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Journal articles on the topic 'Optimization-simulation modelling'

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Author: Grafiati
Published: 6 September 2023

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1

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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2

Stanković, Ratko, and Diana Božić. "Applying Simulation Modelling in Quantifying Optimization Results." Tehnički glasnik 15, no. 4 (November 1, 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, while the results are quantified on the simulation model, by using Rockwell Automation simulation software. Input data are collected from a freight forwarding company in Zagreb, specialized in groupage transport (Less Than Truckload - LTL).
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3

Yang, Xin-She, and Slawomir Koziel. "Computational optimization, modelling and simulation–a paradigm shift." Procedia Computer Science 1, no. 1 (May 2010): 1297–300. http://dx.doi.org/10.1016/j.procs.2010.04.144.

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4

Husain, Asghar, Ali Hassan, Darwish M. K. Al-Gobaisi, Adil Al-Radif, A. Woldai, and C. Sommariva. "Modelling, simulation, optimization and control of multistage flashing (MSF) desalination plants Part I: Modelling and simulation." Desalination 92, no. 1-3 (July 1993): 21–41. http://dx.doi.org/10.1016/0011-9164(93)80073-v.

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5

Kodym, Oldřich, Zdeněk Čujan, Michal Turek, and Nikoleta Mikušová. "Optimization of material flow using simulation." MATEC Web of Conferences 263 (2019): 01007. http://dx.doi.org/10.1051/matecconf/201926301007.

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The strategic aim of all production enterprises is to achieve the highest possible profit, for which they use attributes of lean production that are related and support the reduction of production costs. Using of theoretical knowledge and realization of a detailed analysis of a material flow including mapping of individual production processes it is possible to obtain the necessary data sets identifying savings options by increasing of efficiency, including cost savings. Methods of modelling and simulation were used for the optimization of material flow.
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6

Westermann, Paul, Theodor Christiaanse, Will Beckett, Paul Kovacs, and Ralph Evins. "besos: Building and Energy Simulation, Optimization and Surrogate Modelling." Journal of Open Source Software 6, no. 60 (April 6, 2021): 2677. http://dx.doi.org/10.21105/joss.02677.

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7

Yang, Xin-She, Slawomir Koziel, and Leifur Leifsson. "Computational Optimization, Modelling and Simulation: Recent Trends and Challenges." Procedia Computer Science 18 (2013): 855–60. http://dx.doi.org/10.1016/j.procs.2013.05.250.

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8

Yang, Xin-She, Slawomir Koziel, and Leifur Leifsson. "Computational Optimization, Modelling and Simulation: Past, Present and Future." Procedia Computer Science 29 (2014): 754–58. http://dx.doi.org/10.1016/j.procs.2014.05.067.

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9

Yang, Xin-She, Slawomir Koziel, and Leifur Leifsson. "Computational optimization, modelling and simulation: Recent advances and overview." Procedia Computer Science 4 (2011): 1230–33. http://dx.doi.org/10.1016/j.procs.2011.04.132.

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10

Lu, C. J. J., K. H. Tsai, J. C. S. Yang, and Y. M. Wang. "Real-Time Operation Plan Optimization Using Accelerated Simulation Modelling." International Journal of Modelling and Simulation 16, no. 4 (January 1996): 184–91. http://dx.doi.org/10.1080/02286203.1996.11760298.

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11

Popa, Cicerone Laurentiu, Costel Emil Cotet, Stefan Gavrila, and Valentin Ionita. "Modelling, Simulation and Optimization of a Packaging and Palletizing System." Applied Mechanics and Materials 760 (May 2015): 205–11. http://dx.doi.org/10.4028/www.scientific.net/amm.760.205.

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This paper presents a material flow management case study based on our research in modelling, simulation and optimization for a packaging and palletizing system. The studied system allows packaging, palletizing, wrapping and storage of several types of products within a food company. For each type of product to be packaged and stored a material flow system simulation is necessary in order to identify and eliminate the bottlenecks and to obtain an optimized system in terms of productivity. Because the optimization algorithm is the same for different kind of products we will illustrate our research using a single type of product. We will focus the case study on packaging coffee bags in boxes. The boxes are positioned on pallets in order to create a stack that will be wrapped for storage. We used Flexim 7 simulation software for modelling, simulation and optimization of the packaging and palletizing system.
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12

Pierreval, Henri, and Jean Luc Paris. "From ‘simulation optimization’ to ‘simulation configuration’ of systems." Simulation Modelling Practice and Theory 11, no. 1 (March 2003): 5–19. http://dx.doi.org/10.1016/s1569-190x(02)00096-5.

