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Статті в журналах з теми "Mathematical optimization"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 30 липня 2024

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1

Kulcsár, T., and I. Timár. "Mathematical optimization and engineering applications." Mathematical Modeling and Computing 3, no. 1 (July 1, 2016): 59–78. http://dx.doi.org/10.23939/mmc2016.01.059.

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2

Bhardwaj, Suyash, Seema Kashyap, and Anju Shukla. "A Novel Approach For Optimization In Mathematical Calculations Using Vedic Mathematics Techniques." MATHEMATICAL JOURNAL OF INTERDISCIPLINARY SCIENCES 1, no. 1 (July 2, 2012): 23–34. http://dx.doi.org/10.15415/mjis.2012.11002.

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3

Chawla, Dr Meenu. "Mathematical optimization techniques." Pharma Innovation 8, no. 2 (January 1, 2019): 888–92. http://dx.doi.org/10.22271/tpi.2019.v8.i2n.25454.

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4

Suhl, Uwe H. "MOPS — Mathematical optimization system." European Journal of Operational Research 72, no. 2 (January 1994): 312–22. http://dx.doi.org/10.1016/0377-2217(94)90312-3.

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5

Blaydа, I. A. "OPTIMIZATION OF THE COAL BACTERIAL DESULFURIZATION USING MATHEMATICAL METHODS." Biotechnologia Acta 11, no. 6 (December 2018): 55–66. http://dx.doi.org/10.15407/biotech11.06.055.

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6

Requelme Ibáñez, Rosa María, Carlos Abel Reyes Alvarado, and Jorge Luis Lozano Cervera. "Mathematical optimization for economic agents." Revista Ciencia y Tecnología 17, no. 3 (September 9, 2021): 81–89. http://dx.doi.org/10.17268/rev.cyt.2021.03.07.

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7

Sezer, Ali Devin, and Gerhard-Wilhelm Weber. "Optimization Methods in Mathematical Finance." Optimization 62, no. 11 (November 2013): 1399–402. http://dx.doi.org/10.1080/02331934.2013.863528.

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8

García, J. M., C. A. Acosta, and M. J. Mesa. "Genetic algorithms for mathematical optimization." Journal of Physics: Conference Series 1448 (January 2020): 012020. http://dx.doi.org/10.1088/1742-6596/1448/1/012020.

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9

Gorissen, Bram L., Jan Unkelbach, and Thomas R. Bortfeld. "Mathematical Optimization of Treatment Schedules." International Journal of Radiation Oncology*Biology*Physics 96, no. 1 (September 2016): 6–8. http://dx.doi.org/10.1016/j.ijrobp.2016.04.012.

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10

Feichtinger, Gustav. "Mathematical Optimization and Economic Analysis." European Journal of Operational Research 221, no. 1 (August 2012): 273–74. http://dx.doi.org/10.1016/j.ejor.2012.03.018.

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11

Carrizosa, Emilio, and Dolores Romero Morales. "Supervised classification and mathematical optimization." Computers & Operations Research 40, no. 1 (January 2013): 150–65. http://dx.doi.org/10.1016/j.cor.2012.05.015.

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12

Freeman, T. L., and Melvyn W. Jeter. "Mathematical Programming: An Introduction to Optimization." Mathematical Gazette 71, no. 458 (December 1987): 350. http://dx.doi.org/10.2307/3617112.

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13

Gozuyilmaz, Seyma, and O. Erhun Kundakcioglu. "Mathematical optimization for time series decomposition." OR Spectrum 43, no. 3 (June 8, 2021): 733–58. http://dx.doi.org/10.1007/s00291-021-00637-w.

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14

Lucatero, Carlos Rodríguez, Marcelo Olivera Villaroel, and Paola Ovando. "A Mathematical Model for Agroforestry Optimization." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 17 (March 2, 2022): 108–22. http://dx.doi.org/10.37394/23203.2022.17.13.

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Анотація:

In the present article, we will describe some extensions of an agroforestry model that has been proposed and computationally implemented in [7]. Our generalizations consist of the inclusion of two additional species of tree, one culture, and a declaration of regeneration tours as variables definable by us as a parameter and the weight allocation by rentability of the treeless soil utilization as well as an exhaustive exploration of the different soil utilization scenarios in order to obtain the one who gives the best economic performance.

