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Journal articles on the topic 'Monte Carlo simulation Optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 June 2025

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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 has been used to construct the Efficient Frontier and find the weights of Maximum Sharpe Ratio portfolio and Minimum Variance portfolio. Lastly, this study put the market price data between 2023/03/12 and 2023/04/12 into the portfolios which had been built in the last step. The returns were compared to the S&P 500 subsequently. As the results shows, the Maximum Sharpe Ratio portfolio is performed better than S&P 500, Minimum Variance portfolio is performed worse than S&P 500. The results of this paper show the performance of these two portfolios compare to the market, which may help investors to decide which strategy to use when it comes to constructing a portfolio.

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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 antithetic variates generally performs well, but in power options, the control variable method has a better effect on Monte Carlo optimization. By leveraging these variance reduction techniques, the accuracy and effectiveness of Monte Carlo simulations can be significantly enhanced, leading to more reliable option pricing. The results not only demonstrate the important role of variance-reduction techniques in the Monte Carlo method but also offer practical methods to improve option pricing strategy.

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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 probabilistic perspective on potential outcomes. Comparative analyses with other forecasting models, such as graphic analysis, underscore the superior reliability of Monte Carlo simulation in evaluating risks and rewards. Furthermore, the paper explores the application of Monte Carlo simulation in real-world scenarios, such as portfolio optimization and retirement planning, highlighting its pragmatic value for individual investors navigating the complexities of the stock market. The research concludes by acknowledging the limitations of the approach and advocating for a comprehensive consideration of all relevant factors in financial decision-making. This exploration serves as a valuable resource for individual investors seeking informed insights into probabilistic forecasting methods for effective stock price predictions.

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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 optimization in the whole production process, an integer linear programming model is constructed and optimized and solved using particle swarm optimization algorithm. A large number of decision combinations and the foraging behavior of bird flocks are simulated to find the optimal detection, dismantling and processing strategies to minimize the total cost and improve the product quality. The final analysis verifies the effectiveness of the proposed method and provides scientific and reasonable decision support for enterprises in complex production environments.

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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 which is implemented in the inventory system. Result: Through the use of Quasi Monte Carlo in Periodic Review in Inventory System, the minimum inventory cost expected by MSMEs is achieved.

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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 inaccessible components of work pieces is determined. The complex process requires a high level of optimization of the instrumental parameters for each sample type whereby the accurate simulation of the physical interactions between X-rays and the sample material is a supplement and alternative to time consuming measurements.

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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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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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Zhao, Zhiqiang, and Feiyue Zhou. "Optimal Control Methods of Experiment Times in System-of-Systems Combat Computer Simulation." ITM Web of Conferences 26 (2019): 03004. http://dx.doi.org/10.1051/itmconf/20192603004.

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In the process of scheme optimization, in order to eliminate the influence of random factor, it needs to conduct computer simulation of Monte Carlo. Therefore, it is proposed to introduce confidence interval into systemof-systems combat simulation, and confirm whether the Monte Carlo simulation finishes according to data sample generated in simulation process. According to characteristic of data sample, extend correspondingly confidence interval method, and under the condition of obtaining the solution meeting accuracy requirements, reduce simulation experiment times as far as possible. The simulation experiment results show that confidence interval extension method is able to possess self-adaptation control to Monte Carlo simulation.

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Valihrach, Jakub, and Petr Konečný. "Exit Condition for Probabilistic Assessment Using Monte Carlo Method." Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series 10, no. 1 (2010): 1–9. http://dx.doi.org/10.2478/v10160-010-0014-3.

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Exit Condition for Probabilistic Assessment Using Monte Carlo Method This paper introduces a condition used to exit a probabilistic assessment using the Monte Carlo simulation, and to evaluate it with regard to the relationship between the computed estimate of the probability of failure and the target design probability. The estimation of probability of failure is treated as a random variable, considering its variance that is dependent on the number of performed Monte Carlo simulation steps. After theoretical derivation of the decision condition, it is tested numerically with regard to its accuracy and computational efficiency. The condition is suitable for optimization design using the Monte Carlo method.

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Kochebina, Olga, David Sarrut, Nicolas Arbor, et al. "GATE Monte Carlo simulation toolkit for medical physics." EPJ Web of Conferences 302 (2024): 16002. http://dx.doi.org/10.1051/epjconf/202430216002.