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13

Yücesan, Enver, Yuh-Chuyn Luo, Chun-Hung Chen, and Insup Lee. "Distributed web-based simulation experiments for optimization." Simulation Practice and Theory 9, no. 1-2 (October 2001): 73–90. http://dx.doi.org/10.1016/s0928-4869(01)00037-4.

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14

Li, Hong-Shuang. "Subset simulation for unconstrained global optimization." Applied Mathematical Modelling 35, no. 10 (October 2011): 5108–20. http://dx.doi.org/10.1016/j.apm.2011.04.023.

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15

Suo, Xin-Shi, Xiong-Qing Yu, and Hong-Shuang Li. "Subset simulation for multi-objective optimization." Applied Mathematical Modelling 44 (April 2017): 425–45. http://dx.doi.org/10.1016/j.apm.2017.02.005.

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16

Glavan, I., Z. Prelec, and B. Pavkovic. "Modelling, Simulation and Optimization of Small-Scale CCHP Energy Systems." International Journal of Simulation Modelling 14, no. 4 (December 15, 2015): 683–96. http://dx.doi.org/10.2507/ijsimm14(4)10.336.

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17

Hameed, Shymaa. "Modelling, simulation and optimization of Bai-Hasan wide distillate hydrodesulfurization." Journal of Environmental Studies 11, no. 1 (June 1, 2013): 49–58. http://dx.doi.org/10.21608/jesj.2013.192889.

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18

Cai, Yanpeng, Cong Dong, and Jianying Cai. "A Review: Simulation and Optimization Modelling for CO2 Geological Storage." Journal of Environmental Accounting and Management 4, no. 3 (September 2016): 223–52. http://dx.doi.org/10.5890/jeam.2016.09.001.

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19

Ganjeizadeh, Farnaz, Nikita Gupta, Anamika Burile, and Helen Zong. "Optimization of Multiple Effect Evaporation System via Modelling and Simulation." Procedia Manufacturing 51 (2020): 1785–90. http://dx.doi.org/10.1016/j.promfg.2020.10.248.

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20

Yang, Xin-She, Slawomir Koziel, and Leifur Leifsson. "Computational Optimization, Modelling and Simulation: Smart Algorithms and Better Models." Procedia Computer Science 9 (2012): 852–56. http://dx.doi.org/10.1016/j.procs.2012.04.091.

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21

Campbell, K. A., and S. M. Dewhurst. "A hierarchical simulation-through-optimization approach to forest disturbance modelling." Ecological Modelling 202, no. 3-4 (April 2007): 281–96. http://dx.doi.org/10.1016/j.ecolmodel.2006.10.020.

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22

Po-ki, Yuen, Alan Dunn, and Mankit Ray Yeung. "Solar Air-Conditioning System Performance Optimization by TRNSYS Simulation Modelling." HKIE Transactions 2, no. 3 (January 1995): 27–32. http://dx.doi.org/10.1080/1023697x.1995.10667688.

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23

Truong, T. H., F. Azadivar, and K. D. E. Stokesbury. "An Application of Simulation Modelling and Optimization in Fisheries Management." International Journal of Modelling and Simulation 30, no. 3 (January 2010): 361–70. http://dx.doi.org/10.1080/02286203.2010.11442592.

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24

Kulkarni, R. N., and A. D. Shaligram. "Modelling, Simulation and Design Optimization of Rapid Thermal Processing System." IETE Technical Review 15, no. 1-2 (January 1998): 123–29. http://dx.doi.org/10.1080/02564602.1998.11416739.

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25

Vakiloroaya, V., Q. P. Ha, and B. Samali. "Energy-efficient HVAC systems: Simulation–empirical modelling and gradient optimization." Automation in Construction 31 (May 2013): 176–85. http://dx.doi.org/10.1016/j.autcon.2012.12.006.

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26

Guariso, Giorgio, Martin Hitz, and Hannes Werthner. "An integrated simulation and optimization modelling environment for decision support." Decision Support Systems 16, no. 2 (February 1996): 103–17. http://dx.doi.org/10.1016/0167-9236(94)00058-1.

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27

Candelieri, Antonio, Andrea Ponti, Ilaria Giordani, and Francesco Archetti. "Lost in Optimization of Water Distribution Systems: Better Call Bayes." Water 14, no. 5 (March 3, 2022): 800. http://dx.doi.org/10.3390/w14050800.