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15

McMullen, P. "MATHEMATICAL PROGRAMMING An Introduction to Optimization." Bulletin of the London Mathematical Society 19, no. 3 (May 1987): 290–91. http://dx.doi.org/10.1112/blms/19.3.290.

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16

Naidu, N. V. R. "Mathematical model for quality cost optimization." Robotics and Computer-Integrated Manufacturing 24, no. 6 (December 2008): 811–15. http://dx.doi.org/10.1016/j.rcim.2008.03.018.

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17

Zomorrodi, Ali R., Patrick F. Suthers, Sridhar Ranganathan, and Costas D. Maranas. "Mathematical optimization applications in metabolic networks." Metabolic Engineering 14, no. 6 (November 2012): 672–86. http://dx.doi.org/10.1016/j.ymben.2012.09.005.

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18

Rakesh, Vineet, and Ashim Datta. "Microwave puffing: mathematical modeling and optimization." Procedia Food Science 1 (2011): 762–69. http://dx.doi.org/10.1016/j.profoo.2011.09.115.

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19

Hales, Roland Oliver, and Sergio García. "Congress seat allocation using mathematical optimization." TOP 27, no. 3 (April 29, 2019): 426–55. http://dx.doi.org/10.1007/s11750-019-00515-3.

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20

Liou, Y. C., X. Q. Yang, and J. C. Yao. "Mathematical Programs with Vector Optimization Constraints." Journal of Optimization Theory and Applications 126, no. 2 (August 2005): 345–55. http://dx.doi.org/10.1007/s10957-005-4720-4.

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21

Shah, Nita, and Poonam Mishra. "Oil production optimization: a mathematical model." Journal of Petroleum Exploration and Production Technology 3, no. 1 (November 2, 2012): 37–42. http://dx.doi.org/10.1007/s13202-012-0040-z.

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22

Billionnet, Alain. "Mathematical optimization ideas for biodiversity conservation." European Journal of Operational Research 231, no. 3 (December 2013): 514–34. http://dx.doi.org/10.1016/j.ejor.2013.03.025.

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23

Venkayya, V. B. "Mathematical optimization in multi-disciplinary design." Mathematical and Computer Modelling 14 (1990): 29–36. http://dx.doi.org/10.1016/0895-7177(90)90144-c.

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24

Russenschuck, S., and T. Tortschanoff. "Mathematical optimization of superconducting accelerator magnets." IEEE Transactions on Magnetics 30, no. 5 (September 1994): 3419–22. http://dx.doi.org/10.1109/20.312673.

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25

Sakhapov, R. L., R. V. Nikolaeva, M. H. Gatiyatullin, and M. M. Makhmutov. "Mathematical model of highways network optimization." Journal of Physics: Conference Series 936 (December 2017): 012032. http://dx.doi.org/10.1088/1742-6596/936/1/012032.

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26

Şen, Alper, Kamyar Kargar, Esma Akgün, and Mustafa Ç. Pınar. "Codon optimization: a mathematical programing approach." Bioinformatics 36, no. 13 (April 20, 2020): 4012–20. http://dx.doi.org/10.1093/bioinformatics/btaa248.

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Анотація:

Abstract Motivation Synthesizing proteins in heterologous hosts is an important tool in biotechnology. However, the genetic code is degenerate and the codon usage is biased in many organisms. Synonymous codon changes that are customized for each host organism may have a significant effect on the level of protein expression. This effect can be measured by using metrics, such as codon adaptation index, codon pair bias, relative codon bias and relative codon pair bias. Codon optimization is designing codons that improve one or more of these objectives. Currently available algorithms and software solutions either rely on heuristics without providing optimality guarantees or are very rigid in modeling different objective functions and restrictions. Results We develop an effective mixed integer linear programing (MILP) formulation, which considers multiple objectives. Our numerical study shows that this formulation can be effectively used to generate (Pareto) optimal codon designs even for very long amino acid sequences using a standard commercial solver. We also show that one can obtain designs in the efficient frontier in reasonable solution times and incorporate other complex objectives, such as mRNA secondary structures in codon design using MILP formulations. Availability and implementation http://alpersen.bilkent.edu.tr/codonoptimization/CodonOptimization.zip.

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27

Heyer, Laurie J. "A Mathematical Optimization Problem in Bioinformatics." PRIMUS 18, no. 1 (January 17, 2008): 101–18. http://dx.doi.org/10.1080/10511970701744992.