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The GATE toolkit (GEANT4 Application for Tomographic Emission) is a GEANT4-based (GEometry ANd Tracking) platform for Monte Carlo simulations in medical physics. GATE applications can be divided into two main axes: radiation-based medical imaging and radiotherapy/dosimetry. The accurate modeling of the first one is crucial for system design and optimization as well as for development and refinement of image analysis algorithms. The importance of the precise simulation of the second is essential for characterisation of external beam radiotherapy (proton therapy and carbon ion therapy) and absorbed dose assessment. Within this paper, we discuss the main features of GATE and give a general view on applications, followed by insights into future development perspectives.

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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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Meher, Premananda, and Rohita Kumar Mishra. "Risk-Adjusted Portfolio Optimization: Monte Carlo Simulation and Rebalancing." Australasian Business, Accounting and Finance Journal 18, no. 3 (2024): 85–101. http://dx.doi.org/10.14453/aabfj.v18i3.06.

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This study evaluates the risk-adjusted performance of a diversified portfolio in the Indian financial market from 2011 to 2021, incorporating Nifty 50 stocks and new-age assets. Leveraging Monte Carlo simulations and mathematical optimization, the research identifies an optimal portfolio on the efficient frontier. Integration of the Black-Litterman model provides a comparative analysis, emphasizing the impact of investor views. Despite transaction costs, optimized portfolios outperform the Nifty 50 index, with the rebalanced portfolio demonstrating higher cumulative returns. Key findings include TCS. NS is a leader in share price, HDFCBANK.NS showcasing stability and alternative assets exhibit higher volatility but have the potential for amplified returns. This research offers valuable insights for investors seeking resilient strategies in the Indian financial landscape.

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Kolafa, Jiří. "On optimization of Monte Carlo simulations." Molecular Physics 63, no. 4 (1988): 559–79. http://dx.doi.org/10.1080/00268978800100381.

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Lécot, Christian, and Faysal El Khettabi. "Quasi-Monte Carlo Simulation of Diffusion." Journal of Complexity 15, no. 3 (1999): 342–59. http://dx.doi.org/10.1006/jcom.1999.0509.

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Adavize, Momoh Hameed, and Mohammed Ahmed. "Assessing hardware-driven variations from workstations to personal computers in gate simulation time for radioembolization studies." Science World Journal 20, no. 1 (2025): 181–85. https://doi.org/10.4314/swj.v20i1.24.

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Monte Carlo GATE (Geant4 Application for Tomographic Emission) is widely used in medical physics for tomographic emission simulations, particularly in nuclear medicine procedures like radioembolization for liver cancer treatment. Despite its userfriendly interface, GATE simulations are often criticized for their slow computational speed, which poses challenges for students and researchers. Factors such as computer hardware (RAM, CPU, GPU, storage type), simulation settings, and the complexity of physics modeling significantly influence simulation times. This study investigates how hardware configurations impact GATE simulation performance by simulating a Yttrium-90 (Y-90) radioembolization procedure using a cylindrical phantom and tumor inserts. Simulations were conducted on three computers: an HP workstation, a DELL, and an HP Envy, with varying hardware specifications. Results revealed that the workstation, equipped with higher RAM, CPU, and GPU capabilities, demonstrated significantly faster simulation times compared to the personal computers. This highlights the critical role of advanced hardware in reducing computational time for GATE simulations. The study provides valuable insights for young researchers, emphasizing the importance of hardware optimization to achieve efficient and timely results in Monte Carlo-based medical physics research.

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Kerdkaen, N., T. Sutthibutpong, S. Phongphanphanee, S. Boonchui, and J. Wong-ekkabut. "Monte Carlo simulations of nanotube filler in composite material: code optimization." IOP Conference Series: Materials Science and Engineering 1234, no. 1 (2022): 012026. http://dx.doi.org/10.1088/1757-899x/1234/1/012026.

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Abstract The electrically conductive polymer composites (CPCs) have attracted intensive attention for several decades due to their flexibility and unique electrical properties. CPCs are potentially used in many applications such as flexible electrodes, batteries, and strain sensors. The percolated conductive pathways are formed by conductive filler in polymer matrix which is a major effect on the electrical behavior of CPCs. Computational simulations have been used to study the percolation phenomena of CPCs. The simulation algorithms need to be developed and optimized for reducing the simulation time-consuming. In this study, the in-house Monte Carlo simulation that used to estimate percolation threshold is optimized. To simulate in the large-scale system, cut-off distance will be defined to avoid unnecessary complex calculations. The calculation sequence within the code has been rearranged to omit the unnecessary calculation processes. Results show that the optimized software takes less processing time than the previous version around 5 times. Therefore, we can perform the large system to investigate the percolation phenomenon with less lattice confinement effect.

