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Littérature scientifique sur le sujet « Stochastic simulation algorithms »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Stochastic simulation algorithms"

Mooasvi, Azam, and Adrian Sandu. "APPROXIMATE EXPONENTIAL ALGORITHMS TO SOLVE THE CHEMICAL MASTER EQUATION." Mathematical Modelling and Analysis 20, no. 3 (2015): 382–95. http://dx.doi.org/10.3846/13926292.2015.1048760.

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This paper discusses new simulation algorithms for stochastic chemical kinetics that exploit the linearity of the chemical master equation and its matrix exponential exact solution. These algorithms make use of various approximations of the matrix exponential to evolve probability densities in time. A sampling of the approximate solutions of the chemical master equation is used to derive accelerated stochastic simulation algorithms. Numerical experiments compare the new methods with the established stochastic simulation algorithm and the tau-leaping method.

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Stutz, Timothy C., Alfonso Landeros, Jason Xu, Janet S. Sinsheimer, Mary Sehl, and Kenneth Lange. "Stochastic simulation algorithms for Interacting Particle Systems." PLOS ONE 16, no. 3 (2021): e0247046. http://dx.doi.org/10.1371/journal.pone.0247046.

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Interacting Particle Systems (IPSs) are used to model spatio-temporal stochastic systems in many disparate areas of science. We design an algorithmic framework that reduces IPS simulation to simulation of well-mixed Chemical Reaction Networks (CRNs). This framework minimizes the number of associated reaction channels and decouples the computational cost of the simulations from the size of the lattice. Decoupling allows our software to make use of a wide class of techniques typically reserved for well-mixed CRNs. We implement the direct stochastic simulation algorithm in the open source program

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Konopel'kin, M. Yu, S. V. Petrov, and D. A. Smirnyagina. "Implementation of stochastic signal processing algorithms in radar CAD." Russian Technological Journal 10, no. 5 (2022): 49–59. http://dx.doi.org/10.32362/2500-316x-2022-10-5-49-59.

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Objectives. In 2020, development work on the creation of a Russian computer-assisted design system for radars (radar CAD) was completed. Radar CAD provides extensive opportunities for creating simulation models for developing the hardware-software complex of radar algorithms, which take into account the specific conditions of aerospace environment observation. The purpose of the present work is to review and demonstrate the capabilities of radar CAD in terms of implementing and testing algorithms for processing stochastic signals.Methods. The work is based on the mathematical apparatus of line

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Wieder, Nicolas, Rainer H. A. Fink, and Frederic von Wegner. "Exact and Approximate Stochastic Simulation of Intracellular Calcium Dynamics." Journal of Biomedicine and Biotechnology 2011 (2011): 1–5. http://dx.doi.org/10.1155/2011/572492.

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In simulations of chemical systems, the main task is to find an exact or approximate solution of thechemical master equation(CME) that satisfies certain constraints with respect to computation time and accuracy. WhileBrownian motionsimulations of single molecules are often too time consuming to represent the mesoscopic level, the classicalGillespie algorithmis a stochastically exact algorithm that provides satisfying results in the representation of calcium microdomains.Gillespie's algorithmcan be approximated via thetau-leapmethod and thechemical Langevin equation(CLE). Both methods lead to a

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Ding, Liangliang, Jingyuan Zhou, Wenhui Tang, Xianwen Ran, and Ye Cheng. "Research on the Crushing Process of PELE Casing Material Based on the Crack-Softening Algorithm and Stochastic Failure Algorithm." Materials 11, no. 9 (2018): 1561. http://dx.doi.org/10.3390/ma11091561.

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In order to more realistically reflect the penetrating and crushing process of a PELE (Penetration with Enhanced Lateral Efficiency) projectile, the stochastic failure algorithm and crack-softening algorithm were added to the corresponding material in this paper. According to the theoretical analysis of the two algorithms, the material failure parameters (stochastic constant γ, fracture energy Gf, and tensile strength σT) were determined. Then, four sets of simulation conditions ((a) no crack softening, (b) no stochastic failure, (c) no crack softening and no stochastic failure, and (d) crack

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Altıntan, Derya, Vi̇lda Purutçuoğlu, and Ömür Uğur. "Impulsive Expressions in Stochastic Simulation Algorithms." International Journal of Computational Methods 15, no. 01 (2017): 1750075. http://dx.doi.org/10.1142/s021987621750075x.

