Auswahl der wissenschaftlichen Literatur zum Thema „Optimisation multistage“
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Zeitschriftenartikel zum Thema "Optimisation multistage"
Goryachkin, E. S., V. N. Matveev, G. M. Popov, O. V. Baturin und Yu D. Novikova. „Optimisation Method for Multistage Compressors“. Herald of the Bauman Moscow State Technical University. Series Mechanical Engineering, Nr. 3 (138) (September 2021): 38–59. http://dx.doi.org/10.18698/0236-3941-2021-3-38-59.
Der volle Inhalt der QuelleLiu, Y. Z. „Studies on process optimisation of multistage atomisation“. Materials Science and Technology 18, Nr. 8 (August 2002): 929–34. http://dx.doi.org/10.1179/026708302225004766.
Der volle Inhalt der QuelleKwak, Doh-Soon, Kwang-Jae Kim und Myeong-Soo Lee. „Multistage PRIM: patient rule induction method for optimisation of a multistage manufacturing process“. International Journal of Production Research 48, Nr. 12 (14.05.2009): 3461–73. http://dx.doi.org/10.1080/00207540902791827.
Der volle Inhalt der QuelleKüttner, R., und J. Majak. „Multistage Optimisation Strategy For Solving Production Planning Problems“. IFAC Proceedings Volumes 42, Nr. 2 (2009): 332–37. http://dx.doi.org/10.3182/20090506-3-sf-4003.00061.
Der volle Inhalt der QuelleVasil'ev, S. V., M. A. Kuz'mina und V. A. Mishin. „Optimisation of a multistage pulsed dye laser system“. Quantum Electronics 31, Nr. 6 (30.06.2001): 505–9. http://dx.doi.org/10.1070/qe2001v031n06abeh001989.
Der volle Inhalt der QuelleFatemifar, Soroush, Shahrokh Asadi, Muhammad Awais, Ali Akbari und Josef Kittler. „Face spoofing detection ensemble via multistage optimisation and pruning“. Pattern Recognition Letters 158 (Juni 2022): 1–8. http://dx.doi.org/10.1016/j.patrec.2022.04.006.
Der volle Inhalt der QuelleArmstrong, M., A. Galli und R. Razanatsimba. „Using multistage stochastic optimisation to manage major production incidents“. Mining Technology 121, Nr. 3 (September 2012): 125–31. http://dx.doi.org/10.1179/1743286312y.0000000010.
Der volle Inhalt der QuelleYan, Zhou, Hany Hassanin, Mahmoud Ahmed El-Sayed, Hossam Mohamed Eldessouky, JRP Djuansjah, Naser A. Alsaleh, Khamis Essa und Mahmoud Ahmadein. „Multistage Tool Path Optimisation of Single-Point Incremental Forming Process“. Materials 14, Nr. 22 (11.11.2021): 6794. http://dx.doi.org/10.3390/ma14226794.
Der volle Inhalt der QuelleLoveday, B. K., und A. L. Hemphill. „Optimisation of a multistage flotation plant using plant survey data“. Minerals Engineering 19, Nr. 6-8 (Mai 2006): 627–32. http://dx.doi.org/10.1016/j.mineng.2005.09.014.
Der volle Inhalt der QuelleSöylemez, Mehmet Sait. „On the thermo-economic optimisation of multistage flash evaporation desalination plants“. International Journal of Nuclear Governance, Economy and Ecology 4, Nr. 2 (2014): 153. http://dx.doi.org/10.1504/ijngee.2014.065935.
Der volle Inhalt der QuelleDissertationen zum Thema "Optimisation multistage"
Ghosh, Tushar Kanti. „Three dimensional modelling and optimisation of multistage collectors“. Thesis, Lancaster University, 2002. http://eprints.lancs.ac.uk/76623/.
Der volle Inhalt der QuelleTeiller, Alexandre. „Aspects algorithmiques de l'optimisation « multistage »“. Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS471.
