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Journal articles on the topic 'Modelling and optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

RAHOU .M, RAHOU M., and SEBAA F. SEBAA .F. "Modelling and Optimization of the Cutting Tool Trajectory." International Journal of Scientific Research 2, no. 11 (2012): 182–84. http://dx.doi.org/10.15373/22778179/nov2013/58.

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2

Klimczyk, Witold Artur, and Zdobyslaw Jan Goraj. "Robust design and optimization of UAV empennage." Aircraft Engineering and Aerospace Technology 89, no. 4 (2017): 609–19. http://dx.doi.org/10.1108/aeat-11-2016-0221.

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Purpose This paper aims to address the issue of designing aerodynamically robust empennage. Aircraft design optimization often narrowed to analysis of cruise conditions does not take into account other flight phases (manoeuvres). These, especially in unmanned air vehicle sector, can be significant part of the whole flight. Empennage is a part of the aircraft, with crucial function for manoeuvres. It is important to consider robustness for highest performance. Design/methodology/approach Methodology for robust wing design is presented. Surrogate modelling using kriging is used to reduce the opt

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3

Bansal, Manoj. "Optimization Modelling for Renewable Energy Resources based Distribution Generation." Revista Gestão Inovação e Tecnologias 11, no. 3 (2021): 1510–19. http://dx.doi.org/10.47059/revistageintec.v11i3.2027.

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4

Maminov, Artem D., and Mikhail A. Posypkin. "Parallel robots modelling and optimization." Journal of Physics: Conference Series 1740 (January 2021): 012067. http://dx.doi.org/10.1088/1742-6596/1740/1/012067.

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5

Porsinger, Tobias, Przemyslaw Janik, Zbigniew Leonowicz, and Radomir Gono. "Modelling and Optimization in Microgrids." Energies 10, no. 4 (2017): 523. http://dx.doi.org/10.3390/en10040523.

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6

Veres, Sandor M. "Exploratory modelling for controller optimization." IFAC Proceedings Volumes 36, no. 16 (2003): 15–20. http://dx.doi.org/10.1016/s1474-6670(17)34731-6.

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7

Gobi, A. F., and W. Pedrycz. "Fuzzy modelling through logic optimization." International Journal of Approximate Reasoning 45, no. 3 (2007): 488–510. http://dx.doi.org/10.1016/j.ijar.2006.06.026.

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8

Spalatelu-Lazar, Mirela, Françoise Léné, and Nicole Turbé. "Modelling and optimization of sails." Computers & Structures 86, no. 13-14 (2008): 1486–93. http://dx.doi.org/10.1016/j.compstruc.2007.05.028.

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9

Mitra, Gautam. "Introduction: Optimization and Risk Modelling." Computational Optimization and Applications 32, no. 1-2 (2005): 5–8. http://dx.doi.org/10.1007/s10589-005-2051-x.

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10

Viswanathan, Shekar, N. V. Ananthanarayanan, and Barry J. Azzopardi. "Venturi Scrubber Modelling and Optimization." Canadian Journal of Chemical Engineering 83, no. 2 (2008): 194–203. http://dx.doi.org/10.1002/cjce.5450830206.

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11

Wang, Likai, Patrick Janssen, Kian Wee Chen, Ziyu Tong, and Guohua Ji. "Subtractive Building Massing for Performance-Based Architectural Design Exploration: A Case Study of Daylighting Optimization." Sustainability 11, no. 24 (2019): 6965. http://dx.doi.org/10.3390/su11246965.

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For sustainable building design, performance-based optimization incorporating parametric modelling and evolutionary optimization can allow architects to leverage building massing design to improve energy performance. However, two key challenges make such applications of performance-based optimization difficult in practice. First, due to the parametric modelling approaches, the topological variability in the building massing variants is often very limited. This, in turn, limits the scope for the optimization process to discover high-performing solutions. Second, for architects, the process of c

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12

Kravchenko, Volodymyr, Sergiy Surkov, and Hussam Ghanem. "Modelling and optimization of seawater desalination process using mechanical steam compression." Odes’kyi Politechnichnyi Universytet. Pratsi, no. 2 (August 20, 2016): 31–35. http://dx.doi.org/10.15276/opu.2.49.2016.08.

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13

Panitz, Hans-Jürgen. "Optimization of long-term air quality modelling applying cluster analysis techniques." Meteorologische Zeitschrift 19, no. 1 (2010): 63–80. http://dx.doi.org/10.1127/0941-2948/2010/0419.

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14

Rahnev, Mariyan. "TRANSPORTATION SYSTEMS MODELLING." Journal Scientific and Applied Research 18, no. 1 (2020): 28–40. http://dx.doi.org/10.46687/jsar.v18i1.277.