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Jumps can be seen in many natural processes. Classical deterministic modeling approach explains the dynamical behavior of such systems by using impulsive differential equations. This modeling strategy assumes that the dynamical behavior of the whole system is deterministic, continuous, and it adds jumps to the state vector at certain times. Although deterministic approach is satisfactory in many cases, it is a well-known fact that stochasticity or uncertainty has crucial importance for dynamical behavior of many others. In this study, we propose to include this abrupt change in the stochastic

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Zhang, Ce, Xiangxiang Meng, and Yan Ji. "Parameter Estimation of Fractional Wiener Systems with the Application of Photovoltaic Cell Models." Mathematics 11, no. 13 (2023): 2945. http://dx.doi.org/10.3390/math11132945.

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Fractional differential equations are used to construct mathematical models and can describe the characteristics of real systems. In this paper, the parameter estimation problem of a fractional Wiener system is studied by designing linear filters which can obtain smaller tunable parameters and maintain the stability of the parameters in any case. To improve the identification performance of the stochastic gradient algorithm, this paper derives two modified stochastic gradient algorithms for the fractional nonlinear Wiener systems with colored noise. By introducing the forgetting factor, a forg

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Tan, Min Keng, Helen Sin Ee Chuo, Kit Guan Lim, Renee Ka Yin Chin, Soo Siang Yang, and Kenneth Tze Kin Teo. "A COMPARISON STUDY OF DETERMINISTIC AND METAHEURISTIC ALGORITHMS FOR STOCHASTIC TRAFFIC FLOW OPTIMIZATION UNDER SATURATED CONDITION." ICTACT Journal on Soft Computing 10, no. 3 (2020): 2117–23. https://doi.org/10.21917/ijsc.2020.0301.

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Traffic congestion is a perennial issue for most cities. Various artificial intelligence (AI) algorithms, which can categorize as deterministic and metaheuristic algorithms have been suggested to mitigate congestion. Although traffic flow is dynamic and stochastic in nature, most of the previous works evaluated the algorithms with a deterministic or nonstochastic traffic flow pattern. As such, the adaptiveness of those AI algorithms in dealing with stochastic traffic flow patterns is yet to be investigated. Therefore, this paper aims to explore the feasibility of both algorithm types in contro

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XU, ZI, YINGYING LI, and XINGFANG ZHAO. "SIMULATION-BASED OPTIMIZATION BY NEW STOCHASTIC APPROXIMATION ALGORITHM." Asia-Pacific Journal of Operational Research 31, no. 04 (2014): 1450026. http://dx.doi.org/10.1142/s0217595914500262.

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This paper proposes one new stochastic approximation algorithm for solving simulation-based optimization problems. It employs a weighted combination of two independent current noisy gradient measurements as the iterative direction. It can be regarded as a stochastic approximation algorithm with a special matrix step size. The almost sure convergence and the asymptotic rate of convergence of the new algorithm are established. Our numerical experiments show that it outperforms the classical Robbins–Monro (RM) algorithm and several other existing algorithms for one noisy nonlinear function minimi

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Bhatnagar, Shalabh, Vivek Kumar Mishra, and Nandyala Hemachandra. "Stochastic Algorithms for Discrete Parameter Simulation Optimization." IEEE Transactions on Automation Science and Engineering 8, no. 4 (2011): 780–93. http://dx.doi.org/10.1109/tase.2011.2159375.

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Thèses sur le sujet "Stochastic simulation algorithms"

Hu, Liujia. "Convergent algorithms in simulation optimization." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54883.