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Andreeva, Darya A., Andrey A. Zaitsev, and Nikita R. Sandu. "OPTIMIZATION OF THE CRYPTOCURRENCY PORTFOLIO USING THE MONTE CARLO METHOD." SOFT MEASUREMENTS AND COMPUTING 10, no. 83 (2024): 26–38. https://doi.org/10.36871/2618-9976.2024.10.003.

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This article discusses a way to optimize a portfolio consisting of cryptocurrencies to get more benefits. Mathematical Monte Carlo simulation allows you to predict the possible outcomes of an uncertain event, as well as identify the most profitable option for asset allocation in the portfolio. Cryptocurrencies that are leading in terms of market capitalization were selected for analysis, which allows you to evaluate the market by optimizing assets that cover a significant part of it. The high volatility of assets in the cryptocurrency market creates a large number of risks, so it is necessary to minimize them and increase the efficiency of investing funds. The scope of the results is the cryptocurrency market itself and investors who want to increase their profits and improve investment decisions in an uncertain environment. The article aims to optimize the investment portfolio of cryptocurrencies with the largest capitalization using the Monte Carlo simulation method by determining the optimal weights of each cryptocurrency in the portfolio, as well as finding profitability and minimal risk. The Monte Carlo simulation method was used to write this article. As a result, the optimal distribution of cryptocurrencies in the investment portfolio and the assumption of the level of profitability and risk are determined. Using the Monte Carlo simulation method, an investment portfolio of cryptocurrencies leading in the market in terms of capitalization was optimized. This reduces the risk of loss of funds for investors, which will lead to an increase in portfolio profitability in conditions of high volatility of crypto assets.

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Zhang, Xiaobo, Zhenzhou Lu, Kai Cheng, and Yanping Wang. "A novel reliability sensitivity analysis method based on directional sampling and Monte Carlo simulation." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 4 (2020): 622–35. http://dx.doi.org/10.1177/1748006x19899504.

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Local reliability sensitivity and global reliability sensitivity are required in reliability-based design optimization, since they can provide rich information including variable importance ranking and gradient information. However, traditional Monte Carlo simulation is inefficient for engineering application. A novel numerical simulation method based on Monte Carlo simulation and directional sampling is proposed to simultaneously estimate local reliability sensitivity and global reliability sensitivity. By suitable transformation, local reliability sensitivity and global reliability sensitivity can be estimated simultaneously as by-products of reliability analysis for Monte Carlo simulation method. The key is how to efficiently classify Monte Carlo simulation samples into two categories: failure samples and safety samples. Directional sampling method, a classical reliability analysis method, is more efficient than Monte Carlo simulation for reliability analysis. A novel strategy based on nearest Euclidean distance is proposed to approximately screen out failure samples from Monte Carlo simulation samples using directional sampling samples. In the proposed method, local reliability sensitivity and global reliability sensitivity are by-products of reliability analysis using the directional sampling method. Different from existing methods, the proposed method does not introduce hypotheses and does not require additional gradient information. The advantages of the Monte Carlo simulation and directional sampling are well integrated in the proposed method. The accuracy and the efficiency of the proposed method for local reliability sensitivity and global reliability sensitivity are demonstrated by four numerical examples and two engineering examples including the headless rivet and the wing box structure.

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Prasad Gundu, Ram, P. Pardhasaradhi, S. Koteswara Rao, and V. Gopi Tilak. "TOA-based source localization using ML estimation." International Journal of Engineering & Technology 7, no. 2.7 (2018): 742. http://dx.doi.org/10.14419/ijet.v7i2.7.10936.

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This paper proposes the Time of arrival (TOA) measurement model for finding the position of a stationary emitting source for Line-of-Sight (LOS) scenario. Here Maximum Likelihood Estimation (MLE) is used as the positioning algorithm. For approximation of the roots of the solution, which directly corresponds to the source location, the optimization techniques used are Gauss-Newton, Gradient descent and Newton-Raphson methods. Two different cases are considered for investigation in this paper. The first case compares the three different optimization techniques in terms of convergence rate. In the second case the error values obtained from two different scenarios are compared, one involving a single trial only, while the second scenario uses Monte Carlo method of simulations. Firstly, the error values, for both the coordinates (two-dimensional), obtained by getting the difference between the measured source positions and the initially guessed source position are obtained for a single trial. Later using Monte Carlo simulation method, the Root-Mean-Square (RMS) error values, for both the coordinates (two-dimensional), for the optimization techniques are obtained. To improve the performance of the algorithm, Monte Carlo simulation has been used for multiple trials.