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The main goal of this paper is to show that Bayesian optimization can be regarded as a general framework for the data-driven modelling and solution of problems arising in water distribution systems. Scenario-based hydraulic simulation and Monte Carlo are key tools in modelling in water distribution systems. The related optimization problems fall into a simulation/optimization framework in which objectives and constraints are often black box. Bayesian optimization (BO) is characterized by a surrogate model, usually a Gaussian process but also a random forest, as well as neural networks and an acquisition function that drives the search for new evaluation points. These modelling options make BO nonparametric, robust, flexible, and sample efficient, making it particularly suitable for simulation/optimization problems. A defining characteristic of BO is its versatility and flexibility, given, for instance, by different probabilistic models, in particular different kernels, different acquisition functions. These characteristics of the Bayesian optimization approach are exemplified by two problems: cost/energy optimization in pump scheduling and optimal sensor placement for early detection of contaminant intrusion. Different surrogate models have been used both in explicit and implicit control schemes, showing that BO can drive the process of learning control rules directly from operational data. BO can also be extended to multi-objective optimization. Two algorithms are proposed for multi-objective detection problems using two different acquisition functions.
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28

Plinta, Dariusz, and Patrik Grznar. "Optimisation of production processes with the use of the modelling and simulation method." MATEC Web of Conferences 299 (2019): 03007. http://dx.doi.org/10.1051/matecconf/201929903007.

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The paper presents an application of the modelling and simulation method for finding an optimum solution related to designing production processes. Using simulation in connection with optimization makes it possible to check almost all admissible variants of the proposed improvements comparatively quickly in order to evaluate them and to choose the best solution. The paper presents how a simulation model is created and shows an example of multi-criteria optimization.
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29

Katebi, M. R., T. Lee, and M. Rouse. "EASY5 Application in Process Modelling, Optimization and Control." Measurement and Control 25, no. 7 (September 1992): 207–12. http://dx.doi.org/10.1177/002029409202500703.

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The Boeing Engineering Analysis System is a menu-driven simulation package for modelling, analysis and design of large complex dynamical systems. The latest version, EASY5x, also provides multi-layered hierarchical modelling facilities and runs under X Windows. The package has many powerful nonlinear/ linear, continuous/discrete, steady-state analysis and optimization functions. This paper describes an introduction to the facilities and features offered by EASY5x. The modelling, control and optimization of a Coordinate Measuring Machine (CMM) is presented as an industrial example to evaluate some important features of EASY5x.
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30

Cusick, T. W. "Airfield simulation with optimization via decomposition." Mathematical and Computer Modelling 39, no. 6-8 (March 2004): 745–58. http://dx.doi.org/10.1016/s0895-7177(04)90552-3.

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31

Shapiro, Alexander. "Simulation-based optimization—convergence analysis and statistical inference." Communications in Statistics. Stochastic Models 12, no. 3 (January 1996): 425–54. http://dx.doi.org/10.1080/15326349608807393.

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32

Li, YD, W. Wang, JW Wang, ZC Cao, and ZK Kang. "The Reliability Optimization of a ShipborneDevice by Surrogate Modelling." Journal of Physics: Conference Series 2460, no. 1 (April 1, 2023): 012010. http://dx.doi.org/10.1088/1742-6596/2460/1/012010.

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Abstract A Shipborne Weapon Device is not only affected by repetitive and low tense bumping impact, but also warship swing and tilt caused by sea wind and wave. To study on the influences in warship mechanical environment on working reliability of shipborne weapon device, the positioning accuracy of a large caliber naval gun rotary magazine is researched. A automated simulation process is carried out in a multidisciplinary design environment. A more accurate surrogate modelling is first set up based on design of experiments technique, and reliability design and optimization are established sequentially, the positioning accuracy reliability of the rotary magazine is increased from 50.2% to 99.3%. In experimental testing phase, the simulation model is verified and corrected based on test data, and the simulation accuracy is ensured, and test limitations are compensated by simulation in addition. This method can promote the work reliability of similar shipborne devices in warship complicated working conditions.
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33

Yu, Bin, and Tao Feng. "Simulation and optimization in engineering." SIMULATION 95, no. 9 (July 5, 2019): 769. http://dx.doi.org/10.1177/0037549719863213.

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34

van Dijk, Nico M., and Erik van der Sluis. "Practical optimization by OR and simulation." Simulation Modelling Practice and Theory 16, no. 8 (September 2008): 1113–22. http://dx.doi.org/10.1016/j.simpat.2008.02.016.

