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Journal articles on the topic 'Optimization via simulation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Bartz-Beielstein, Thomas, Martin Zaefferer, and Quoc Cuong Pham. "Optimization via multimodel simulation." Structural and Multidisciplinary Optimization 58, no. 3 (2018): 919–33. http://dx.doi.org/10.1007/s00158-018-1934-2.

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2

Fu, Michael C. "Optimization via simulation: A review." Annals of Operations Research 53, no. 1 (1994): 199–247. http://dx.doi.org/10.1007/bf02136830.

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3

Hong, L. Jeff, and Barry L. Nelson. "Discrete Optimization via Simulation Using COMPASS." Operations Research 54, no. 1 (2006): 115–29. http://dx.doi.org/10.1287/opre.1050.0237.

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4

Wald, Andrew, Mark Harmon, and Diego Klabjan. "Structured deplaning via simulation and optimization." Journal of Air Transport Management 36 (April 2014): 101–9. http://dx.doi.org/10.1016/j.jairtraman.2014.01.001.

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5

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

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6

Talpaz, H., and A. B. Hearn. "Calibration of Simulation Models via Nonlinear Optimization." IFAC Proceedings Volumes 21, no. 9 (1988): 1373–75. http://dx.doi.org/10.1016/s1474-6670(17)54920-4.

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7

Safizadeh, M. Hossein, and Robert Signorile. "Optimization of Simulation via Quasi-Newton Methods." ORSA Journal on Computing 6, no. 4 (1994): 398–408. http://dx.doi.org/10.1287/ijoc.6.4.398.

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8

Pichitlamken, Juta, and Barry L. Nelson. "A combined procedure for optimization via simulation." ACM Transactions on Modeling and Computer Simulation 13, no. 2 (2003): 155–79. http://dx.doi.org/10.1145/858481.858485.

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9

Chen, Shih-Pin. "Simulation response optimization via an alternating variable methodsimulation response optimization." Engineering Optimization 35, no. 6 (2003): 675–84. http://dx.doi.org/10.1080/03052150310001620722.

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10

Wing, Robyn, Michael P. Goldman, Monica M. Prieto, et al. "Usability Testing Via Simulation." Pediatric Emergency Care 40, no. 8 (2024): 575–81. http://dx.doi.org/10.1097/pec.0000000000003223.

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Objectives To inform development of a preintubation checklist for pediatric emergency departments via multicenter usability testing of a prototype checklist. Methods This was a prospective, mixed methods study across 7 sites in the National Emergency Airway Registry for Pediatric Emergency Medicine (NEAR4PEM) collaborative. Pediatric emergency medicine attending physicians and senior fellows at each site were first oriented to a checklist prototype, including content previously identified using a modified Delphi approach. Each site used the checklist in 2 simulated cases: an “easy airway” and

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11

Rahim, N. H. A., A. M. Kassim, M. F. Miskon, A. H. Azahar, and H. Sakidin. "Optimization of One Legged Hopping Robot Hardware Parameters via Solidworks." Applied Mechanics and Materials 393 (September 2013): 544–49. http://dx.doi.org/10.4028/www.scientific.net/amm.393.544.

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This paper discussed about simulation of one legged hopping robot via Solidworks software in order to determine the optimum hardware parameters of the hopping robot. Simulations have been done according to different variables that have been set up earlier which are crank bar length, spring length and spring coefficient. The best parameters were chosen in terms of higher and stable hopping performance. Besides that, an experiment is done to validate the parameters from the simulation. Average hopping height is discussed and overall performances of hopping height stability are proved by the norm

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12

Richie, James E., and Cristinel Ababei. "Optimization of patch antennas via multithreaded simulated annealing based design exploration." Journal of Computational Design and Engineering 4, no. 4 (2017): 249–55. http://dx.doi.org/10.1016/j.jcde.2017.06.004.

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Abstract In this paper, we present a new software framework for the optimization of the design of microstrip patch antennas. The proposed simulation and optimization framework implements a simulated annealing algorithm to perform design space exploration in order to identify the optimal patch antenna design. During each iteration of the optimization loop, we employ the popular MEEP simulation tool to evaluate explored design solutions. To speed up the design space exploration, the software framework is developed to run multiple MEEP simulations concurrently. This is achieved using multithreadi

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13

Kassem, Ayman Hamdy. "Trajectory Optimization via Simulation Using Incremented Genetic Algorithms." Canadian Aeronautics and Space Journal 52, no. 1 (2006): 15–20. http://dx.doi.org/10.5589/q06-005.