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Basil, Martin, and Andrew Jamieson. "Uncertainty of Complex Systems by Monte Carlo Simulation." Measurement and Control 32, no. 1 (1999): 16–20. http://dx.doi.org/10.1177/002029409903200104.

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Geng, Dai, Shi Min Zhang, De Guo Wang, Jian Gao, and Li Sha Dai. "The Optimization Analysis of Equipment Maintenance Based on Monte-Carlo Simulation." Advanced Materials Research 189-193 (February 2011): 424–27. http://dx.doi.org/10.4028/www.scientific.net/amr.189-193.424.

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In order to improve the reliability and security in production, effectively checking and maintaining equipment must be put into practice. In this paper, the on-condition maintenance period of the equipment is optimized by Monte-Carlo for the lowest maintenance cost in unit time by expressing the maintenance interval as an exponential function parameterizing Weibull’s distribution function。Finally, the oil centrifugal pump as an example was demonstrated. The results show that our model has the obvious economic benefits. The optimization analysis of equipment maintenance based on Monte-Carlo provides a theoretical basis for optimized detection and maintenance decisions.

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Shah, Sahil. "Monte Carlo Simulation in Renewable Energy Planning: A Comprehensive Review and Novel Framework for Uncertainty Quantification." American Journal of Engineering and Technology 07, no. 06 (2025): 24–45. https://doi.org/10.37547/tajet/volume07issue06-04.

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The integration of renewable energy sources into modern power systems presents significant challenges due to inherent uncertainties in resource availability, demand fluctuations, and technical performance. Monte Carlo simulation has emerged as a powerful tool for addressing these uncertainties in renewable energy planning and optimization. This paper presents a comprehensive review of Monte Carlo applications across solar, wind, and hybrid renewable energy systems over the past two decades. Through systematic analysis of 75+ peer-reviewed publications, we identify key methodological trends, implementation challenges, and emerging opportunities. The review reveals that while Monte Carlo methods have been extensively applied to single-source renewable systems, significant gaps exist in addressing correlated uncertainties across hybrid configurations and real-time operational scenarios. We propose a novel unified framework that integrates machine learning-enhanced sampling techniques with traditional Monte Carlo approaches to improve computational efficiency while maintaining accuracy. The framework addresses five critical uncertainty dimensions: resource variability, demand stochasticity, equipment degradation, market price fluctuations, and grid integration constraints. Case studies demonstrate that the proposed framework reduces computational time by 40-60% compared to traditional methods while improving prediction accuracy by 15-25%. This review provides researchers and practitioners with a structured approach to implementing Monte Carlo simulations for renewable energy planning under uncertainty, contributing to more robust and economically viable renewable energy deployment strategies.

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Koreshi, Zafar, Hamda Khan, and Muhammad Yaqub. "Variational methods and speed-up of Monte Carlo perturbation computations for optimal design in nuclear systems." Nuclear Technology and Radiation Protection 34, no. 3 (2019): 211–21. http://dx.doi.org/10.2298/ntrp190214032k.

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Seeking optimal material distribution in a nuclear system to maximize a response function of interest has been a subject of considerable interest in nuclear engineering. Examples are the optimal fuel distribution in a nuclear reactor core to achieve uniform burnup using minimum critical mass and the use of composite materials with an optimal mix of constituent elements in detection systems and radiation shielding. For such studies, variational methods have been found to be useful but, they have been used for standalone analyses often restricted to idealized models, while more elaborate design studies have required computationally expensive Monte Carlo simulations ill-suited to iterative schemes for optimization. Such an inherent disadvantage of Monte Carlo methods changed with the development of perturbation algorithms but, their efficiency is still dependent on the reference configuration for which a hit-and-trial approach is often used. In the first illustrative example, this paper explores the computational speedup for a bare cylindrical reactor core, achievable by using a variational result to enhance the computational efficiency of Monte Carlo design optimization simulation. In the second example, the effect of non-uniform material density in a fixed-source problem, applicable to optimal moderator and radiation shielding, is presented. While applications of this work are numerous, the objective of this paper is to present preliminary variational results as inputs to elaborate stochastic optimization by Monte Carlo simulation for large and realistic systems.

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Lytvynenko, Volodymyr, Svitlana Antoshuk, Andrii Mrykhin, Nataliya Savina, and Oleh Marchuk. "Applying the Monte Carlo Method for Modeling Order Fulfillment with Consideration of Supply Risk." Modeling, Control and Information Technologies, no. 7 (December 7, 2024): 251–57. https://doi.org/10.31713/mcit.2024.078.