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It is frequently the case that deterministic optimization models could be made more practical by explicitly incorporating uncertainty. The resulting stochastic optimization problems are in general more difficult to solve than their deterministic counterparts, because the objective function cannot be evaluated exactly and/or because there is no explicit relation between the objective function and the corresponding decision variables. This thesis develops random search algorithms for solving optimization problems with continuous decision variables when the objective function values can be estima

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Qureshi, Sumaira Ejaz. "Comparative study of simulation algorithms in mapping spaces of uncertainty /." St. Lucia, Qld, 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16450.pdf.

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Vo, Hong Thanh. "On Efficient Algorithms for Stochastic Simulation of Biochemical Reaction Systems." Doctoral thesis, Università degli studi di Trento, 2013. https://hdl.handle.net/11572/369286.

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Computational techniques provide invaluable tools for developing a quantitative understanding the complexity of biological systems. The knowledge of the biological system under study is formalized in a precise form by a model. A simulation algorithm will realize the dynamic interactions encoded in the model. The simulation can uncover biological implications and derive further predictive experiments. Several successful approaches with different levels of detail have been introduced to deal with various biological pathways including regulatory networks, metabolic pathways and signaling pathways

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Vo, Hong Thanh. "On Efficient Algorithms for Stochastic Simulation of Biochemical Reaction Systems." Doctoral thesis, University of Trento, 2013. http://eprints-phd.biblio.unitn.it/1070/1/PhD-Thesis_vhthanh.pdf.

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MOSCA, ETTORE. "Membrane systems and stochastic simulation algorithms for the modelling of biological systems." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19296.

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Membrane Computing is a branch of computer science that was born after the introduction of Membrane Systems (or P systems) by a seminal paper by Gh. Paun. Membrane systems are computing devices inspired by the structure and functioning of living cells as well as from the way the cells are organized in tissues and higher order structures. The aim of membrane computing is to abstract computing ideas and models imitating these products of natural evolution. A typical membrane system is composed by a number of regions surrounded by membranes; regions contains multisets of objects (molecules) and r

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Xu, Guanglei. "Adiabatic processes, noise, and stochastic algorithms for quantum computing and quantum simulation." Thesis, University of Strathclyde, 2018. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=30919.

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Rapid developments in experiments provide promising platforms for realising quantum computation and quantum simulation. This, in turn, opens new possibilities for developing useful quantum algorithms and explaining complex many-body physics. The advantages of quantum computation have been demonstrated in a small range of subjects, but the potential applications of quantum algorithms for solving complex classical problems are still under investigation. Deeper understanding of complex many-body systems can lead to realising quantum simulation to study systems which are inaccessible by other mean

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Park, Chuljin. "Discrete optimization via simulation with stochastic constraints." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49088.

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In this thesis, we first develop a new method called penalty function with memory (PFM). PFM consists of a penalty parameter and a measure of constraint violation and it converts a discrete optimization via simulation (DOvS) problem with stochastic constraints into a series of DOvS problems without stochastic constraints. PFM determines a penalty of a visited solution based on past results of feasibility checks on the solution. Specifically, assuming a minimization problem, a penalty parameter of PFM, namely the penalty sequence, diverges to infinity for an infeasible solution but converges to

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Yarmolskyy, Oleksandr. "Využití distribuovaných a stochastických algoritmů v síti." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-370918.

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This thesis deals with the distributed and stochastic algorithms including testing their convergence in networks. The theoretical part briefly describes above mentioned algorithms, including their division, problems, advantages and disadvantages. Furthermore, two distributed algorithms and two stochastic algorithms are chosen. The practical part is done by comparing the speed of convergence on various network topologies in Matlab.

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Zhang, Chao Ph D. Massachusetts Institute of Technology. "Computationally efficient offline demand calibration algorithms for large-scale stochastic traffic simulation models." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120639.

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Thesis: Ph. D. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 168-181).<br>This thesis introduces computationally efficient, robust, and scalable calibration algorithms for large-scale stochastic transportation simulators. Unlike a traditional "black-box" calibration algorithm, a macroscopic analytical network model is embedded through a metamodel simulation-based optimization (SO) framework. The computational efficiency is achieved through

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Chen, Si. "Design of Energy Storage Controls Using Genetic Algorithms for Stochastic Problems." UKnowledge, 2015. http://uknowledge.uky.edu/ece_etds/80.