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14

Hu, Liujia, and Sigrun Andradottir. "Constrained Search via Penalization for Continuous Simulation Optimization." IEEE Transactions on Automatic Control 65, no. 11 (2020): 4741–52. http://dx.doi.org/10.1109/tac.2020.2968548.

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15

Guo, Wei, Yi Zhang, Man Xu, Zuo Zhang, and Li Li. "Parking Spaces Repurchase Strategy Design via Simulation Optimization." Journal of Intelligent Transportation Systems 20, no. 3 (2015): 255–69. http://dx.doi.org/10.1080/15472450.2015.1063424.

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16

Bhatnagar, Shalabh, N. Hemachandra, and Vivek Kumar Mishra. "Stochastic approximation algorithms for constrained optimization via simulation." ACM Transactions on Modeling and Computer Simulation 21, no. 3 (2011): 1–22. http://dx.doi.org/10.1145/1921598.1921599.

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17

Weerasinghe, Ganesha, Hongmei Chi, and Yanzhao Cao. "Particle Swarm Optimization Simulation via Optimal Halton Sequences." Procedia Computer Science 80 (2016): 772–81. http://dx.doi.org/10.1016/j.procs.2016.05.367.

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18

Golenko-Ginzburg, Dimitri, Ahazon Gonik, and Ljubisa Papic. "Developing cost-optimization production control model via simulation." Mathematics and Computers in Simulation 49, no. 6 (1999): 335–51. http://dx.doi.org/10.1016/s0378-4754(99)00090-7.

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19

Rothuizen, E., W. Mérida, M. Rokni, and M. Wistoft-Ibsen. "Optimization of hydrogen vehicle refueling via dynamic simulation." International Journal of Hydrogen Energy 38, no. 11 (2013): 4221–31. http://dx.doi.org/10.1016/j.ijhydene.2013.01.161.

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20

Kao, Chiang, Chang-Chung Li, and Shih-Pin Chen. "Simulation response optimization via direct conjugate direction method." Computers & Operations Research 30, no. 4 (2003): 541–52. http://dx.doi.org/10.1016/s0305-0548(02)00023-0.

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21

Kabirian, Alireza, and Sigurdur Ólafsson. "Continuous optimization via simulation using Golden Region search." European Journal of Operational Research 208, no. 1 (2011): 19–27. http://dx.doi.org/10.1016/j.ejor.2010.09.002.

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22

Kleijnen, Jack P. C. "Simulation-optimization via Kriging and bootstrapping: a survey." Journal of Simulation 8, no. 4 (2014): 241–50. http://dx.doi.org/10.1057/jos.2014.4.

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23

Chen, Dongdong, Yintang Yang, Xianglong Wang, Di Li, Yi Liang, and Changqing Xu. "Thermal-Stress Coupling Optimization for Coaxial through Silicon Via." Symmetry 15, no. 2 (2023): 264. http://dx.doi.org/10.3390/sym15020264.

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In this paper, a thermal-stress coupling optimization strategy for coaxial through silicon via (TSV) is developed based on the finite element method (FEM), artificial neural network (ANN) model and particle swarm optimization (PSO) algorithm. In order to analyze the effect of design parameters on the thermal-stress distribution of coaxial TSV, the FEM simulations of coaxial TSV are conducted by COMSOL Multiphysics. The structure of coaxial TSV is symmetric. The mapping relationships between the design parameters and performance indexes are described by ANN models based on the simulation data o

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24

Xu, Wendy Lu, and Barry L. Nelson. "Empirical stochastic branch-and-bound for optimization via simulation." IIE Transactions 45, no. 7 (2013): 685–98. http://dx.doi.org/10.1080/0740817x.2013.768783.

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25

Chia, Yen Lin, and Peter W. Glynn. "Limit Theorems for Simulation-Based Optimization via Random Search." ACM Transactions on Modeling and Computer Simulation 23, no. 3 (2013): 1–18. http://dx.doi.org/10.1145/2499913.2499915.

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26

Yeomans, J. S. "Automatic generation of efficient policy alternatives via simulation-optimization." Journal of the Operational Research Society 53, no. 11 (2002): 1256–67. http://dx.doi.org/10.1057/palgrave.jors.2601439.

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27

Kim, Minseok, Sunghoon Jun, Sukhan Kim, Moonyong Lee, and Eung Ki Lee. "Design optimization of a sublimation purifier via computer simulation." Electronic Materials Letters 9, S1 (2013): 17–22. http://dx.doi.org/10.1007/s13391-013-3187-y.

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28

Tsai, Shing Chih. "Rapid Screening Procedures for Zero-One Optimization via Simulation." INFORMS Journal on Computing 25, no. 2 (2013): 317–31. http://dx.doi.org/10.1287/ijoc.1120.0504.