Der volle Inhalt der QuelleN a classical combinatorial optimization setting, given an instance of a problem one needs to find a good feasible solution. However, in many situations, the data may evolve over time and one has to solve a sequence of instances. Gupta et al. (2014) and Eisenstat et al. (2014) proposed a multistage model where given a time horizon the input is a sequence of instances (one for each time step), and the goal is to find a sequence of solutions (one for each time step) reaching a trade-off between the quality of the solutions in each time step and the stability/similarity of the solutions in consecutive time steps. In Chapter 1 of the thesis, we will present an overview of optimization problems tackling evolving data. Then, in Chapter 2, the multistage knapsack problem is addressed in the offline setting. The main contribution is a polynomial time approximation scheme (PTAS) for the problem in the offline setting. In Chapter 3, the multistage framework is studied for multistage problems in the online setting. The main contribution of this chapter was the introduction of a structure for these problems and almost tight upper and lower bounds on the best-possible competitive ratio for these models. Finally in chapter 4 is presented a direct application of the multistage framework in a musical context i.e. the target-based computed-assisted orchestration problem. Is presented a theoretical analysis of the problem, with NP-hardness and approximation results as well as some experimentations
Tran, Duy-Nghi. „Programmation dynamique tropicale en optimisation stochastique multi-étapes“. Thesis, Paris Est, 2020. http://www.theses.fr/2020PESC1040.
Der volle Inhalt der QuelleIn this thesis, we are interested in the resolution by dynamic programming of Multistage Stochastic optimization Problems (MSP).In the first part, we are interested in the approximation of the value functions of a MSP as min-plus or max-plus linear combinations of basic functions. This approach can be interpreted as the tropical algebra analogue of Approximate Dynamic Programming parametric models, notably studied by Bertsekas and Powell.In the simplified framework of multistage deterministic optimisation problems, we introduce an algorithm, called Tropical Dynamic Programming (TDP), which iteratively constructs approximations of value functions as min-plus or max-plus linear combinations. At each iteration, a trajectory of states is randomly drawn and the states forming this trajectory are called trial points. Based on the current approximations of the value functions, TDP then recursively calculates, by going back in time, a new basic function to be added to the current linear min-plus or max-plus combination. The basic function added to the approximation at time t must verify two compatibility conditions: it must be tight at the t-th trial point and valid. In this way TDP avoids discretising the entire state space and tries to emancipate itself from the curse of dimensionality.Our first contribution, within the framework of deterministic multistage optimization problems, is sufficient conditions on the richness of the trial points in order to ensure almost surely the asymptotic convergence of the generated approximations to the value functions, at points of interest.In the second part, the framework of the TDP algorithm was extended to Lipschitz MSPs where the noises are finite and independent. In this framework, max-plus linear and min-plus linear approximations of the value functions are generated simultaneously. At each iteration, in a forward phase, a particular deterministic trajectory of states called the problem-child trajectory is generated. Then, in the backward phase in time, the common approximations are refined by adding basic functions that are tight and valid.Our second contribution is the proof that the difference between the max-plus and min-plus linear combinations thus generated tends towards 0 along the problem-child trajectories. This result generalises a result from Baucke, Downward and Zackeri in 2018 who proved the convergence of a similar scheme, introduced by Philpott, de Matos and Zackeri in 2013, for convex MSPs. However, the algorithmic complexity of the TDP extension presented is highly dependent on the size of the noise support of a given MSP.In the third part, we are interested in quantifying the difference between the values of two MSPs differing only in their scenario tree. Under assumptions of regularities, Pflug and Pichler showed in 2012 that the value of such MSPs is Lipschitz-continuous with respect to the Nested Distance they introduced. However, the computation of the Nested Distance requires an exponential number (w.r.t. the horizon T) of computation of optimal transport problems.Motivated by the success of Sinkhorn's algorithm for computing entropic relaxation of the optimal transport problem, as a third contribution we propose an entropic relaxation of the Nested Distance which we illustrate numerically.Finally, in order to justify the resolution by dynamic programming in more general cases of MSPs, interchange between integration and minimisation must be justified. In the fourth contribution, we establish a general interchange result between integration and minimization which includes some usual results
Guba, Nadine [Verfasser], Brigitte [Gutachter] Werners und Marion [Gutachter] Steven. „Energy portfolio optimisation under uncertainty : multistage stochastic models for optimising energy procurement and power plant operation planning / Nadine Guba ; Gutachter: Brigitte Werners, Marion Steven ; Fakultät für Wirtschaftswissenschaft“. Bochum : Ruhr-Universität Bochum, 2018. http://nbn-resolving.de/urn:nbn:de:hbz:294-61946.