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This paper presents the application of the Monte Carlo method for modelling order fulfilment, taking into account supply risks and delays. The method allows for the consideration of stochastic events and uncertainties in supply chains, which are becoming increasingly complex and vulnerable to various risks, such as production failures, transportation issues, and external factors. Using probabilistic distributions, the Monte Carlo method enables forecasting the frequency and impact of delays, supporting proactive decision-making in logistics management. The developed model assesses the likelihood of on-time order fulfilment under uncertainty, demonstrating the effectiveness of Monte Carlo simulations. The simulation results provide insights into delay patterns, risk factors, and potential strategies for minimizing them, creating opportunities for supply chain optimization.

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Engeman, Richard M., and Robert T. Sugihara. "OPTIMIZATION OF VARIABLE AREA TRANSECT SAMPLING USING MONTE CARLO SIMULATION." Ecology 79, no. 4 (1998): 1425–34. http://dx.doi.org/10.1890/0012-9658(1998)079[1425:oovats]2.0.co;2.

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Hu, Yiwen. "Portfolio Optimization Using Machine Learning Method and Monte Carlo Simulation." Highlights in Business, Economics and Management 41 (October 15, 2024): 214–20. http://dx.doi.org/10.54097/farx3k44.

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Investment portfolio optimization is a crucial aspect of quantitative finance, aiming to maximize returns and minimize risks. This study focuses on optimizing investment allocation among five selected stocks (BAIC Blue Valley, BYD, Chang’ an Automobile, Kweichow Moutai, Sunwoda) over 100 days using Monte Carlo simulation and machine learning methods. This study uses the sliding window approach with the linear regression model to predict future returns and calculate the variance-covariance matrix to determine the optimal portfolio weights, leading to two key strategies: maximizing the Sharpe ratio and minimizing the risk. The results reveal that the Max Sharpe Ratio portfolio achieved a cumulative return of 0.0909, significantly outperforming the CSI 300 index’s return of -0.0348. Additionally, the Min Risk portfolio exceeded the market index with a cumulative return of 0.0062. These findings demonstrate that both the Max Sharpe Ratio and Min Risk strategies are effective in achieving a balance between risk and return and surpassing the market, offering valuable insights for investors seeking optimized portfolio allocations.

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Zhang, Jinfeng, S. C. Kou, and Jun S. Liu. "Biopolymer structure simulation and optimization via fragment regrowth Monte Carlo." Journal of Chemical Physics 126, no. 22 (2007): 225101. http://dx.doi.org/10.1063/1.2736681.

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Boldyš, Jiří, Jiří Dvořák, Magdaléna Skopalová, and Otakar Bělohlávek. "Monte Carlo simulation of PET images for injection dose optimization." International Journal for Numerical Methods in Biomedical Engineering 29, no. 9 (2012): 988–99. http://dx.doi.org/10.1002/cnm.2527.

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Zhevnyak, O. G., V. M. Borzdov, A. V. Borzdov, and A. N. Petlitsky. "Monte Carlo Simulation of Flash Memory Elements’ Electrophysical Parameters." Devices and Methods of Measurements 13, no. 4 (2022): 276–80. http://dx.doi.org/10.21122/2220-9506-2022-13-4-276-280.

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Operation of modern flash memory elements is based on electron transport processes in the channel of silicon MOSFETs with floating gate. The aim of this work was calculation of electron mobility and study of the influence of phonon and ionized impurity scattering mechanisms on the mobility, as well as calculation of parasitic tunneling current and channel current in the conductive channel of flash memory element. Numerical simulation during the design stage of flash memory element allows working out guidelines for optimization of device parameters defining its performance and reliability.In the work such electrophysical parameters, characterizing electron transport, as mobility and average electron energy, as well as tunneling current and current in the channel of the flash memory element are studied via the numerical simulation by means of Monte Carlo method. Influence of phonon and ionized impurity scattering processes on electron mobility in the channel has been analyzed. It is shown that in the vicinity of drain region a sufficient decrease of electron mobility defined by phonon scattering processes occurs and the growth of parasitic tunneling current is observed which have a negative influence on device characteristics.The developed simulation program may be used in computer-aided design of flash memory elements for the purpose of their structure optimization and improvement of their electrical characteristics.

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Cai, W. "Fixture optimization for sheet panel assembly considering welding gun variations." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 222, no. 2 (2008): 235–46. http://dx.doi.org/10.1243/09544062jmes457.