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29

Pujowidianto, Nugroho A. "Constrained optimization via simulation models for new product innovation." IOP Conference Series: Materials Science and Engineering 273 (November 2017): 012024. http://dx.doi.org/10.1088/1757-899x/245/1/012024.

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30

Pujowidianto, Nugroho A. "Constrained optimization via simulation models for new product innovation." IOP Conference Series: Materials Science and Engineering 273 (November 2017): 012024. http://dx.doi.org/10.1088/1757-899x/273/1/012024.

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31

Vocaturo, Francesca. "Optimization via simulation for logistic systems planning and control." 4OR 7, no. 1 (2008): 97–100. http://dx.doi.org/10.1007/s10288-008-0070-z.

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32

Wu, D., C. L. Zhao, C. L. Huang, and X. Luo. "Micro-Texture Optimization for Titanium Cutting Tools via Simulation." International Journal of Simulation Modelling 24, no. 2 (2025): 333–44. https://doi.org/10.2507/ijsimm24-2-co7.

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33

Matúšová, Miriam, Erika Hrušková, and Karol Velíšek. "Analyse of Flexible Assembly Cell via Software Witness." Applied Mechanics and Materials 120 (October 2011): 65–69. http://dx.doi.org/10.4028/www.scientific.net/amm.120.65.

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This Computer simulation models allow to solve the problems by their complexity beyond the capacity of other mathematical and analytical methods. WITNESS is suitable software for simulation and optimization of production, service and logistics systems.

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34

Gao, Yun, Mohammad Reza Farahani, and Wei Gao. "Ontology optimization tactics via distance calculating." Applied Mathematics and Nonlinear Sciences 1, no. 1 (2016): 159–74. http://dx.doi.org/10.21042/amns.2016.1.00012.

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AbstractIn this article, we propose an ontology learning algorithm for ontology similarity measure and ontology mapping in view of distance function learning techniques. Using the distance computation formulation, all the pairs of ontology vertices are mapped into real numbers which express the distance of their corresponding vectors. The more distance between two vertices, the smaller similarity between their corresponding concepts. The stabilities of our learning algorithm are defined and several bounds are yielded via stability assumptions. The simulation experimental conclusions show that

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35

Yuan, Yue Feng, Wu Yi Chen, and Dong Liu. "Determination of Flow Stress Based on Cutting Simulation and Optimization." Advanced Materials Research 189-193 (February 2011): 2274–79. http://dx.doi.org/10.4028/www.scientific.net/amr.189-193.2274.

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A new simple methodology to determine the flow stress of material is presented and Johnson–Cook (JC) constitutive model of titanium alloy Ti6Al4V is obtained via this approach. The JC model is verified by comparison between simulations and experiments. The results show that the model is suitable for cutting simulation. This simple method can be used to determine conveniently JC model not only in the scientific research but also in the workshop production.

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36

Mounchili, Arouna Patouossa, Stefan Bosse, Dirk Lehmhus, and Adrian Struss. "Putting Stiffness where it’s needed: Optimizing the Mechanical Response of Multi-Material Structures." MATEC Web of Conferences 349 (2021): 03001. http://dx.doi.org/10.1051/matecconf/202134903001.

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Manufacturing processes are increasingly adapted to multi-material part production to facilitate lightweight design via improvement of structural performance. The difficulty lies in determining the optimum spatial distribution of the individual materials. Multi-Phase Topology Optimization (MPTO) achieves this aim via iterative, linear-elastic Finite Element (FE) simulations providing element- and part-level strain energy data under a given design load and using it to redistribute predefined material fractions to minimize total strain energy. The result us a part configuration offering maximum

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37

Yin, Qiaozhi, Hong Nie, Xiaohui Wei, and Ming Zhang. "Aircraft electric anti-skid braking and combined direction control system using co-simulation and experimental methods." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 234, no. 2 (2019): 173–91. http://dx.doi.org/10.1177/0954410019852825.

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The work reported in this paper concentrates on the design and application of an electric ground control system combining the braking and steering mechanisms on a small unmanned aerial vehicle. A virtual prototype of a small unmanned aerial vehicle is built with a multibody dynamic software LMS Virtual.Lab Motion. An electric anti-skid braking system and a new combined direction control system considering the sensors models are established with MATLAB/Simulink. Optimizations are carried out using a global optimization command patternsearch first and then a local optimization method fminsearch

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38

Lan, Mengqiao, Libo Huang, Ling Yang, et al. "Late-Stage Optimization of Modern ILP Processor Cores via FPGA Simulation." Applied Sciences 12, no. 23 (2022): 12225. http://dx.doi.org/10.3390/app122312225.