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28

Zowe, Jochem, Michal Kočvara, and Martin P. Bendsøe. "Free material optimization via mathematical programming." Mathematical Programming 79, no. 1-3 (October 1997): 445–66. http://dx.doi.org/10.1007/bf02614328.

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29

Norkin, B. V. "Mathematical models for insurance business optimization." Cybernetics and Systems Analysis 47, no. 1 (January 2011): 117–33. http://dx.doi.org/10.1007/s10559-011-9295-5.

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30

Ahmad Mala, Firdous. "Mathematical Analysis and Optimization for Economists." Technometrics 65, no. 2 (April 3, 2023): 300–301. http://dx.doi.org/10.1080/00401706.2023.2201131.

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31

Petridis, Konstantinos, Garyfallos Arabatzis, and Angelo Sifaleras. "Mathematical optimization models for fuelwood production." Annals of Operations Research 294, no. 1-2 (October 31, 2017): 59–74. http://dx.doi.org/10.1007/s10479-017-2697-7.

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32

Xie, Hua Long, Hui Min Guo, Qing Bao Wang, and Yong Xian Liu. "The Spindle Structural Optimization Design of HTC3250µn NC Machine Tool Based on ANSYS." Advanced Materials Research 457-458 (January 2012): 60–64. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.60.

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Анотація:

The optimization of spindle has important significance. The optimization method based on ANSYS is introduced and spindle mathematical mode of HTC3250µn NC machine tool is given. By scanning of design variables, the main optimized design variables are determined. The single objective and multi-objective optimizations are done. In the end, the main size comparison of spindle before and after optimization is given.

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33

Ulitinas, Tomas, and Stanislovas Kalanta. "OPTIMIZATION OF TRUSS HEIGHT." Mokslas - Lietuvos ateitis 2, no. 6 (December 31, 2010): 56–60. http://dx.doi.org/10.3846/mla.2010.112.

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The article analyzes the task in truss height and in the optimization of the cross-sections of their elements. Element cross-sections are designed of steel profiles considering requirements for strength, stability and rigidity. A mathematical model is formulated as a nonlinear mathematical programming problem. It is solved as an iterative process, using mathematical software package “MATLAB” routine “fmincon”. The ratio of buckling is corrected in the each iteration. Optimization results are compared with those obtained applying software package “Robot Millennium”.

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34

Monabbati, S. E., and H. Torabi. "Mathematical modeling of finite topologies." Carpathian Mathematical Publications 12, no. 2 (December 29, 2020): 434–42. http://dx.doi.org/10.15330/cmp.12.2.434-442.

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Integer programming is a tool for solving some combinatorial optimization problems. In this paper, we deal with combinatorial optimization problems on finite topologies. We use the binary representation of the sets to characterize finite topologies as the solutions of a Boolean quadratic system. This system is used as a basic model for formulating other types of topologies (e.g. door topology and $T_0$-topology) and some combinatorial optimization problems on finite topologies. As an example of the proposed model, we found that the smallest number $m(k)$ for which the topology exists on an $m(k)$-elements set containing exactly $k$ open sets, for $k = 8$ and $k = 15$ is $3$ and $5$, respectively.

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35

Zhong, Mei Peng. "Parameter Optimization of Compressor Based on an Ant Colony Optimization." Applied Mechanics and Materials 201-202 (October 2012): 916–19. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.916.

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Анотація:

A mathematical model of operation on air compressors is set up in order to improve the efficiency of air compressors. Parameter of Compressor is optimized by an Ant Colony Optimization (ACO) Particle approach. Volume and its weight of the new compressor are little, and its efficiency is high. An Ant Colony Optimization embed BLDCM module which optimizating the air compressor was put forward. Optimizated target of an Ant Colony Optimization is the efficiency of BLDCM. Optimizated variables are the diameter of low pressure cylinder, the diameter of high pressure cylinder, the journey of low pressure piston, the journey of high pressure piston and the rotate speed of BLDCM. Simulated result shows that the efficiency of BLDCM is more than that before optimizating. The test is done. The result shows that the specifc Power of air compressor is much less than before optimizating on 2.5Mpa. The result also shows that an Ant Colony Optimization which optimizating the air compressor is availability and practicality.

