Relevant bibliographies by topics / Monte Carlo simulation Optimization

Contents

  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'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2025

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Journal articles on the topic "Monte Carlo simulation Optimization"

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Li, Ankang. "Portfolio Optimization by Monte Carlo Simulation." Advances in Economics, Management and Political Sciences 50, no. 1 (2023): 133–38. http://dx.doi.org/10.54254/2754-1169/50/20230568.

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In this paper, Monte Carlo simulation is used for constructing Efficient Frontier and optimizing the portfolio. Then the performance of the optimized portfolio had been evaluated and compared to the performance of the whole market, Firstly, this study collected the closing prices of five stocks in different industries that was listed in New York stock exchange between 2023/01/01 and 2023/04/12. Secondly, to testify if the construction of the portfolio can possibly mitigate the volatility, the correlation coefficient between these chosen stocks has been calculated. Then, Monte Carlo simulation
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Mo, Zihan, Boxu He, and Tian Qin. "Option Pricing Based on Several Monte Carlo Techniques." Theoretical and Natural Science 107, no. 1 (2025): 227–34. https://doi.org/10.54254/2753-8818/2025.22650.

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The Monte Carlo method is broadly used in financial technology and engineering for pricing complex derivatives and managing risk due to its flexibility and adaptability. However, Monte Carlo simulation may suffer from high variance problems, impacting accuracy and effectiveness. Control and antithetic variates are two main variance-reduction techniques to optimize the simulation. This paper compares the performance of normal Monte Carlo, and Monte Carlo optimized with control variates or antithetic variates in four different European options. In the work, the Monte Carlo optimization based on
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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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Norozpour, Sajedeh. "Mathematical Optimization of Monte Carlo Simulation Parameters for Predicting Stock Prices." International Journal of Engineering Technologies IJET 9, no. 3 (2025): 84–88. https://doi.org/10.19072/ijet.1569085.

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Stock price prediction holds paramount significance for individual investors, guiding crucial decisions in financial planning and investment strategies. This research delves into the methodology of Monte Carlo simulation, a versatile tool in financial modeling, to assess its advantages and disadvantages in the context of predicting stock prices. The study employs Python code to demonstrate the step-by-step implementation of Monte Carlo simulations, emphasizing the mathematical optimization of parameters for enhanced accuracy. Results showcase a characteristic bell curve, offering a probabilist
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Heuer, Hans-Otto. "Optimization of Monte Carlo simulations." Physica A: Statistical Mechanics and its Applications 182, no. 4 (1992): 649–71. http://dx.doi.org/10.1016/0378-4371(92)90029-p.

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Wu, Zhaoyang, Bowen Bai, and Lin Liu. "Optimization study of production decision based on Monte Carlo simulation and particle swarm optimization algorithm." Highlights in Business, Economics and Management 53 (March 17, 2025): 132–39. https://doi.org/10.54097/dgw4ce43.

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In this paper, a solution based on Monte Carlo simulation and particle swarm optimization algorithm is proposed for the problems of spare parts monitoring and production process optimization in the production process of enterprises. A Monte Carlo simulation-based sampling and testing method is designed for spare parts incoming inspection decision, which evaluates the inspection accuracy and cost under different sample sizes by simulating a large number of random samples through a large number of random simulations. Thus, the optimal sampling scheme is determined. For the multi-stage decision o
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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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Sugiharti, Endang, Mustafid, R. Rizal Isnanto, Budi Warsito, and Adi Wibowo. "Quasi Monte Carlo for Periodic Review in Inventory Systems." E3S Web of Conferences 448 (2023): 02033. http://dx.doi.org/10.1051/e3sconf/202344802033.

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Periodic Review as a method is widely used especially in inventory system. In this paper Quasi Monte Carlo is used for simulating Periodic Review. The problem: How to implement Quasi Monte Carlo simulation in Periodic Review for inventory system of MSMEs in order to achieve the expected minimum inventory cost? The solution offered: Implementation of Periodic Review with Quasi Monte Carlo in inventory system in MSMEs in order to achieve the expected minimum inventory cost. The method used in this article is a Literature Study on the use of Periodic Review optimization with Quasi Monte Carlo whi
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Chyba, B., M. Mantler, and M. Reiter. "Monte Carlo simulation of projections in computed tomography." Powder Diffraction 23, no. 2 (2008): 150–53. http://dx.doi.org/10.1154/1.2919045.