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A successful power system in military applications (warship, aircraft, armored vehicle etc.) must operate acceptably under a wide range of conditions involving different loading configurations; it must maintain war fighting ability and recover quickly and stably after being damaged. The introduction of energy storage for the power system of an electric warship integrated engineering plant (IEP) may increase the availability and survivability of the electrical power under these conditions. Herein, the problem of energy storage control is addressed in terms of maximizing the average performance.

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Livres sur le sujet "Stochastic simulation algorithms"

Asmussen, Søren, and Peter W. Glynn. Stochastic Simulation: Algorithms and Analysis. Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-69033-9.

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Asmussen, Søren. Stochastic simulation: Algorithms and analysis. Springer, 2011.

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Öttinger, Hans Christian. Stochastic processes in polymeric fluids: Tools and examples for developing simulation algorithms. Springer, 1996.

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Chang, Hyeong Soo. Simulation-Based Algorithms for Markov Decision Processes. 2nd ed. Springer London, 2013.

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Judd, Kenneth L. One-node quadrature beats monte carlo: A generalized stochastic simulation algorithm. National Bureau of Economic Research, 2011.

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Shi, Yixi. Rare Events in Stochastic Systems: Modeling, Simulation Design and Algorithm Analysis. [publisher not identified], 2013.

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Dieter, Fiems, Vincent Jean-Marc, and SpringerLink (Online service), eds. Analytical and Stochastic Modeling Techniques and Applications: 19th International Conference, ASMTA 2012, Grenoble, France, June 4-6, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Glynn, Peter W., and Søren Asmussen. Stochastic Simulation: Algorithms and Analysis. Springer London, Limited, 2007.

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Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability). Springer, 2007.

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Stochastic Processes in Polymeric Fluids: Tools and Examples for Developing Simulation Algorithms. Springer, 1996.

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Chapitres de livres sur le sujet "Stochastic simulation algorithms"

Kashtanov, Y. N., and I. N. Kuchkova. "Monte Carlo Algorithms For Neumann Boundary Value Problem Using Fredholm Representation." In Advances in Stochastic Simulation Methods. Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1318-5_2.

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Behnke, Henning, Michael Kolonko, Ulrich Mertins, and Stefan Schnitter. "Optimization and Simulation: Sequential Packing of Flexible Objects Using Evolutionary Algorithms." In Stochastic Algorithms: Foundations and Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45322-9_10.

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van den Akker, Marjan, Kevin van Blokland, and Han Hoogeveen. "Finding Robust Solutions for the Stochastic Job Shop Scheduling Problem by Including Simulation in Local Search." In Experimental Algorithms. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38527-8_35.

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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Conclusion and Further Research Directions." In Sine Cosine Algorithm for Optimization. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_6.

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AbstractThe increasing complexity of real-world optimization problems demands fast, robust, and efficient meta-heuristic algorithms. The popularity of these intelligent techniques is gaining popularity day by day among researchers from various disciplines of science and engineering. The sine cosine algorithm is a simple population-based stochastic approach for handling different optimization problems. In this work, we have discussed the basic sine cosine algorithm for continuous optimization problems, the multi-objective sine cosine algorithm for handling multi-objective optimization problems,

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Boukhanovsky, Alexander V., and Sergey V. Ivanov. "Stochastic Simulation of Inhomogeneous Metocean Fields. Part III: High-Performance Parallel Algorithms." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44862-4_26.

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Jiménez-Pastor, A., K. G. Larsen, M. Tribastone, and M. Tschaikowski. "Forward and Backward Constrained Bisimulations for Quantum Circuits." In Tools and Algorithms for the Construction and Analysis of Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57249-4_17.