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As it is with every activity, the transport network can be organized in many ways by using different technical means and technologies. Taking certain management decisions is connected with the choice of one of the many possible options for realization of the transportation process. This is done with the help of one or a few indicators which serve as a criterion of efficiency, e.g. exploitation costs, profit, safety, time, etc. The research of the transport objects functioning and the optimization of their parameters is a very complicated task in most of the cases. In such cases the solutions o

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15

Ellman, Thomas, John Keane, Mark Schwabacher, and Ke-Thia Yao. "Multilevel modelling for engineering design optimization." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, no. 5 (1997): 357–78. http://dx.doi.org/10.1017/s0890060400003309.

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AbstractPhysical systems can be modelled at many levels of approximation. The right model depends on the problem to be solved. In many cases, a combination of models will be more effective than a single model. Our research investigates this idea in the context of engineering design optimization. We present a family of strategies that use multiple models for unconstrained optimization of engineering designs. The strategies are useful when multiple approximations of an objective function can be implemented by compositional modelling techniques. We show how a compositional modelling library can b

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16

Arkabaev, Nurkasym, Elshan Rahimov, Alisher Abdullaev, Harish Padmanaban, and Vugar Salmanov. "MODELLING AND ANALYSIS OF OPTIMIZATION ALGORITHMS." Jurnal Ilmiah Ilmu Terapan Universitas Jambi 9, no. 1 (2025): 161–77. https://doi.org/10.22437/jiituj.v9i1.38410.

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The purpose of this study was to comprehensively analyze existing optimization algorithms for Machine Learning (ML) models and develop new approaches aimed at improving their performance and efficiency. The study compared traditional and novel machine learning optimization techniques to evaluate their impact on model performance. The main results include a detailed overview of the main optimization methods in ML, including gradient descent, stochastic gradient descent, metaheuristic-based methods, and non-zero methods. Specific cases of using optimization algorithms in ML tasks, such as image

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17

Munsamy, Megashnee, and Arnesh Telukdarie. "Business Process (4IR) Centric Optimization Modelling." Procedia Computer Science 180 (2021): 581–90. http://dx.doi.org/10.1016/j.procs.2021.01.279.

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18

Suidan, Makram T., Joseph R. V. Flora, Pratim Biswas, and Gregory D. Sayles. "Optimization modelling of anaerobic biofilm reactors." Water Science and Technology 30, no. 12 (1994): 347–55. http://dx.doi.org/10.2166/wst.1994.0634.

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A rigorous steady state model of acetate-utilizing methanogenic biofilms is developed accounting for the mass transfer of neutral and ionic species, pH changes within the biofilm, pH-dependent Monod kinetics, chemical equilibrium, electroneutrality, gas production within the biofilm, and the presence of a concentration boundary layer (CBL). In contrast to traditional biofilm models where the pH is assumed to be constant within the biofilm, an increase in pH in acetate-utilizing methanogenic biofilms is predicted. Furthermore, significant differences can exist between the flux predictions using

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19

Scanlan, Jim, Terry Hill, Rob Marsh, Christophe Bru, Martin Dunkley, and Paul Cleevely. "Cost modelling for aircraft design optimization." Journal of Engineering Design 13, no. 3 (2002): 261–69. http://dx.doi.org/10.1080/09544820110108962.

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20

Varbanov, P. S., S. Doyle, and R. Smith. "Modelling and Optimization of Utility Systems." Chemical Engineering Research and Design 82, no. 5 (2004): 561–78. http://dx.doi.org/10.1205/026387604323142603.

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21

Patel, R., and K. Niranjan. "Modelling and optimization of fermentation processes." Food Chemistry 49, no. 3 (1994): 331–32. http://dx.doi.org/10.1016/0308-8146(94)90180-5.

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22

Forrester, Alexander I. J., András Sóbester, and Andy J. Keane. "Multi-fidelity optimization via surrogate modelling." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 463, no. 2088 (2007): 3251–69. http://dx.doi.org/10.1098/rspa.2007.1900.

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This paper demonstrates the application of correlated Gaussian process based approximations to optimization where multiple levels of analysis are available, using an extension to the geostatistical method of co-kriging . An exchange algorithm is used to choose which points of the search space to sample within each level of analysis. The derivation of the co-kriging equations is presented in an intuitive manner, along with a new variance estimator to account for varying degrees of computational ‘noise’ in the multiple levels of analysis. A multi-fidelity wing optimization is used to demonstrate

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23

Guizani, Amir, Moncef Hammadi, Jean-Yves Choley, Thierry Soriano, Mohamed Slim Abbes, and Mohamed Haddar. "Electric vehicle design, modelling and optimization." Mechanics & Industry 17, no. 4 (2016): 405. http://dx.doi.org/10.1051/meca/2015095.