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36

Zhang, Hongxin. "Optimization Strategies for Mathematical Algorithms in Computer Programming." Journal of Big Data and Computing 1, no. 1 (March 2023): 16–19. http://dx.doi.org/10.62517/jbdc.202301104.

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Анотація:

Computer programming is an important part of computing information technology, mathematical operation is one of the main modules of computer programming, through the optimization of mathematical operation to simple computer programming algorithm, can improve the efficiency of computer software. Therefore, in order to improve the efficiency of computer operation, it is particularly important to optimize the mathematical algorithms. Based on this, this paper studies the optimization strategy of mathematical algorithm in computer programming. Firstly, a brief overview of mathematical algorithm and computer programming is made, secondly, the role of mathematical algorithm in computer programming is analyzed, and finally, the optimization strategy of mathematical algorithm in computer programming is given.

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37

Montoro, Johnny Moisés Valverde, Milton Milciades Cortez Gutiérrez, and Hernán Oscar Cortez Gutiérrez. "Optimization of the mathematical programming and applications." South Florida Journal of Development 2, no. 5 (December 9, 2021): 7902–11. http://dx.doi.org/10.46932/sfjdv2n5-114.

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Анотація:

The present investigation responds to the need to solve optimization problems with optimality conditions. The KKT conditions are considered for multiobjective optimization problems with interval-valued objective functions.

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38

Journal, IJSREM. "Reviewing the Role of Mathematical Optimization in Operations Research: Algorithms, Applications, and Challenges." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (February 8, 2024): 1–11. http://dx.doi.org/10.55041/ijsrem28578.

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Анотація:

This review paper examines the pivotal role of mathematical optimization in operations research, focusing on its algorithms, applications, and challenges. Mathematical optimization, a cornerstone of operations research, offers powerful tools for addressing complex decision-making problems. We discuss a variety of optimization algorithms, from classical methods like linear programming to modern metaheuristic techniques such as genetic algorithms. Through specific case studies, we highlight the diverse applications of mathematical optimization in industries such as logistics, finance, and manufacturing. Additionally, analyze challenges like computational complexity and scalability issues, providing insights into the practical implementation of optimization solutions in real world scenarios. Keywords: Mathematical Optimization, Operations Research, Algorithms, Decision-Making Problems, Challenges, Applications

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39

Kutova, О. V., and R. V. Sahaidak-Nikitiuk. "Optimization methods for multi-criteria decisions in pharmacy." Social Pharmacy in Health Care 9, no. 4 (November 17, 2023): 3–10. http://dx.doi.org/10.24959/sphhcj.23.302.

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Анотація:

Optimization methods for multi-criteria decisions in pharmacy In pharmaceutical technological research, the determination of the quantitative composition of granules is considered as a task of multi-criteria selection. Today, to solve this problem, the regression analysis and multi-criteria optimization methods are widely used; they are based on mathematical models obtained for the object under study. Aim. To identify a decision-making method in a multi-criteria space that is effective for use in pharmaceutical technology research with quantitative factors. Materials and methods. The study uses tools of the popular computer mathematics system Mathcad (MathSoft Ins., USA) to automate the solution of mathematical problems. To automatically search for the type and coefficients of regression equations, the MS Excel application was used, namely: the data analysis package (regression analysis). The MS Word processor was used to edit the code. Results. A variety of approaches to the formalization of the multi-criteria optimization task have been studied. The optimal quantitative content of excipients when developing the granule technology has been found using two different optimization criteria, which are formed according to different methodical approaches. The method proposed does not provide for the mandatory introduction of gradation of individual criteria or their weighting factors. Conclusions. As a result of the comparison of multi-criteria optimization methods, the effectiveness of the decision-making method in the multi-criteria space has been shown; it synthesizes a mathematical procedure related to the vector of criteria and is based on determining the ideal point and introducing the concept of a norm into the space of functionals; it has not been mathematically proven, but it is practically useful decision-making algorithm compared to the mathematical method of convolution of criteria. The optimization method proposed has advantages that are manifested in the possibility of using a relatively simple mathematical apparatus and simplified logic of obtaining a solution.

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40

Carrizosa, Emilio, Cristina Molero-Río, and Dolores Romero Morales. "Mathematical optimization in classification and regression trees." TOP 29, no. 1 (March 17, 2021): 5–33. http://dx.doi.org/10.1007/s11750-021-00594-1.

