J. Bender, K. Erleben, M. Teschner (editors et al. · 2012
We present a novel approach to fluid simulation, allowing us to take into account the surface energy in a precise manner. This new approach combines a novel, topology-adaptive approach to deformable interface tracking, called the deformable simplicial complexes method (DSC) with an optimization-based, linear finite element method for solving the incompressible Euler equations. The deformable simplicial complexes track the surface of the fluid: the fluid-air interface is represented explicitly as a piecewise linear surface which is a subset of tetrahedralization of the space, such that the interface can be also represented implicitly as a set of faces separating tetrahedra marked as inside from the ones marked as outside. This representation introduces insignificant and controllable numerical diffusion, allows robust topological adaptivity and provides both a volumetric finite element mesh for solving the fluid dynamics equations as well as direct access to the interface geometry data, making inclusion of a new surface energy term feasible. Furthermore, using an unstructured mesh makes it straightforward to handle curved solid boundaries and gives us a possibility to explore several fluid-solid interaction scenarios.
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Xiaotao Wan · Purdue e-Pubs (Purdue University System) · 2004
This thesis presents surrogate model based algorithms to solve static and dynamic stochastic optimization problems under the Simulation Based Optimization (Sim-Opt) framework. For static problems, a surrogate framework comprising domain reduction, DACE (design and analysis computer experiment) and LSSVM (least square support vector machine) is proposed. Domain reduction directs the attention of the optimization to sub-regions containing good solutions and avoids spending effort equally across the whole space, DACE intelligently explores the decision space with adaptive experimental design, while LSSVM generalizes observed simulation results by extracting the embedded input vs. output relations, upon which optimization is performed to locate good decisions. Essentially, the framework builds a series of surrogate models with a gradually reduced domain and accumulated information from experimental design until good decisions are found. Applying the framework to testing functions and a supply chain optimization problem demonstrated its superiority over existing Sim-Opt algorithms. Surrogate model based Sim-Opt algorithms are also proposed to conduct risk optimization in dynamic systems with SDP (stochastic dynamic programming), a subject not addressed by any existing algorithm. The one-step back-propagation based on the Bellman equation is extended to multi-step back-propagation to enable the calculation of desired risks; the optimal decision for any state is obtained through maximizing its pseudo-utility which combines the average return with the risk to balance their trade-of. The curse-of-dimensionality associated with the pseudo-utility function is addressed with a surrogate model by generalizing the results of a sample to the whole space with LSSVM; a second level surrogate model is also devised to directly approximate the optimal policy of SDP, which replaces the online optimization in simulation sample paths with simple function evaluations and substantially reduces the computational overhead. The effectiveness of the proposed algorithms were manifested through constructing the NPV (net present value) vs. risk efficient frontiers for a pharmaceutical company making capacity decisions under various uncertainties.
Cited by 3
Yunpeng Bai, Shengdong Zhao, Antti Oulasvirta · arXiv (Cornell University) · 2026
Augmented reading systems aim to adapt text presentation to improve comprehension and task performance, yet existing approaches rely heavily on heuristics, opaque data-driven models, or repeated human involvement in the design loop. We propose framing augmented reading as a simulation-based optimization problem grounded in resource-rational models of human reading. These models instantiate a simulated reader that allocates limited cognitive resources, such as attention, memory, and time under task demands, enabling systematic evaluation of text user interfaces. We introduce two complementary optimization pipelines: an offline approach that explores design alternatives using simulated readers, and an online approach that personalizes reading interfaces in real time using ongoing interaction data. Together, this perspective enables adaptive, explainable, and scalable augmented reading design without relying solely on human testing.
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B. A. Dendrou, Stergios A. Dendrou, Elias N. Houstis · Purdue e-Pubs (Purdue University System) · 1978
Cited by 0Open access
Anh Tran, Yan Wang · 2022
Abstract not provided.
Cited by 0Open access
Karlstetter, C., Latz, A., Leiss, N. et al. · Fraunhofer-Publica (Fraunhofer-Gesellschaft) · 2013
S.52-59
Cited by 0
Russell R. Barton, Martin Meckesheimer · Handbooks in operations research and management science · 2006
Cited by 267
Syed, Hidayath Ulla, Thajuddin, Shamnath · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2016
Manufacturing companies are constantly looking for new, innovative technologies andtools to find out the real constraints and bottlenecks that impede the performance oftheir production systems. There are several approaches and methods that have beendeveloping from decades to overcome these constraints of production processes butthey are not sufficient to pinpoint the exact location and its severity. They also generallyfall short to suggest the way to implement the right actions in the right order, to avoidsub-optimizations and wastes in time and expenses. So according to recent research inusing simulation based optimization, it is believed that some more accurate and efficientmethodology for supporting decision making in production systems development andimprovement is badly needed. SCORE (Simulation-based Constraint Removal) is apromising methodology for identifying and ranking the bottlenecks of productionsystems that utilizes simulation-based multi-objective optimization (SMO), which wasdeveloped by Pehrsson (2013) as a part of his PhD work. The main principle of thisnew methodology is the application of SMO with the objectives to maximize thethroughput and minimize the number of required improvement actions simultaneously.Additionally, by using post-optimality analysis to process the generated optimizationdataset, the precise improvement actions needed to attain a certain level of performanceof the production line are automatically put into a rank order. The main aim of thisproject is therefore to apply this new technique in a real-world context, in order tounderstand how far this technique will support for decision making, by conducting asimulation-based bottleneck analysis in one of the major Volvo group trucks facilities.This is to find out the bottlenecks and optimize it in order to increase the overallproductivity. Three research questions related to the effectiveness and accuracy of themethodology will be answered through this real-world application study.
