thesis towards the second major investigation - Monte Carlo (MC) simulation for IMRT. MC is considered to be the "gold standard" for radiation dose calculation accuracy. This investigation incorporates MC calculated beamlets of dose deposition into a direct aperture optimization (DAO) algorithm for IMRT inverse planning (MC-DAO) . The goal is to show that accurate tissue inhomogeneity information and lateral electronic transport information, combined with DAO, will improve the quality/accuracy of the patient treatment plan. MC simulation generates accurate beamlet dose distributions in traditionally difficultto- calculate regions (e.g. air-tissue interfaces or small (≤ 5 cm² ) x-ray fields). Combining DAO with MC beamlets reduces the required number of radiation units delivered by the linear accelerator by ~30-50%. The MC method is criticized for having long simulation times (hours). This can be addressed with distributed computing methods and data filtering ('denoising'). The third major investigation describes a practical implementation of the 3D Savitzky-Golay digital filter for MC dose 'denoising'. This thesis concludes that MC-based DAO for IMRT inverse planning is clinically feasible and offers accurate modeling of particle transport and dose deposition in difficult environments where lateral electronic dis-equilibrium exists.<br>Science, Faculty of<br>Physics and Astronomy, Department of<br>Graduate
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Books on the topic "Monte Carlo simulation Optimization - OvSMC"

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Monte Carlo optimization, simulation, and sensitivity of queueing networks. Wiley, 1986.

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Rubinstein, Reuven Y. Monte Carlo optimization, simulation, and sensitivity of queuing networks. Krieger Pub. Co., 1992.

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Modeling risk: Applying Monte Carlo simulation, real options analysis, forecasting, and optimization techniques. John Wiley & Sons, 2006.

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Mun, Johnathan. Modeling risk: Applying Monte Carlo simulation, real options analysis, forecasting, and optimization techniques. 2nd ed. Wiley, 2010.

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Rubinstein, Reuven Y. The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. Springer New York, 2004.

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Mun, Johnathan. Modeling Risk: Applying Monte Carlo Simulation, Real Options Analysis, Forecasting, and Optimization Techniques. Wiley & Sons, Incorporated, John, 2006.

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Dabruck, Jan Philipp. Target Station Optimization for the High-Brilliance Neutron Source HBS: Simulation Studies Based on the Monte Carlo Method. Springer, 2018.

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Kroese, Dirk P., and Reuven Y. Rubinstein. The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning (Information Science and Statistics). Springer, 2004.

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Book chapters on the topic "Monte Carlo simulation Optimization - OvSMC"

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Cvitanić, Jaksa, Levon Goukasian, and Fernando Zapatero. "Hedging with Monte Carlo Simulation." In Applied Optimization. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3613-7_18.

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Kreinin, Alexander, and Alexander Levin. "Robust Monte Carlo Simulation for Approximate Covariance Matrices and VaR Analyses." In Nonconvex Optimization and Its Applications. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3150-7_8.

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Myasnichenko, Vladimir, Nickolay Sdobnyakov, Leoneed Kirilov, Rossen Mikhov, and Stefka Fidanova. "Structural Instability of Gold and Bimetallic Nanowires Using Monte Carlo Simulation." In Recent Advances in Computational Optimization. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22723-4_9.

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Rosetti, M., M. Benassi, V. Bruzzaniti, A. Bufacchi, and M. D’Andrea. "Intra-Operative Radiation Therapy Optimization Using the Monte Carlo Method." In Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-18211-2_72.

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Wang, Longyan, and Andy C. C. Tan. "Robust Wind Farm Layout Optimization Under Weibull Distribution by Monte Carlo Simulation." In Engineering Assets and Public Infrastructures in the Age of Digitalization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48021-9_100.

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Neubert, W., W. Enghardt, U. Lehnert, et al. "Optimization of a Tunable Quasi-Monochromatic X-ray Source for Cell Irradiations." In Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-18211-2_21.

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Ullman, Gustaf, Michael Sandborg, David R. Dance, Martin Yaffe, Mia Skarpathiotakis, and Gudrun Alm Carlsson. "Optimization of Digital Subtraction Mammography Using Monte Carlo Simulation of the Imaging Chain." In Digital Mammography. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-59327-7_36.

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Mitake, S., O. Sato, and H. Tsunoda. "Application of Biasing Optimization Techniques to Monte Carlo Shielding Analysis of a Transport Cask." In Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-18211-2_137.

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Dian-hui, Zhang, Huang Liu-xing, and Niu Sheng-li. "Shield Optimization for X-rays Using the Monte Carlo Method Combined with Analytical Calculation." In Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-18211-2_77.

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Marseguerra, Marzio, Enrico Zio, and Luca Podofillini. "Genetic Algorithms and Monte Carlo Simulation for the Optimization of System Design and Operation." In Computational Intelligence in Reliability Engineering. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37368-1_4.

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Conference papers on the topic "Monte Carlo simulation Optimization - OvSMC"

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Schuyler, John R. "Bid Optimization With Monte Carlo Simulation." In SPE Hydrocarbon Economics and Evaluation Symposium. Society of Petroleum Engineers, 2010. http://dx.doi.org/10.2118/130141-ms.

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Drew, Shane, and Tito Homem-de-Mello. "Quas-Monte Carlo Strategies for Stochastic Optimization." In 2006 Winter Simulation Conference. IEEE, 2006. http://dx.doi.org/10.1109/wsc.2006.323158.

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Hande, Sayaji, Prasoon Patidar, Sachin Meena, and Saurabh Banerjee. "Network flow Optimization through Monte Carlo Simulation." In 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2018. http://dx.doi.org/10.1109/pdgc.2018.8745965.

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Tan, Hui. "Adaptive Monte Carlo sampling gradient method for optimization." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8248222.

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Chen, Xi, and Enlu Zhou. "Population model-based optimization with sequential Monte Carlo." In 2013 Winter Simulation Conference - (WSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/wsc.2013.6721490.

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Martin, Jay, and Timothy Simpson. "A Monte Carlo Simulation of the Kriging Model." In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4483.

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Wang, Haikun, Zhean Gong, Hong-Zhong Huang, Xiaoling Zhang, and Zhiqiang Lv. "System Reliability Based Design Optimization with Monte Carlo simulation." In 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE). IEEE, 2012. http://dx.doi.org/10.1109/icqr2mse.2012.6246423.

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Fu, Michael C. "AlphaGo and Monte Carlo tree search: The simulation optimization perspective." In 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822130.

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Kenzap, Serguei A., and Vassilios N. Kazakidis. "Optimization of Operating Risk in Mining System through Monte-Carlo Simulation." In Applied Simulation and Modelling. ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.715-004.

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Bechard, Vincent. "Robust Data-Driven Optimization Using Machine Learning and Monte-Carlo Simulation." In 2019 Winter Simulation Conference (WSC). IEEE, 2019. http://dx.doi.org/10.1109/wsc40007.2019.9004745.

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Reports on the topic "Monte Carlo simulation Optimization - OvSMC"

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Martinez, Michael A. A Computerized Approach to a Multivariable, Constrained, Nonlinear Optimization Blending Problem Using a Monte Carlo Simulation,. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada329078.

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Mun, Johnathan, and Thomas Housal. A Primer on Applying Monte Carlo Simulation, Real Options Analysis, Knowledge Value Added, Forecasting, and Portfolio Optimization. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada518628.

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