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24

Mabitsela, Lesego, Arnesh Telukdarie, and Megashnee Munsamy. "Modelling for Cleaner Production & Optimization." Procedia Computer Science 217 (2023): 679–88. http://dx.doi.org/10.1016/j.procs.2022.12.264.

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25

Singh, Indu Shekhar, and Sowmyanarayanan Sadagopan. "A support system for optimization modelling." Decision Support Systems 3, no. 2 (1987): 165–78. http://dx.doi.org/10.1016/0167-9236(87)90075-3.

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26

Wang, Cheng-Chi. "Applied Modelling, Numerical Simulation and Optimization." Applied Mathematical Modelling 47 (July 2017): 755. http://dx.doi.org/10.1016/j.apm.2017.05.025.

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27

Teo, Kok Lay, Altannar Chinchuluun, and Cedric Ka-Fai Yiu. "Modelling and Optimization in Engineering Applications." Applied Mathematical Modelling 58 (June 2018): 1–2. http://dx.doi.org/10.1016/j.apm.2018.02.019.

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28

Singh, Ajay. "Optimization modelling for seawater intrusion management." Journal of Hydrology 508 (January 2014): 43–52. http://dx.doi.org/10.1016/j.jhydrol.2013.10.042.

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29

Lasch, Peter, Christof Büskens, and Matthias Knauer. "Modelling and Optimization of Cogeneration Plants." PAMM 5, no. 1 (2005): 683–84. http://dx.doi.org/10.1002/pamm.200510317.

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30

Szakács, Tamás. "Pneumatic Piston Control Modelling and Optimization." Acta Polytechnica Hungarica 20, no. 6 (2023): 249–65. http://dx.doi.org/10.12700/aph.20.6.2023.6.14.

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31

Szakács, Tamás, and Naim Khalifa. "Pneumatic Piston Control Optimization." Journal of Physics: Conference Series 2848, no. 1 (2024): 012002. http://dx.doi.org/10.1088/1742-6596/2848/1/012002.

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Abstract Piston control can be applied to regulate position, speed, acceleration, and force. The primary distinction between pneumatic and hydraulic cylinders lies in the working fluid: hydraulic systems utilize practically incompressible fluid, whereas pneumatic systems use compressible gas. Consequently, hydraulic cylinders offer more precise control over position and speed. In contrast, pneumatic cylinders are more influenced by external loads. However, pneumatic systems are simpler, more cost-effective than hydraulic systems, and more commonly used in laboratories, workshops, and productio

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32

POPA, Catalin, and Ionela GOIA. "THE MODELLING OF CARGO TRANSSHIPMENT OPERATIONS USING THE BUSINESS PROCESS MODELLING TOOLS." Scientific Journal of Silesian University of Technology. Series Transport 124 (September 30, 2024): 157–69. http://dx.doi.org/10.20858/sjsutst.2024.124.11.

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This paperwork explores the transshipment operations at the Port of Constanta, Romania, focusing on the unloading of big bags from barges. Utilizing Business Process Management (BPM) software, the study models the transshipment process to identify optimization opportunities. The investigation reveals challenges such as coordination complexities and potential cargo damage, alongside the benefits of cost-efficiency and flexibility offered by transshipment services. Through literature review and analysis, the study emphasizes the importance of efficient business processes and the role of BPM soft

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33

Sulaiman, Ahmad S. S., Aimrun Wayayok, Samsuzana A. Aziz, Wong Mui Yun, and Guo Leifeng. "Advancements in Crop Water Modelling: Algorithmic Developments and Parameter Optimization Strategies for Sustainable Agriculture: A Review." Basrah Journal of Agricultural Sciences 37, no. 2 (2024): 310–25. https://doi.org/10.37077/25200860.2024.37.2.24.

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This paper presents a review on algorithm development and crop water modelling with a focus on optimizing significant parameters related to crop factors, soil factors, and weather factors. The accurate representation and optimization of these parameters are crucial for reliable predictions and effective decision-making in agricultural practices. The objective of this review is to analyse the existing literature on algorithm development, parameter optimization techniques, and their application in crop water modelling, specifically emphasizing the importance of crop factors, soil factors, and we

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34

Wang, Zheng-Xin, and Ling-Ling Pei. "An Optimized Grey Dynamic Model for Forecasting the Output of High-Tech Industry in China." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/586284.

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The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data andnseries related, abbreviated as GDMC(1,n), performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC(1,n),ninterpolation coefficients (taken as unknown parameters) are introduced into the background values of thenvariables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic ou

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35

Ivanenko, Y., M. Nedic, M. Gustafsson, B. L. G. Jonsson, A. Luger, and S. Nordebo. "Quasi-Herglotz functions and convex optimization." Royal Society Open Science 7, no. 1 (2020): 191541. http://dx.doi.org/10.1098/rsos.191541.