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Late-stage (post-RTL implementation) optimization is important in achieving target performance for realistic processor design. However, several challenges remain for modern out-of-order ILP (instruction-level-parallelism) processors, such as simulation speed, flexibility, and complexity problems. This paper restudy FPGA simulation as an effective performance simulation method and proposes FPGA-enhanced design flow as an effective method to address these problems. It features a late-stage aware RTL design that parameterizes various potential design options induced from early-stage optimization.

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39

Gao, Lei, Guohua Nie, and Teng Zhang. "A Study of Hierarchical Biological Composite Structures Via a Coarse-Grained Molecular Dynamics Simulation Approach." International Journal of Applied Mechanics 08, no. 06 (2016): 1650084. http://dx.doi.org/10.1142/s1758825116500848.

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A coarse-grained molecular dynamics (MD) simulation approach based on a widely used fuse model is developed to study the mechanical behaviors of hierarchical brick and mortar bio-composites made from hard minerals and soft polymers. Massively parallel MD simulations are performed to investigate the toughness enhancement and the effect of stochastic variations in brick strength in representative bio-composites. Our simulations indicate that the hierarchical structure of bio-composites not only plays a key role in toughness optimization, but also reduces the sensitivity of the structure to biomi

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40

Gocken, M., A. T. Dosdogru, and A. Boru. "Optimization via Simulation for Inventory Control Policies and Supplier Selection." International Journal of Simulation Modelling 16, no. 2 (2017): 241–52. http://dx.doi.org/10.2507/ijsimm16(2)5.375.

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41

Azadeh, Ali, and Farid Ghaderi. "Optimization of a Heavy Continuous Rolling Mill System Via Simulation." Journal of Applied Sciences 6, no. 3 (2006): 611–15. http://dx.doi.org/10.3923/jas.2006.611.615.

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42

Hongwei Chen, Lei Xiong, and Chunzhi Wang. "Cloud Task Scheduling Simulation via Improved Ant Colony Optimization Algorithm." Journal of Convergence Information Technology 8, no. 7 (2013): 1139–47. http://dx.doi.org/10.4156/jcit.vol8.issue7.140.

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43

Hamizan, S., S. Roselina, H. Habibollah, Y. Yusliza, and M. Y. Lizawati. "Crowd Emergency Evacuation Simulation Time Analysis via Obstacle Optimization Strategy." Journal of Physics: Conference Series 2129, no. 1 (2021): 012045. http://dx.doi.org/10.1088/1742-6596/2129/1/012045.

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Abstract The crowd evacuation simulation is essential to provide important results for occupants, especially in the large capacity building compared to the human fire drill exercise. The strategy of evacuation such as the use of obstacles may need to be adapted by many organizations as an aid to help in visualizing and estimating the evacuation time during an emergency. During certain crowd events, they may consider the various setting of the object to ensure smoothness and effective crowd evacuation flow. In this paper, it aims to provide the simulation with 100-1000 agents and testing with o

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44

Hong, L. Jeff, Barry L. Nelson, and Jie Xu. "Speeding up COMPASS for high-dimensional discrete optimization via simulation." Operations Research Letters 38, no. 6 (2010): 550–55. http://dx.doi.org/10.1016/j.orl.2010.09.003.

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45

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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46

Legato, Pasquale, Rina Mary Mazza, and Daniel Gullì. "Integrating tactical and operational berth allocation decisions via Simulation–Optimization." Computers & Industrial Engineering 78 (December 2014): 84–94. http://dx.doi.org/10.1016/j.cie.2014.10.003.

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47

Yegul, Mustafa Fatih, Fatih Safa Erenay, Soeren Striepe, and Mustafa Yavuz. "Improving configuration of complex production lines via simulation-based optimization." Computers & Industrial Engineering 109 (July 2017): 295–312. http://dx.doi.org/10.1016/j.cie.2017.04.019.

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48

Breitner, M. H., B. Koslik, O. von Stryk, and H. J. Pesch. "Iterative design of economic models via simulation, optimization and modeling." Mathematics and Computers in Simulation 39, no. 5-6 (1995): 527–32. http://dx.doi.org/10.1016/0378-4754(95)00114-5.

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49

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

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50

Yang, Yang, and Rick S. Blum. "Sensor Placement in Gaussian Random Field Via Discrete Simulation Optimization." IEEE Signal Processing Letters 15 (2008): 729–32. http://dx.doi.org/10.1109/lsp.2008.2001821.

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