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Results from Monte Carlo simulations of two-dimensional projections for a simple real sample (an aluminium cube with a cylindrical hole filled by air or steel) in a realistic experimental environment are presented. A meaningful comparison with measurements was therefore possible. Coherent and incoherent scattering as well as excitation of fluorescent radiation are accounted for; multiple sequences of these interactions are followed up to a selectable order. Such simulations are important aids to modern metrological applications of computed tomography where the dimensional accuracy of hidden or
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Dissertations / Theses on the topic "Monte Carlo simulation Optimization"

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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
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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 (teet
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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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Wang, Yunsong. "Optimization of Monte Carlo Neutron Transport Simulations with Emerging Architectures." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX090/document.

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L’accès aux données de base, que sont les sections efficaces, constitue le principal goulot d’étranglement aux performances dans la résolution des équations du transport neutronique par méthode Monte Carlo (MC). Ces sections efficaces caractérisent les probabilités de collisions des neutrons avec les nucléides qui composent le matériau traversé. Elles sont propres à chaque nucléide et dépendent de l’énergie du neutron incident et de la température du matériau. Les codes de référence en MC chargent ces données en mémoire à l’ensemble des températures intervenant dans le système et utilisent un
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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 environm
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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 usef
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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 mo
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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 ré
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Giani, Monardo. "A cost-based optimization of a fiberboard pressing plant using Monte-Carlo simulation (a reliability program)." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/30417/1/Monardo_Giani_Thesis.pdf.

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In this research the reliability and availability of fiberboard pressing plant is assessed and a cost-based optimization of the system using the Monte- Carlo simulation method is performed. The woodchip and pulp or engineered wood industry in Australia and around the world is a lucrative industry. One such industry is hardboard. The pressing system is the main system, as it converts the wet pulp to fiberboard. The assessment identified the pressing system has the highest downtime throughout the plant plus it represents the bottleneck in the process. A survey in the late nineties revealed there
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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 m
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Books on the topic "Monte Carlo simulation Optimization"

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

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Franklin, Mendivil, ed. Explorations in Monte Carlo methods. Springer, 2009.

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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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Mooney, Christopher. Monte Carlo Simulation. SAGE Publications, Inc., 1997. http://dx.doi.org/10.4135/9781412985116.

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Zhu, Zhen, and Hari Rajagopalan. Monte Carlo Simulation. SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071908969.

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I, Schueller G., ed. Monte Carlo simulation. A.A. Balkema, 2001.

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Hess, Karl, ed. Monte Carlo Device Simulation. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7.

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Thomopoulos, Nick T. Essentials of Monte Carlo Simulation. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6022-0.

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Brandimarte, Paolo. Handbook in Monte Carlo Simulation. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118593264.

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Book chapters on the topic "Monte Carlo simulation Optimization"

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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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Loucks, Daniel P. "Chance Constrained and Monte Carlo Modeling." In International Series in Operations Research & Management Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93986-1_14.

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AbstractConstraints of models that contain random variables may be applicable only some of the time. Constraints that apply only a specified fraction of the time are called chance constraints. This chapter illustrates how chance constraints can be included in optimization models. In addition, the chapter demonstrates how to generate values of random variables fitting user defined probability distributions. These random variable values often serve as inputs to stochastic simulation models.
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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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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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Das, Ashok, and Jitendra Kumar. "Mathematical Modeling of Different Breakage PBE Kernels Using Monte Carlo Simulation Results." In Optimization of Pharmaceutical Processes. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90924-6_4.

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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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Rahmani, Shima, Asad Saghari, and Masoud Ebrahimi. "A Shifted-Constraint RBDO Framework Using Monte Carlo Simulations." In Advances in Structural and Multidisciplinary Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67988-4_33.

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Mukherjee, Ayan, Ashish Kumar Singh, Pradeep Kumar Mallick, and Sasmita Rani Samanta. "Portfolio Optimization for US-Based Equity Instruments Using Monte-Carlo Simulation." In Cognitive Informatics and Soft Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8763-1_57.

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Wu, Ruize. "Predictive models and Monte Carlo simulations in portfolio optimization." In Exploring the Financial Landscape in the Digital Age. CRC Press, 2024. http://dx.doi.org/10.1201/9781003508816-65.

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Grelier, Cyril, Olivier Goudet, and Jin-Kao Hao. "Monte Carlo Tree Search with Adaptive Simulation: A Case Study on Weighted Vertex Coloring." In Evolutionary Computation in Combinatorial Optimization. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30035-6_7.