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AbstractEfficient methods for the simulation of quantum circuits on classic computers are crucial for their analysis due to the exponential growth of the problem size with the number of qubits. Here we study lumping methods based on bisimulation, an established class of techniques that has been proven successful for (classic) stochastic and deterministic systems such as Markov chains and ordinary differential equations. Forward constrained bisimulation yields a lower-dimensional model which exactly preserves quantum measurements projected on a linear subspace of interest. Backward constrained

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Budde, Carlos E., and Arnd Hartmanns. "Replicating $$\textsc {Restart}$$ with Prolonged Retrials: An Experimental Report." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72013-1_21.

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AbstractStatistical model checking uses Monte Carlo simulation to analyse stochastic formal models. It avoids state space explosion, but requires rare event simulation techniques to efficiently estimate very low probabilities. One such technique is $$\textsc {Restart}$$ R E S T A R T . Villén-Altamirano recently showed—by way of a theoretical study and ad-hoc implementation—that a generalisation of $$\textsc {Restart}$$ R E S T A R T to prolonged retrials offers improved performance. In this paper, we demonstrate our independent replication of the original experimental results. We implemented

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Johnson, Erik A., Lawrence A. Bergman, David E. Goldberg, and Shirley J. Dyke. "Monte Carlo Simulation of Dynamical Systems of Engineering Interest in a Massively Parallel Computing Environment: an Application of Genetic Algorithms." In IUTAM Symposium on Advances in Nonlinear Stochastic Mechanics. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0321-0_21.

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Qureshi, Sumaira Ejaz, and Roussos Dimitrakopoulos. "Comparison of Stochastic Simulation Algorithms in Mapping Spaces of Uncertainty of Non-linear Transfer Functions." In Geostatistics Banff 2004. Springer Netherlands, 2005. http://dx.doi.org/10.1007/978-1-4020-3610-1_100.

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López-Oriona, Ángel, José A. Vilar, and Pierpaolo D’Urso. "Unsupervised Classification of Categorical Time Series Through Innovative Distances." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_26.

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AbstractIn this paper, two novel distances for nominal time series are introduced. Both of them are based on features describing the serial dependence patterns between each pair of categories. The first dissimilarity employs the so-called association measures, whereas the second computes correlation quantities between indicator processes whose uniqueness is guaranteed from standard stationary conditions. The metrics are used to construct crisp algorithms for clustering categorical series. The approaches are able to group series generated from similar underlying stochastic processes, achieve ac

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Actes de conférences sur le sujet "Stochastic simulation algorithms"

Arslan, Nazlican, Oscar Dowson, and David P. Morton. "An SDDP Algorithm for Multistage Stochastic Programs with Decision-Dependent Uncertainty." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838765.

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Nesic, Srdjan, Ying Xiao, and B. F. M. Pots. "A Quasi 2-D Localized Corrosion Model." In CORROSION 2004. NACE International, 2004. https://doi.org/10.5006/c2004-04628.

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Abstract In 1996 Pots has written a two-dimensional (2-D) stochastic algorithm to simulate the morphology of localized attack. The rule based algorithm operates on the assumption that the morphology of corrosion attack depends on the balance of two processes: corrosion (leading to metal loss) and precipitation (leading to metal protection). The rules of the original algorithm were modified to enable simulation of a broader variety of localized corrosion morphologies found in practice. The algorithm, which uses scaling tendency as the only input parameter, was connected with the mechanistic mod

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Vel�zquez, Josu� J. Herrera, Erik L. Pi��n Hern�ndez, Luis A. Vega, Dana E. Carrillo Espinoza, J. Rafael Alc�ntara Avila, and Juli�n Cabrera Ruiz. "Comparative and Statistical Study on Aspen Plus Interfaces Used for Stochastic Optimization." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.102858.

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New research on complex intensified distillation schemes has popularized the use of several commercial process simulation software. The interfaces between process simulation and optimization-oriented software have allowed the use of rigorous and robust models. This type of optimization is mentioned in the literature as "Black Box Optimization", since successive evaluations exploits the information from the simulator without altering the model that represents the given process. Among process simulation software, Aspen Plus� has become popular due to their rigorous calculations, model customizat

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Mohamed, Lina, Michael A. Christie, and Vasily Demyanov. "Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification." In SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, 2009. http://dx.doi.org/10.2118/119139-ms.