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Sheet panel assembly in automotive body manufacturing typically involves fixturing (including locating and clamping), joining (especially welding), and unclamping. Research has shown that the choices of fixturing schemes are important and that fixture optimization can generally improve the assembly dimensional quality with reduced assembly distortions. However, these research studies generally modelled weld guns as constant disturbances (such as deterministic forces) without considering the weld gun variations to avoid the complexity of Monte Carlo simulations. In the current paper, a fixture optimization model is formulated to minimize the assembly dimensional variations under welding gun variations. It is shown that, by using the proposed weld gun variations model, an assembly variation simulation can be converted to a deterministic assembly model, eliminating the need for Monte Carlo simulation. The efficiency improvement makes it feasible for a full scale optimization solution for optimal fixture locations with welding variations. The current paper further introduces the fixture-element models for continuous variable optimization in finite-element analysis, an enabler for fixture optimization. Numerical examples are shown to demonstrate the proposed methods.

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Kalda, Jaan. "On the optimization of Monte-Carlo simulations." Physica A: Statistical Mechanics and its Applications 246, no. 3-4 (1997): 646–58. http://dx.doi.org/10.1016/s0378-4371(97)00354-3.

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Martineau, A., J. M. Rocchisani, and J. L. Moretti. "Coded aperture optimization using Monte Carlo simulations." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 616, no. 1 (2010): 75–80. http://dx.doi.org/10.1016/j.nima.2010.02.261.

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Bastin, Fabian, Cinzia Cirillo, and Stephane Hess. "Evaluation of Optimization Methods for Estimating Mixed Logit Models." Transportation Research Record: Journal of the Transportation Research Board 1921, no. 1 (2005): 35–43. http://dx.doi.org/10.1177/0361198105192100105.

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The performances of different simulation-based estimation techniques for mixed logit modeling are evaluated. A quasi–Monte Carlo method (modified Latin hypercube sampling) is compared with a Monte Carlo algorithm with dynamic accuracy. The classic Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization algorithm line-search approach and trust region methods, which have proved to be extremely powerful in nonlinear programming, are also compared. Numerical tests are performed on two real data sets: stated preference data for parking type collected in the United Kingdom, and revealed preference data for mode choice collected as part of a German travel diary survey. Several criteria are used to evaluate the approximation quality of the log likelihood function and the accuracy of the results and the associated estimation runtime. Results suggest that the trust region approach outperforms the BFGS approach and that Monte Carlo methods remain competitive with quasi–Monte Carlo methods in high-dimensional problems, especially when an adaptive optimization algorithm is used.

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Haq, Fajri Izzul, Abdul Hafizh Firmansyah, Wasis Putro Jatmiko, and Soffiana Agustin. "Implementasi Penggunaan Simulasi Monte Carlo dalam Estimasi Distribusi Jajanan Tradisional." Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) 7, no. 4 (2024): 499–507. https://doi.org/10.32672/jnkti.v7i4.7696.

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Abstrak— Penentuan estimasi order, biaya, dan keuntungan dalam manajemen distribusi sangatlah penting untuk meningkatkan efisiensi operasional dan profitabilitas UMKM. Ketidakpastian jumlah order dapat menyebabkan risiko kelebihan atau kekurangan stok, yang berdampak negatif pada operasional dan finansial. Tujuan penelitian ini adalah mengaplikasikan simulasi Monte Carlo untuk mengestimasi jumlah pesanan distributor guna mengatasi ketidakpastian tersebut. Metode yang digunakan adalah simulasi Monte Carlo dengan program Microsoft Excel, menghasilkan nilai-nilai acak yang diterapkan dalam sistem distribusi. Data penjualan selama 50 hari digunakan untuk simulasi. Hasil penelitian menunjukkan tingkat kesalahan estimasi sebesar 15.92% untuk Distributor 1, 15.50% untuk Distributor 2, dan 14.42% untuk Distributor 3. Kesimpulannya, metode simulasi Monte Carlo efektif dalam mengatasi ketidakpastian manajemen distribusi pesanan, meskipun perlu pengoptimalan lebih lanjut untuk mengurangi kesalahan estimasi.Kata kunci: Monte Carlo, Manajemen Distribusi, Microsoft Excel.Abstract−− Determining order, cost, and profit estimates in distribution management is crucial for enhancing SMEs’ operational efficiency and profitability. Uncertainty in order quantities can lead to the risk of overstock or stock shortages, negatively impacting operations and finances. This study aims to apply the Monte Carlo simulation to estimate the number of distributor orders to address this uncertainty. The method used is the Monte Carlo simulation with Microsoft Excel, generating random values applied within the distribution system. Sales data over 50 days were used for the simulation. The research results showed an estimation error rate of 15.92% for Distributor 1, 15.50% for Distributor 2, and 14.42% for Distributor 3. In conclusion, the Monte Carlo simulation method effectively addresses the uncertainty in order distribution management, although further optimization is needed to reduce estimation errors.Keywords: Monte Carlo, Distribution Management, Microsoft Excel.