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Анотація:

AbstractClassification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization and the Mixed-Integer Linear Optimization paradigms to develop novel formulations in this research area. We compare those in terms of the nature of the decision variables and the constraints required, as well as the optimization algorithms proposed. We illustrate how these powerful formulations enhance the flexibility of tree models, being better suited to incorporate desirable properties such as cost-sensitivity, explainability, and fairness, and to deal with complex data, such as functional data.

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41

Lebedev, Vladimir, and Ekaterina Yushkova. "Mathematical model for optimization of heat exchange systems." E3S Web of Conferences 164 (2020): 02011. http://dx.doi.org/10.1051/e3sconf/202016402011.

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Анотація:

The article is devoted to the issue of thermodynamic optimization of heat transfer systems. Optimization is carried out by an exergy pinch method. This method includes the advantages of exergy analysis and pinch method. Exergy analysis takes into account the quantitative and qualitative characteristics of thermal processes, the pinch method allows structural and parametric optimization of heat transfer systems. The article presents a mathematical model for optimization by exergy pinch analysis. This model allows automated system optimization. Exergy pinch analysis allows more efficient use of energy and resources at the enterprise, which is relevant today.

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42

ZAVGORODNIY, OLEXIY, DMYTRO LEVKIN, YANA KOTKO, and OLEXANDER MAKAROV. "RESEARCH OF COMPUTATIONAL MATHEMATICAL MODELS FOR TECHNICAL SYSTEMS." Herald of Khmelnytskyi National University. Technical sciences 319, no. 2 (April 27, 2023): 108–12. http://dx.doi.org/10.31891/2307-5732-2023-319-1-108-112.

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Анотація:

In the theory of analysis and synthesis of technical systems, mathematical modelling and optimization of multilayer systems containing sources of physical fields occupy an important place. This is due to the fact that their state is described by means of boundary value problems with multidimensional differential equations. To solve the boundary value problems and implement the process of optimizing the technical parameters of the modelled systems, it is necessary to conduct interdisciplinary studies of computational and applied optimization mathematical models. Fulfilment of the conditions for the existence of a single solution to boundary value problems by default is possible only when the object of study is a single-layer material under the action of load sources. If it is necessary to calculate and optimize the technical parameters of a multilayer material subjected to load sources, then it is impossible to immediately guarantee the correctness of the calculated and applied optimization mathematical models, since it is necessary to obtain the conditions for the existence and uniqueness of solutions to boundary value problems with systems of differential equations. Maximizing the technical parameters of load sources and averaging the characteristics of material layers will lead to approximate values of the objective function and technical parameters of the modelled system, which leads to irrational consumption of energy and heat resources and uncontrolled losses, and useless losses of the test material in the technological process. The article presents the conditions for the correctness of multipoint boundary value problems with multidimensional differential equations describing the state of a multilayer material under thermal action. It is advisable to use these studies to substantiate the correctness of other technical and biotechnological systems, which will increase the accuracy of the implementation of applied optimization problems of economic and mathematical modelling.

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43

Saraf, Atul R., Nitin D. Misal, and M. Sadaiah. "Mathematical Modelling and Optimization of Photochemical Machining." Advanced Materials Research 548 (July 2012): 617–22. http://dx.doi.org/10.4028/www.scientific.net/amr.548.617.

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Анотація:

Photochemical Machining is a novel machining process capable of processing wide range of hard-to-machine materials. This research addresses modelling and optimization of the process parameters for this machining technique. To model the process a set of experimental data has been used to evaluate the effects of various parameter settings in machining of SS316L. The process variables considered here include etchant temperature, time and concentration. Undercut, as one of the most important output characteristics, has been evaluated based on different parameter settings. The full factorial method and regression modelling are used in order to establish the relationships between input and output parameters. The effect of control parameters on undercut was analysed using Analysis of Variance (ANOVA) technique and their optimal conditions were evaluated. It was found that etchant temperature and etching time are the most significant factors for undercut.

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44

Levkin, D. "Mathematical modeling and optimization of multilayer systems." Energy and automation 2019, no. 1 (May 6, 2019): 45–56. http://dx.doi.org/10.31548/energiya2019.01.045.