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35

Leeuw, E. J., J. F. Kramer, B. A. Bult, and M. H. Wijcherson. "Optimization of nutrient removal with on-line monitoring and dynamic simulation." Water Science and Technology 33, no. 1 (January 1, 1996): 203–9. http://dx.doi.org/10.2166/wst.1996.0020.

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Many Wastewater Treatment Plants (WWTP) have to reach stricter effluent standards. WWTPs loaded with a low F/M-ratio can often reach these standards by process optimization, without extension of the plant. This paper presents the step by step approach of the process optimization. An important step is a three week period of on-line monitoring of ammonium, nitrate and phosphorus: the analyses provide information about changes of effluent quality within the day. Another important step is process modelling. The model is calibrated by the on-line measurements. Process modelling enables a comparison of the effectiveness of different process modifications. This approach is applied at a WWTP of the Friesland Waterboard.
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36

Zhilnikova, Natalia. "Geoinformation modelling system of natural technical complexes for simulation modelling and optimization of load distribution." IOP Conference Series: Materials Science and Engineering 450 (November 30, 2018): 062010. http://dx.doi.org/10.1088/1757-899x/450/6/062010.

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37

Jian, C. F., and Y. Wang. "Batch Task Scheduling-Oriented Optimization Modelling and Simulation in Cloud Manufacturing." International Journal of Simulation Modelling 13, no. 1 (March 15, 2014): 93–101. http://dx.doi.org/10.2507/ijsimm13(1)co2.

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38

Ziaei, Mina, Lee Teang Shui, and Ehsan Goodarzi. "Optimization and simulation modelling for operation of the Zayandeh Rud Reservoir." Water International 37, no. 3 (May 2012): 305–18. http://dx.doi.org/10.1080/02508060.2012.688189.

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39

Kalachev, V. N., V. E. Krivonozko, and B. V. Nemchinov. "Optimization Modelling and Simulation for Production Planning in Flexible Manufacturing System." IFAC Proceedings Volumes 25, no. 18 (August 1992): 159–62. http://dx.doi.org/10.1016/s1474-6670(17)49963-0.

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40

Dinkelmann, M., M. Wächter, and G. Sachs. "Modelling and simulation of unsteady heat transfer for aerospacecraft trajectory optimization." Mathematics and Computers in Simulation 53, no. 4-6 (October 2000): 389–94. http://dx.doi.org/10.1016/s0378-4754(00)00231-7.

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41

Schlüter, F., and P. Wältermann. "Hierarchical Control Structures for Hybrid Vehicles - Modelling, Simulation, and Optimization 1." IFAC Proceedings Volumes 28, no. 1 (March 1995): 115–20. http://dx.doi.org/10.1016/s1474-6670(17)45682-5.

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42

S., Senthil, and Senthilmurugan S. "Reverse Osmosis–Pressure Retarded Osmosis hybrid system: Modelling, simulation and optimization." Desalination 389 (July 2016): 78–97. http://dx.doi.org/10.1016/j.desal.2016.01.027.

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43

Singh, Ajay, and Sudhindra Nath Panda. "Optimization and Simulation Modelling for Managing the Problems of Water Resources." Water Resources Management 27, no. 9 (April 25, 2013): 3421–31. http://dx.doi.org/10.1007/s11269-013-0355-7.

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44

Kimmerle, Sven-Joachim. "Modelling, simulation and optimization of an elastic structure under moving loads." PAMM 16, no. 1 (October 2016): 697–98. http://dx.doi.org/10.1002/pamm.201610337.

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45

Domschke, Pia, Oliver Kolb, and Jens Lang. "Adaptive Modelling, Simulation and Optimization of Gas and Water Supply Networks." PAMM 16, no. 1 (October 2016): 839–40. http://dx.doi.org/10.1002/pamm.201610408.

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46

Chen, Jing. "The Modelling by Parameter Identification and Online Simulation in Refining." Advanced Materials Research 756-759 (September 2013): 1769–73. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1769.

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FCCU (fluid catalytic cracking unit) is a complex system featured by highly non-linear, time variable, long time delay, and close coupling. Studying the process mechanism of FCCU, this paper defines a mathematical model for coupling parameter identification and online simulation. In addition, the author proposes comprehensive tuning technologies for multiple optimization and also systematically describes the cause-effect statistical law for parameter variation based on the input-and-output quantitative relation. The memory, comparison and reasoning of operation experience can help to guide the optimization operation in production process.
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47

Rahnejat, H. "Simulating for ‘Resource Optimization’ in Robot-Assisted Automatic Assembly." Proceedings of the Institution of Mechanical Engineers, Part B: Management and engineering manufacture 200, no. 3 (August 1986): 181–86. http://dx.doi.org/10.1243/pime_proc_1986_200_069_02.