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Busqué, Raquel, Matias Bossio, Raimon Fabregat, et al. "Hybrid CFD and Monte Carlo-Driven Optimization Approach for Heat Sink Design." Energies 18, no. 11 (2025): 2801. https://doi.org/10.3390/en18112801.

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This study introduces a hybrid topology optimization methodology aimed at improving heat sink efficiency through a data-driven approach. The method integrates CFD simulations in Ansys Fluent with a Monte Carlo-driven optimization algorithm, modeling the design of a heat sink domain as a porous medium. Porosity is used as a design variable, iteratively adjusted in a binary manner to optimize fluid-solid distribution. Three design variants were evaluated, with the selected optimized configuration reaching a maximum temperature of 57.11 °C, compared to 46.15 °C for a baseline serpentine channel. Despite slightly higher peak temperature, the optimized design achieved a substantial reduction in pressure drop, up to 91.57%, translating into significantly lower pumping power requirements and thus lower energy consumption. Experimental validation, using physical prototypes of both the reference and optimized channels, confirmed strong agreement with simulation results, with average surface temperatures of 29.27 °C and 30.03 °C, respectively. These findings validate the accuracy of the simulation-based approach and highlight the potential of data-driven optimization in thermal management system designs.

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Inna Muthmainnah, Alfina Richi, and Ichwanul Muslim Karo karo. "Optimization of Kampung Chicken Seedling Production Using the Monte Carlo Method (Case Study on Hacci Farm)." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 3, no. 3 (2024): 852–57. http://dx.doi.org/10.59934/jaiea.v3i3.526.

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This research aims to optimize free-range chicken hatchery production at Hacci Farm using the Monte Carlo method, which is known for its ability to overcome uncertainty and variability in production. The data in this research uses chicken seed production data in 2022 and 2023 which is then used as input in the Monte Carlo simulation. The simulation results reveal that the Monte Carlo method provides more accurate predictions regarding free-range chicken seed production. Through this simulation, Hacci Farm can predict the amount of free-range chicken hatchery production in the future and can prepare more effective strategic steps in hatchery management to increase operational efficiency, reduce risks, and maximize free-range chicken production results. The results of implementing the Monte Carlo method show quite high accuracy, namely 98.7% in 2022 and 97.95% in 2023, this can help in planning and managing resources optimally. It is hoped that the use of this method can make a significant contribution in increasing the quality and quantity of free-range chicken production at Hacci Farm, as well as providing a model that can be adopted by other farms for similar purposes.

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Saraiva Júnior, Abraão Freires, Cristiane de Mesquita Tabosa, and Reinaldo Pacheco da Costa. "Monte Carlo simulation applied to order economic analysis." Production 21, no. 1 (2011): 149–64. http://dx.doi.org/10.1590/s0103-65132011005000016.

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The use of mathematical and statistical methods can help managers to deal with decision-making difficulties in the business environment. Some of these decisions are related to productive capacity optimization in order to obtain greater economic gains for the company. Within this perspective, this study aims to present the establishment of metrics to support economic decisions related to process or not orders in a company whose products have great variability in variable direct costs per unit that generates accounting uncertainties. To achieve this objective, is proposed a five-step method built from the integration of Management Accounting and Operations Research techniques, emphasizing the Monte Carlo simulation. The method is applied from a didactic example which uses real data achieved through a field research carried out in a plastic products industry that employ recycled material. Finally, it is concluded that the Monte Carlo simulation is effective for treating variable direct costs per unit variability and that the proposed method is useful to support decision-making related to order acceptance.

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Xue, Gang, Ruiping Zhang, Yihao Chen, Wei Xu, and Changxing Zhang. "Glucose Sensor Design Based on Monte Carlo Simulation." Biosensors 15, no. 1 (2025): 17. https://doi.org/10.3390/bios15010017.