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We introduce the set of quasi-Herglotz functions and demonstrate that it has properties useful in the modelling of non-passive systems. The linear space of quasi-Herglotz functions constitutes a natural extension of the convex cone of Herglotz functions. It consists of differences of Herglotz functions and we show that several of the important properties and modelling perspectives are inherited by the new set of quasi-Herglotz functions. In particular, this applies to their integral representations, the associated integral identities or sum rules (with adequate additional assumptions), their b

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36

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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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

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37

Stanković, Ratko, and Diana Božić. "Applying Simulation Modelling in Quantifying Optimization Results." Tehnički glasnik 15, no. 4 (2021): 518–23. http://dx.doi.org/10.31803/tg-20210326111551.

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Improvements achieved by applying linear programming models in solving optimization problems in logistics cannot always be expressed by physically measurable values (dimensions), but in non-dimensional values. Therefore, it may be difficult to present the actual benefits of the improvements to the stake holders of the system being optimized. In this article, a possibility of applying simulation modelling in quantifying results of optimizing cross dock terminal gates allocation is outlined. Optimal solution is obtained on the linear programming model by using MS Excel spreadsheet optimizer, whi

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38

Morganti, Elisa, Iolanda Fuduli, Andrea Montefusco, Marco Petasecca, and Giorgio U. Pignatel *. "SPICE modelling and design optimization of micropumps." International Journal of Environmental Analytical Chemistry 85, no. 9-11 (2005): 687–98. http://dx.doi.org/10.1080/03067310500153876.

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39

Beeby, S. P., and M. J. Tudor. "Modelling and optimization of micromachined silicon resonators." Journal of Micromechanics and Microengineering 5, no. 2 (1995): 103–5. http://dx.doi.org/10.1088/0960-1317/5/2/011.

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40

Song, Chaolong, Nam-Trung Nguyen, Say-Hwa Tan, and Anand Krishna Asundi. "Modelling and optimization of micro optofluidic lenses." Lab on a Chip 9, no. 9 (2009): 1178. http://dx.doi.org/10.1039/b819158a.

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41

Lee, Cheol W., and Yung C. Shin. "Evolutionary modelling and optimization of grinding processes." International Journal of Production Research 38, no. 12 (2000): 2787–813. http://dx.doi.org/10.1080/002075400411484.

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42

Nikas, G. K., G. Burridge, and R. S. Sayles. "Modelling and optimization of rotary vane seals." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 221, no. 6 (2007): 699–715. http://dx.doi.org/10.1243/13506501jet290.

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43

KARPOUZAS, I., and Y. POULIQUEN. "Modelling and Numerical Optimization of Corneal Rotation." Mathematical Medicine and Biology 8, no. 1 (1991): 73–82. http://dx.doi.org/10.1093/imammb/8.1.73.

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44

Skrzyniowski, A. "Modelling and optimization of rotary parking system." IOP Conference Series: Materials Science and Engineering 148 (September 2016): 012051. http://dx.doi.org/10.1088/1757-899x/148/1/012051.

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45

Alrefaei, Mahmoud H., and Ali Diabat. "Modelling and optimization of outpatient appointment scheduling." RAIRO - Operations Research 49, no. 3 (2015): 435–50. http://dx.doi.org/10.1051/ro/2014041.

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46

Khayet, M., C. Cojocaru, and G. Zakrzewska-Trznadel. "Response surface modelling and optimization in pervaporation." Journal of Membrane Science 321, no. 2 (2008): 272–83. http://dx.doi.org/10.1016/j.memsci.2008.05.002.

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47

Hashimoto, F., and D. B. DeBra. "Modelling and Optimization of Vibratory Finishing Process." CIRP Annals 45, no. 1 (1996): 303–6. http://dx.doi.org/10.1016/s0007-8506(07)63068-6.

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48

Cha, Ji Hwan, and Maxim Finkelstein. "Environmental stress screening modelling, analysis and optimization." Reliability Engineering & System Safety 139 (July 2015): 149–55. http://dx.doi.org/10.1016/j.ress.2015.03.003.

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49

Gomide, F. A. C., W. C. Amaral, J. E. R. Cury, L. Gimeno, M. L. Andrade, and M. J. Mendes. "Modelling, Optimization and Control of Subway Systems." IFAC Proceedings Volumes 20, no. 3 (1987): 165–68. http://dx.doi.org/10.1016/s1474-6670(17)55892-9.

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50

Guo, Baoyu, Aibing Yu, and Jun Guo. "Numerical Modelling of ESP for Design Optimization." Procedia Engineering 102 (2015): 1366–72. http://dx.doi.org/10.1016/j.proeng.2015.01.268.

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