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Amini, Sasan, and Inneke Van Nieuwenhuyse. "A TUTORIAL ON KRIGING-BASED STOCHASTIC SIMULATION OPTIMIZATION." In 12th Simulation Workshop. The Operational Research Society, 2025. https://doi.org/10.36819/sw25.003.

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This tutorial focuses on kriging-based simulation optimization, emphasizing the importance of data efficiency in optimization problems involving expensive simulation models. It discusses how kriging models contribute to developing algorithms that minimize the number of required simulations, particularly in the presence of noisy evaluations. The tutorial compares the performance of kriging-based algorithms against traditional polynomial-based optimization methods using an illustrative example. Additionally, it discusses key extensions of kriging-based algorithms, including multi-objective and c

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Hashemi, Fatemeh Sadat, and Raghu Pasupathy. "Averaging and derivative estimation within Stochastic Approximation algorithms." In 2012 Winter Simulation Conference - (WSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/wsc.2012.6465142.

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Ramaswamy, Rajesh, Ivo F. Sbalzarini, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Fast Exact Stochastic Simulation Algorithms Using Partial Propensities." In ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics 2010. AIP, 2010. http://dx.doi.org/10.1063/1.3497968.

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Köster, Till, and Adelinde M. Uhrmacher. "Handling Dynamic Sets of Reactions in Stochastic Simulation Algorithms." In SIGSIM-PADS '18: SIGSIM Principles of Advanced Discrete Simulation. ACM, 2018. http://dx.doi.org/10.1145/3200921.3200943.

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"Verification of Sensing Drone Stochastic Control Theory Algorithms." In the 26th International Conference on Harbor, Maritime and Multimodal Logistic Modeling & Simulation. CAL-TEK srl, 2024. http://dx.doi.org/10.46354/i3m.2024.hms.002.

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Luboschik, Martin, Stefan Rybacki, Roland Ewald, Benjamin Schwarze, Heidrun Schumann, and Adelinde M. Uhrmacher. "Interactive visual exploration of simulator accuracy: A case study for stochastic simulation algorithms." In 2012 Winter Simulation Conference - (WSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/wsc.2012.6465190.

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Rapports d'organisations sur le sujet "Stochastic simulation algorithms"

Bhatnagar, Shalabh, Michael C. Fu, Steven I. Marcus, and Shashank Bhatnagar. Randomized Difference Two-Timescale Simultaneous Perturbation Stochastic Approximation Algorithms for Simulation Optimization of Hidden Markov Models. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada637176.

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Xiu, Dongbin. Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1258292.

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Xiu, Dongbin. Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1345533.

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Judd, Kenneth, Lilia Maliar, and Serguei Maliar. One-node Quadrature Beats Monte Carlo: A Generalized Stochastic Simulation Algorithm. National Bureau of Economic Research, 2011. http://dx.doi.org/10.3386/w16708.

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Vollenkemper, Lukas, Marvin Mönikes, Florian Wortmann, et al. Humanzentrierte Produktionsplanung mit KI. Kompetenzzentrum Arbeitswelt.Plus, 2023. http://dx.doi.org/10.55594/uxit4205.

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Im Rahmen dieses Working Papers wird ein KI-gestütztes Assistenzsystem vorgestellt, welches dem Anwendungspartner Bette GmbH eine humanzentrierte Produktionsplanung ermöglicht. Dazu werden zunächst die Besonderheiten vorgestellt, welche die Produktionsplanung bei Bette herausfordernd machen. Dazu gehören insbesondere eine hohe Variantenvielfalt bei gleichzeitig hohen Qualitätsanforderungen. Außerdem führen stochastische Ereignisse wie Nacharbeit an einzelnen Produkten zu unerwarteten Mehraufwänden. In einer ersten Befragung wurden Belastungsfaktoren und die Einstellung der Beschäftigten gegenü

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