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Continuous glucose monitoring based on the minimally invasive implantation of glucose sensor is characterized by high accuracy and good stability. At present, glucose concentration monitoring based on fluorescent glucose capsule sensor is a new development trend. In this paper, we design a fluorescent glucose capsule sensor with a design optimization study. The motion trajectory of incident light in the fluorescent gel layer is simulated based on the Monte Carlo method, and the cloud maps of light intensity with the light intensity distribution at the light-receiving layer are plotted. Altering the density of fluorescent molecules, varying the thickness of tissue layers, and adjusting the angle of incidence deflection, the study investigates the influence of these parameter changes on the optimal position of reflected light at the bottom. Finally, the simulation results were utilized to design and fabricate a fluorescent glucose capsule sensor. Rabbit subcutaneous tissue glucose level tests and real-time glucose solution concentration monitoring experiments were performed. This work contributes to the real-time monitoring of glucose levels and opens up new avenues for research on fabricating glucose sensors.

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Enggar, Muhammad, Moch Nuruddin, and Efta Dhartikasari3. "CONTROL OF RAW MATERIALS INVENTORY PROBABILISTIC MODEL USING MONTE CARLO SIMULATION AND DYNAMIC SYSTEM." Jurnal Teknovasi 9, no. 01 (2022): 37–44. http://dx.doi.org/10.55445/jt.v9i01.36.

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Increasing efficiency and effectiveness is a fundamental problem for every company. Optimization of raw material inventory is an effort for this problem. PT XYZ is a company engaged in manufacturing various flavors of beverages, one of which is sugar. The level of sugar usage in each month is probabilistic and dynamic. PT XYZ's raw material inventory is determined based on field assumptions, causing a shortage or excess of raw material inventory. This study aims to determine the policy of raw material inventory P backorder model with the integration of Monte Carlo simulation and Dynamic System. The raw material inventory policy is determined based on the order time interval, safety stock, and maximum capacity and compares the actual total inventory cost with the Monte Carlo simulation. Dynamic system simulation is carried out to ensure that the P backorder model inventory policy can be applied. The results showed that the total inventory without Monte Carlo was better than the total cost of the Monte Carlo simulation of Rp198.846.582,98 with an order time interval of 0,30929 years, a maximum inventory capacity of 11149,8 kg and a safety stock of 4335,83 kg. From the results of the comparison using the one sample T-test, it was found that there was no statistical difference between the results of the dynamic system simulation and the results from the calculation of the P backorder model so that the P backorder model policy could be applied to PT XYZ to determine the minimum inventory cost.

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Oliveira, Marcos Tadeu Barros de, Patrícia de Sousa Oliveira Silva, Elisa Oliveira, André Luís Marques Marcato, and Giovani Santiago Junqueira. "Availability Projections of Hydroelectric Power Plants through Monte Carlo Simulation." Energies 14, no. 24 (2021): 8398. http://dx.doi.org/10.3390/en14248398.

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The present work proposes a Monte Carlo Simulation (MCS) to obtain availability projections for Hydroelectric Power Plants (HPP), based mainly on regulatory aspects involving the Availability Factor (AFA). The main purpose of the simulation is to generate scenarios to obtain statistics for risk analysis and decision-making in relation to the HPP. The proposed methodology consists of two steps, firstly, the optimization of the maintenance schedule of the hydroelectric plant is carried out, in order to allocate the mandatory maintenance in the simulation horizon. Then, for the MCS, scenarios of forced shutdowns of the Generating Units (GU) will be generated, which directly influence the operation and, consequently, the availability of the HPP. The scenarios will be inserted into an operation optimization model, which considers the impact of forced shutdown samples on the MCS. The proposed modeling was applied using real data from the Santo Antônio HPP, which is one of the largest hydroelectric plants in Brazil.

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Wang, Bin. "Reliability Assessment of Power System Using Importance Sampling Technique Based on Layer Optimization Simulation." Applied Mechanics and Materials 88-89 (August 2011): 554–58. http://dx.doi.org/10.4028/www.scientific.net/amm.88-89.554.

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An improved importance sampling method with layer simulation optimization is presented in this paper. Through the solution sequence of the components’ optimum biased factors according to their importance degree to system reliability, the presented technique can further accelerate the convergence speed of the Monte-Carlo simulation. The idea is that the multivariate distribution’ optimization of components in power system is transferred to many steps’ optimization based on importance sampling method with different optimum biased factors. The practice is that the components are layered according to their importance degree to the system reliability before the Monte-Carlo simulation, the more forward, the more important, and the optimum biased factors of components in the latest layer is searched while the importance sampling is carried out until the demanded accuracy is reached. The validity of the presented is verified using the IEEE-RTS79 test system.

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