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45

Смирнов, Николай Васильевич, Александр Николаевич Кириллов, Nikolay Smirnov, and Alexandr Kirillov. "MATHEMATICAL MODEL OF WASTEWATER TREATMENT PROCESS OPTIMIZATION." Proceedings of the Karelian Research Centre of the Russian Academy of Sciences, no. 08 (September 1, 2016): 55. http://dx.doi.org/10.17076/mat350.

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46

Pichler, Alois, and Alexander Shapiro. "Mathematical Foundations of Distributionally Robust Multistage Optimization." SIAM Journal on Optimization 31, no. 4 (January 2021): 3044–67. http://dx.doi.org/10.1137/21m1390517.

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47

Kasatkina, E. V., and D. D. Vavilova. "Mathematical modeling and optimization of traffic flows." Journal of Physics: Conference Series 2134, no. 1 (December 1, 2021): 012002. http://dx.doi.org/10.1088/1742-6596/2134/1/012002.

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Анотація:

Abstract The article presents a mathematical model for optimizing traffic flows in an urban environment based on a stochastic approach. It allows to optimize traffic flows using a genetic algorithm by changing the phases of traffic lights operation. An exponential law of distribution of the generation of cars at the input points of the transport network has been established. The relationship between the intensity of servicing the traffic flow and the time of the green signal of the traffic light is revealed. Practical calculations have confirmed the applicability of the optimization model in traffic management.

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48

Sokolov, Nikita Yu. "Mathematical Modeling and Optimization Heat Pipe Systems." Journal of Siberian Federal University. Engineering & Technologies 14, no. 7 (November 2021): 860–79. http://dx.doi.org/10.17516/1999-494x-0352.

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The results of optimization of the supporting structure of a block of onboard electronic equipment with a built-in single flat heat pipe in the supporting structure with an integrated heat pipe system are presented, using the example of solving a model problem with a single heat source. The comparison was carried out at an equal temperature, occupied volume and for a certain maximum temperature of electrical radio products. The results of computational modeling are presented, demonstrating a comparison of the characteristics of a single flat heat pipe with a system of sequentially located flat heat pipes. Ultimately, the studies carried out have determined the limiting values of the removed thermal power of a single heat pipe, two-level and three-level heat pipe systems with different heat carriers. The versatility of the mathematical model, supplemented by the optimization method, has been confirmed

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49

Bakiri, Zahir. "A mathematical optimization model for secondary settler." Journal of Physics: Conference Series 2063, no. 1 (November 1, 2021): 012029. http://dx.doi.org/10.1088/1742-6596/2063/1/012029.

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Abstract The study of secondary settler modelling, which aims to establish the main model (one-dimensional-1D model), which is involved in some fundamental processes of the hydrodynamic behaviour of this liquid/solid separation unit and to engender variations of the sludge blanket height as a function of the operating parameters and maintaining of the municipal wastewater treatment plant of Setif. The objective of this research is focused on solid/liquid separation in the secondary settler by attempting a mathematical model that allows us to evaluate the sedimentation velocity as a function of the sludge settleability parameters.

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50

Hou, Maowen, and Weiyun Wang. "Sensor Mathematical Model Data Fusion Biobjective Optimization." Journal of Sensors 2022 (January 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/1612715.

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Sensors are an important tool to quantify the changes and an important part of the information acquisition system; the performance and accuracy of sensors are more strictly desired. In this paper, a highly sensitive fiber optic sensor for measuring temperature and refractive index is prepared by using femtosecond laser micromachining technology and fiber fusion technology. The multimode fiber is first spliced together with single-mode fiber in a positive pair, and then, the multimode fiber is perforated using a femtosecond laser. The incorporation of data model sensors has led to a rapid increase in the development and application of sensors as well. Based on the design concept and technical approach of the wireless sensor network system, a general development plan of the indoor environmental monitoring system is proposed, including the system architecture and functional definition, wireless communication protocols, and design methods of node applications. The sensor has obvious advantages over traditional electrical sensors; the sensor is resistant to electromagnetic interference, electrical insulation, corrosion resistance, low loss, small size, high accuracy, and other advantages. The upper computer program of the indoor environment monitoring system was developed in a Visual Studio development environment using C# language to implement the monitoring, display, and alarm functions of the indoor environment monitoring system network. The sensor-data model interfusion with each other for mutual integration performs the demonstration of the application.

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