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Daman Oberoi, Alan Fern · 2006
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Hirotaka Nakayama, Masaaki Arakawa, Rie Sasaki · Optimization and Engineering · 2002
Cited by 115
Timo Korvola, Jari Lappalainen, Jukka K. Nurminen · Työväentutkimus Vuosikirja · 2019
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Jobin Puthuparampil, Pierre E. Sullivan · Noise Control Engineering Journal · 2020
Noise control of large diesel and natural gas generators is achieved through industrial mufflers. Design of such mufflers relies heavily on general guidelines. However, these guidelines are not suitable for complex mufflers; instead, computer-based optimization provides an effective means of design. Optimization of a plug flow muffler is conducted in this work with a multi-objective (transmission loss and pressure drop) finite element simulation-based optimization using the efficient global optimization (EGO) algorithm. The EGO algorithm is shown to be well suited for computationally expensive muffler optimization, performing vastly better than genetic algorithms, such as the commonly used NSGA-II algorithm.
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Lukas Reuter, Jan Ole Berndt, Ingo J. Timm · Lecture notes in computer science · 2017
Cited by 6
Kailun Feng, Shiwei Chen, Weizhuo Lu · 2018 Winter Simulation Conference (WSC) · 2018
Building construction comprises interaction and interdependence among processes. Discrete-event simulation (DES) is widely applied to model these processes interaction. To find optimal construction plans, optimization technique is usually integrated with DES. However, present simulation-optimization integrated method directly invokes simulation model within optimization algorithms, which is found significantly computationally expensive. This study proposes a machine learning based construction simulation and optimization integrated method. After trained by DES, the machine learning model accelerates simulation-optimization integration by nearly real-time providing fitness evaluation within optimization. This method was implemented into a real construction project for construction time-cost-environment optimization. Results show that proposed machine learning based method significantly reduce computing time compared with original simulation-optimization integration. Less than 1% of construction cost and time improvement were miss, while greenhouse gas emissions obtained same performance. The new method could be a more effective DES and optimization integration approach for practical engineering application.
Cited by 9
Sachin Man Bajimaya, Chang Mok Park, Gi-nam Wang · 2007
The objective of this paper is to optimize the principal extrusion variables by means of manufacturing simulation of the extrusion model. The finite element analysis of the extrusion model does not consider the manufacturing process in its modeling. Therefore, the process parameters obtained through such an analysis remains highly theoretical in that these parameters differ from the actual extrusion process parameters. Additionally, due to the nature of the extrusion process, it is often quite difficult to determine the cause of an extrusion problem and find its proper solution, particularly if it must be done quickly. The manufacturing simulation of the extrusion process prior to plant execution helps to make the actual extrusion operation more efficient because more realistic parameters may be obtained through the simulation. Additionally, troubleshooting the process becomes simpler. In this paper, we have developed the simulation model of a real extrusion plant using IGRIP. The model of the plant is simulated in IGRIP using the Graphical Simulation Language (GSL). The dynamics of the plant is also incorporated in the simulation model. Through simulation, optimized values of variables that affect the extrusion process are obtained.
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Ji Lia, Pingan Dub · 2016
Abstract. For most past scheduling problems, research condition is based on enough orders. But on the condition of production capability more than the number of the orders, how to accomplish the limited orders to make the production cost as small as possible is a significantly considered question. Based on Plant Simulation and its Genetic Algorithm, a method of optimizing production cost when scheduling is put forward. This research realizes the relevant scheduling to minimize the production cost and propose a solution for these problems of complex combined cost optimization.
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Jaroslav Sklenar, Pavel Popelab · Procedia Computer Science · 2010
Cited by 7Open access
2024
The integration of drone technology with 5G networks presents novel opportunities for enhancing wireless communication systems.This paper explores the application of beamforming optimization techniques in dynamic environments, specifically focusing on moving drones in a simulated environment based on the DeepMIMO O1 scenario.By leveraging the unique properties of the O1 drone setup of DeepMIMO simulation environment, which simulates realistic urban mobility patterns at millimeter-wave (mmWave) frequencies, we propose a novel beamforming algorithm designed to optimize the signal quality and stability in highly mobile aerial networks.Key performance metrics used in this study include Signal-to-Noise Ratio (SNR), battery consumption, and power consumption of both the drones and the base station.Our findings indicate that the adaptive beamforming algorithm not only enhances the SNR and reduces power consumption but also optimizes battery usage compared to conventional beamforming methods.This study enhances the understanding of mmWave beamforming dynamics in aerial scenarios but also lays the groundwork for future advancements in drone-based communication networks.
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Aleksandrina Goeva, Henry Lam, Huajie Qian et al. · Operations Research · 2019
Studies on simulation input uncertainty are often built on the availability of input data. In this paper, we investigate an inverse problem where, given only the availability of output data, we nonparametrically calibrate the input models and other related performance measures of interest. We propose an optimization-based framework to compute statistically valid bounds on input quantities. The framework utilizes constraints that connect the statistical information of the real-world outputs with the input–output relation via a simulable map. We analyze the statistical guarantees of this approach from the view of data-driven distributionally robust optimization, and show how they relate to the function complexity of the constraints arising in our framework. We investigate an iterative procedure based on a stochastic quadratic penalty method to approximately solve the resulting optimization. We conduct numerical experiments to demonstrate our performances in bounding the input models and related quantities.
Cited by 16
Tianjian Cao · Dissertationes Forestales · 2010
Forest management is facing new challenges under climate change. By adjusting thinning regimes, conventional forest management can be adapted to various objectives of utilization of forest resources, such as wood quality, forest bioenergy, and carbon sequestration. This thesis aims to develop and apply a simulation-optimization system as a tool for an interdisciplinary understanding of the interactions between wood science, forest ecology, and forest economics.
Cited by 4Open access