Der volle Inhalt der QuelleGuba, Nadine [Verfasser], Brigitte Gutachter] Werners und Marion [Gutachter] [Steven. „Energy portfolio optimisation under uncertainty : multistage stochastic models for optimising energy procurement and power plant operation planning / Nadine Guba ; Gutachter: Brigitte Werners, Marion Steven ; Fakultät für Wirtschaftswissenschaft“. Bochum : Ruhr-Universität Bochum, 2018. http://d-nb.info/1173421351/34.
Der volle Inhalt der QuelleGuba, Nadine Verfasser], Brigitte [Gutachter] Werners und Marion [Gutachter] [Steven. „Energy portfolio optimisation under uncertainty : multistage stochastic models for optimising energy procurement and power plant operation planning / Nadine Guba ; Gutachter: Brigitte Werners, Marion Steven ; Fakultät für Wirtschaftswissenschaft“. Bochum : Ruhr-Universität Bochum, 2018. http://d-nb.info/1173421351/34.
Der volle Inhalt der QuelleHawaidi, Ebrahim A. M. „Simulation, optimisation and flexible scheduling of MSF desalination process under fouling. Optimal design and operation of MSF desalination process with brine heater and demister fouling, flexible design operation and scheduling under variable demand and seawater temperature using gPROMS“. Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5629.
Der volle Inhalt der QuelleHawaidi, Ebrahim Ali M. „Simulation, optimisation and flexible scheduling of MSF desalination process under fouling : optimal design and operation of MSF desalination process with brine heater and demister fouling, flexible design operation and scheduling under variable demand and seawater temperature using gPROMS“. Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5629.
Der volle Inhalt der QuelleMohammadi, Mehrdad. „A multi-objective optimization framework for an inspection planning problem under uncertainty and breakdown“. Thesis, Paris, ENSAM, 2015. http://www.theses.fr/2015ENAM0055/document.
Der volle Inhalt der QuelleQuality inspection in multistage production systems (MPSs) has become an issue and this is because the MPS presents various possibilities for inspection. The problem of finding the best inspection plan is an “inspection planning problem”. The main simultaneous decisions in an inspection planning problem in a MPS are: 1) which quality characteristics need to be inspected, 2) what type of inspection should be performed for the selected quality characteristics, 3) where these inspections should be performed, and 4) how the inspections should be performed. In addition, lack of information about production processes and several environmental factors has become an important issue that imposes a degree of uncertainty to the inspection planning problem. This research provides an optimization framework to plan an inspection process in a MPS, wherein, input parameters are uncertain and inspection tools and production machines are subject to breakdown. This problem is formulated through several mixed-integer mathematical programming models with the objectives of minimizing total manufacturing cost, maximizing customer satisfaction, and minimizing total production time. Furthermore, Taguchi and Monte Carlo methods are applied to cope with the uncertainties. Due to the complexity of the proposed models, meta-heuristic algorithms are employed to find optimal or near-optimal solutions. Finally, this research implements the findings and methods of the inspection planning problem in another application as hub location problem. General and detail concluding remarks are provided for both inspection and hub location problems
Nasri, Karima. „Frigo pompes à absorption multiétagées de haute performance : simulation et conception d'une maquette expérimentale“. Vandoeuvre-les-Nancy, INPL, 1997. http://www.theses.fr/1997INPL054N.