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Conference papers on the topic "Monte Carlo simulation Optimization"

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Wang, Zhiyuan, Yuhao Jiang, Haitao Ye, et al. "Production Process Optimization Decision Based on Monte Carlo Simulation and Dynamic Programming." In 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC). IEEE, 2024. https://doi.org/10.1109/icftic64248.2024.10913383.

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Sun, Yinuo, Wei Xiong, Haoran Pang, Hanyao Cao, and Xinghan Ba. "Research on Planting Optimization Based on Greedy Algorithm and Monte Carlo Simulation." In 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE). IEEE, 2024. https://doi.org/10.1109/iceace63551.2024.10898461.

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Zhang, Wanhua, Jie Chen, and Xin Jiang. "Optimization of Crop Planting Based on Genetic Algorithm and Monte Carlo Simulation." In 2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE). IEEE, 2025. https://doi.org/10.1109/eespe63401.2025.10987152.

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Zhang, Bojiang, Zixuan Guo, Xinyang Yu, Xing Che, and Jiahao Xue. "An Investigation of Op-Amp Circuit Optimization Based on Monte Carlo Simulation and PVT Simulation." In 2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2024. https://doi.org/10.1109/auteee62881.2024.10869769.

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Xie, Lipeng, Hao Wang, and Kaiwen Li. "Optimization of Agricultural Planting Resource Allocation Based on Particle Swarm Optimization and Monte Carlo Simulation." In 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE). IEEE, 2024. https://doi.org/10.1109/iceace63551.2024.10898608.

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Glombik, Sebastian, and Felix Fromme. "REAL OPTIONS ANALYSIS APPLIED ON RESIDENTIAL ENERGY SYSTEMS USING LEAST SQUARES MONTE CARLO SIMULATION." In 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2024). ECOS 2024, 2024. http://dx.doi.org/10.52202/077185-0032.

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Zhang, Yuting, Chaoyun Gu, Heng Li, Zhe Wang, Hongyi Zhang, and Dongyue Qu. "Research on Hole and Shaft Selection Assembly Optimization Based on JSD and Monte Carlo Simulation." In 2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE). IEEE, 2025. https://doi.org/10.1109/icmre64970.2025.10976275.

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Lan, Lihua, Hanping Huang, Fupei Ning, Yanbin Zheng, and Qin Yang. "Research on Dual-Objective Robust Optimization Based on Monte Carlo Simulation and Simulated Annealing Algorithm." In 2025 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE). IEEE, 2025. https://doi.org/10.1109/edpee65754.2025.00177.

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Lu, Yanchun, Hongqi Yang, Ting Lei, and Yong Pan. "Research on spares optimization method of the complex electronic information system based on Monte Carlo simulation." In International Conference on Mechatronics and Intelligent Control (ICMIC 2024), edited by Kun Zhang and Pascal Lorenz. SPIE, 2025. https://doi.org/10.1117/12.3044953.

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Chen, Make, Haitian Geng, and Jingran Li. "Research on Multi-Stage Decision Optimization Based on Monte Carlo Simulation and Deep Q-Network Algorithm." In 2025 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE). IEEE, 2025. https://doi.org/10.1109/edpee65754.2025.00175.

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Reports on the topic "Monte Carlo simulation Optimization"

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Welch, Tabitha. Exploring the Great Pyramid: Stand-Alone Monte Carlo Simulations for Detector Optimization. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1769388.

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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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Glaser, R. Monte Carlo simulation of scenario probability distributions. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/632934.

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Xu, S. L., B. Lai, and P. J. Viccaro. APS undulator and wiggler sources: Monte-Carlo simulation. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10134610.

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Douglas, L. J. Monte Carlo Simulation as a Research Management Tool. Office of Scientific and Technical Information (OSTI), 1986. http://dx.doi.org/10.2172/1129252.

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Aguayo Navarrete, Estanislao, Austin S. Ankney, Timothy J. Berguson, et al. Monte Carlo Simulation Tool Installation and Operation Guide. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1095434.

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Xu, S. L., B. Lai, and P. J. Viccaro. APS undulator and wiggler sources: Monte-Carlo simulation. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/5494991.

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Boyd, Lain D. Monte Carlo Simulation of Radiation in Hypersonic Flows. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada414031.

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Richie, David A., James A. Ross, Song J. Park, and Dale R. Shires. A Monte Carlo Method for Multi-Objective Correlated Geometric Optimization. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada603830.

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