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In most manufacturing systems emphasis is now given to resource flexibility in operation. The aim is to respond swiftly to changes in product mix and/or market demands. Discrete event computer simulation is seen as a tool in defining a suitable system configuration at the preliminary design stage. Furthermore, simulation in dynamic form can represent the interactions between the system components and provide a detailed prediction of its performance. Although many existing computer simulation packages have reached a good level of general purpose modelling, by and large they lack the required versatility to deal with some specific features of manufacturing systems. One such important area is the robot-assisted automatic assembly where minimization of non-productive activities in the product assembly cycle is of vital interest. This paper introduces a flexible modelling technique which identifies the resource utilization and optimization levels during the individual processes of a product assembly cycle. Within the working constraints of an assembly system, an ‘optimal’ robot sequential cycle is obtained by implementing this modelling technique in GPSL (general purpose simulation language).
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48

Zainal, S. A., W. R. Daud, M. I. Rosli, S. Harun, Zulfan Adi Putra, and M. R. Bilad. "Development of An Integrated Surface and Sub-Surface Simulation Model in A Single Simulation Platform." Indonesian Journal of Science and Technology 5, no. 1 (January 29, 2020): 109–24. http://dx.doi.org/10.17509/ijost.v5i1.17439.

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An integrated model between surface and sub-surface is typically done by interconnecting many process modelling platforms. PROSPER and GAP are the common steady state modelling platforms for sub-surface while VMGSim and HYSYS are typical steady state surface modelling platforms. A major issue of using multiple simulation platforms is the compatibility of thermodynamic physical properties calculations among the platforms. This situation makes the simulations difficult to converge to a consistent thermo physical properties values. This is due to different interaction parameters applied in each platform that impact flashing and the physical property values even though the same property package such as Peng Robinson is used. To overcome this convergence problem, a single simulation platform within iCON (PETRONAS’s standard process simulation software, co-developed with VMG-Schlumberger) has been developed. This allows the use of one thermodynamic package across the integrated model. PROSPER sub-surface pressure-flow relationship results were automatically correlated and connected to surface models within the iCON environment. This integrated model was validated with data from operations and yielded about 1.23% average error tolerance. Based on this validated model, an optimization envelope can be developed with all possible well lineup configurations. This envelope covers set points for the operations where CAPEX free optimization can readily be applied.
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49

Niu, Ben, and Hong Wang. "Bacterial Colony Optimization." Discrete Dynamics in Nature and Society 2012 (2012): 1–28. http://dx.doi.org/10.1155/2012/698057.

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This paper investigates the behaviors at different developmental stages inEscherichia coli(E. coli) lifecycle and developing a new biologically inspired optimization algorithm named bacterial colony optimization (BCO). BCO is based on a lifecycle model that simulates some typical behaviors ofE. colibacteria during their whole lifecycle, including chemotaxis, communication, elimination, reproduction, and migration. A newly created chemotaxis strategy combined with communication mechanism is developed to simplify the bacterial optimization, which is spread over the whole optimization process. However, the other behaviors such as elimination, reproduction, and migration are implemented only when the given conditions are satisfied. Two types of interactive communication schemas: individuals exchange schema and group exchange schema are designed to improve the optimization efficiency. In the simulation studies, a set of 12 benchmark functions belonging to three classes (unimodal, multimodal, and rotated problems) are performed, and the performances of the proposed algorithms are compared with five recent evolutionary algorithms to demonstrate the superiority of BCO.
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50

Starikovičius, Vadimas, Raimondas Čiegis, and Oleg Iliev. "A PARALLEL SOLVER FOR THE DESIGN OF OIL FILTERS." Mathematical Modelling and Analysis 16, no. 1 (June 24, 2011): 326–41. http://dx.doi.org/10.3846/13926292.2011.582591.

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Nowadays, it is widely recognized that computer simulation plays a crucial role in designing oil filters used in the automotive industry. However, even a single direct simulation of the flow usually requires significant computational resources. Thus, it is obvious that solution of optimization problems is only feasible using parallel computers and algorithms.In this paper, we present a general master-slave parallel template, which was specially designed for the easy integration of direct parallel solvers into a parallel optimization tool. We show how an already existing direct solver for the 3D simulation of flow through the oil filter is integrated into our template to obtain a parallel optimization solver. Some capabilities and performance of this solver are demonstrated by solving geometry optimization problem of a filter element.
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