Der volle Inhalt der QuelleBuchteile zum Thema "Optimisation multistage"
Liu, Songsong, Jose M. Pinto und Lazaros G. Papageorgiou. „Medium-term planning of multistage multiproduct continuous plants using mixed integer optimisation“. In Computer Aided Chemical Engineering, 393–98. Elsevier, 2009. http://dx.doi.org/10.1016/s1570-7946(09)70066-5.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Optimisation multistage"
Engmark, Edda, Hanne Sandven, Stein-Erik Fleten und Gro Klaeboe. „Stochastic Multistage Bidding Optimisation in an Intraday Market with Limited Liquidity“. In 2018 15th International Conference on the European Energy Market (EEM). IEEE, 2018. http://dx.doi.org/10.1109/eem.2018.8469997.
Der volle Inhalt der QuelleMotie, Mohadeseh, Peyman Moein, Ramin Moghadasi und Ali Hadipour. „Separator Pressure Optimisation and Cost Evaluation of a Multistage Production Unit Using Genetic Algorithm“. In International Petroleum Technology Conference. International Petroleum Technology Conference, 2019. http://dx.doi.org/10.2523/19396-ms.
Der volle Inhalt der QuelleMotie, Mohadeseh, Peyman Moein, Ramin Moghadasi und Ali Hadipour. „Separator Pressure Optimisation and Cost Evaluation of a Multistage Production Unit Using Genetic Algorithm“. In International Petroleum Technology Conference. International Petroleum Technology Conference, 2019. http://dx.doi.org/10.2523/iptc-19396-ms.
Der volle Inhalt der QuelleLi, H., D. Kalinin, A. Bruce und T. Bukovac. „Pioneering Beetaloo Shale Appraisal: Case Study of Australia's Largest Multistage Fracturing Operation“. In Asia Pacific Unconventional Resources Symposium. SPE, 2023. http://dx.doi.org/10.2118/217282-ms.
Der volle Inhalt der QuelleWakeley, Guy R., und Ian Potts. „Origins of Loss Within a Multistage Turbine Environment Under Conditions of Partial Admission“. In ASME 1997 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/97-gt-096.
Der volle Inhalt der QuelleIbrahim, M. Z., M. M. Ibrahim, C. Findlay, T. Techanukul, M. F. Noor Hassim, K. Wongyaowarat und M. R. Ramli. „Cost and Operation Optimisation for Downhole Tractor Conveyance for Perforating in ERD Wells by Using Wireline Multistage Pressure Deployment System“. In IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition. Society of Petroleum Engineers, 2018. http://dx.doi.org/10.2118/191095-ms.
Der volle Inhalt der QuelleBaert, Lieven, Ingrid Lepot, Caroline Sainvitu, Emmanuel Chérière, Arnaud Nouvellon und Vincent Leonardon. „Aerodynamic Optimisation of the Low Pressure Turbine Module: Exploiting Surrogate Models in a High-Dimensional Design Space“. In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91570.
Der volle Inhalt der QuelleRamsden, K. W. „An Interactive Methodology for Axial Compressor Design Teaching“. In ASME 1999 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/99-gt-449.
Der volle Inhalt der QuelleAhdeema, Jamal, Morteza Haghighat Sefat und Khafiz Muradov. „Hybrid Optimization Technique Allows Dynamic Completion Design and Control in Advanced Multilateral Wells with Multiple Types of Flow Control Devices“. In SPE Offshore Europe Conference & Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215507-ms.
Der volle Inhalt der QuelleKrishnababu, Senthil, Vili Panov, Simon Jackson und Andrew Dawson. „Quick Start of an Industrial Gas Turbine Engine Through the Development of “Silent Start” VGV Schedule“. In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15983.
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