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Journal articles on the topic 'Multistage optimization'

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Published: 25 May 2024

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1

Nusinovich, G. S., B. Levush, and O. Dumbrajs. "Optimization of multistage harmonic gyrodevices." Physics of Plasmas 3, no. 8 (August 1996): 3133–44. http://dx.doi.org/10.1063/1.871589.

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2

Aleksandrov, V. Yu, and K. K. Klimovskii. "Optimization of multistage gas ejectors." Thermal Engineering 56, no. 9 (September 2009): 790–94. http://dx.doi.org/10.1134/s0040601509090146.

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3

Rao, S. S., and H. R. Eslampour. "Multistage Multiobjective Optimization of Gearboxes." Journal of Mechanisms, Transmissions, and Automation in Design 108, no. 4 (December 1, 1986): 461–68. http://dx.doi.org/10.1115/1.3258755.

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The problems of kinematic and strength designs of multispeed gearboxes are formulated as multiobjective optimization problems. In the kinematic design stage, the speeds of all the shafts, the number of teeth on various gears and the gear module are selected so as to minimize the deviation of output speeds from specified values and the overall center distance of the gearbox. In the strength design stage, the face widths of the various gear pairs are chosen so as to minimize the volume of the material of the gears and to maximize the power transmitted by the gearbox. A goal programming approach is suggested for the solution of the multiobjective nonlinear constrained optimization problem by treating the ideal feasible solutions as the goals for the corresponding objective functions. The utility of the resulting computer program is demonstrated through the design of six- and 18-speed gearboxes. The present methodology offers the feasibility of automating the design of gearboxes by incorporating all the (conflicting) design requirements and objectives.
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4

Ghosh, T. K., and R. G. Carter. "Optimization of Multistage Depressed Collectors." IEEE Transactions on Electron Devices 54, no. 8 (August 2007): 2031–39. http://dx.doi.org/10.1109/ted.2007.900003.

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5

Rubchinsky, Alexander. "Choice functions in multistage optimization." Journal of Multi-Criteria Decision Analysis 3, no. 2 (August 1994): 105–17. http://dx.doi.org/10.1002/mcda.4020030205.

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6

Waleed Hammad, Azhar, and Faiz Faig Showkat. "Multistage Ant System Optimization Algorithm." Engineering and Technology Journal 29, no. 10 (July 1, 2011): 1893–901. http://dx.doi.org/10.30684/etj.29.10.3.

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7

Moslemi, Amir, and Mirmehdi Seyyed-Esfahani. "Robust optimization of multistage process: response surface and multi-response optimization approaches." International Journal of Nonlinear Sciences and Numerical Simulation 23, no. 2 (November 26, 2021): 163–75. http://dx.doi.org/10.1515/ijnsns-2017-0003.

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Abstract A multistage system refers to a system contains multiple components or stages which are necessary to finish the final product or service. To analyze these problems, the first step is model building and the other is optimization. Response surfaces are used to model multistage problem as an efficient procedure. One regular approach to estimate a response surface using experimental results is the ordinary least squares (OLS) method. OLS method is very sensitive to outliers, so some multivariate robust estimation methods have been discussed in the literature in order to estimate the response surfaces accurately such as multivariate M-estimators. In optimization phase, multi-response optimization methods such as global criterion (GC) method and ε-constraints approaches are different methods to optimize the multi-objective-multistage problems. An example of the multistage problem had been estimated considering multivariate robust approaches, besides applying multi-response optimization approaches. The results show the efficiency of the proposed approaches.
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8

Bistrickas, V. J., and N. Šimelienė. "Discrete Multistage Optimization and Hierarchical Market." Nonlinear Analysis: Modelling and Control 11, no. 2 (May 18, 2006): 149–56. http://dx.doi.org/10.15388/na.2006.11.2.14755.

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New simple form of mixed solutions is described by bilinear continuous optimization processes. It enables investigate an analytic solutions and the connection between discrete and continuous optimization processes. Connection between discrete and continuous processes is stochastic. Discrete optimization processes are used for the control works in levels and groups of the hierarchical market. Equilibrium between local and global levels of works is investigated in hierarchical market.
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9

Bertsimas, Dimitris, and Constantine Caramanis. "Finite Adaptability in Multistage Linear Optimization." IEEE Transactions on Automatic Control 55, no. 12 (December 2010): 2751–66. http://dx.doi.org/10.1109/tac.2010.2049764.

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10

Wang, Hao, Xiaohui Lei, Soon-Thiam Khu, and Lixiang Song. "Optimization of Pump Start-Up Depth in Drainage Pumping Station Based on SWMM and PSO." Water 11, no. 5 (May 13, 2019): 1002. http://dx.doi.org/10.3390/w11051002.

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The pumps in multistage drainage pumping stations are often subject to frequent start-up and shutoffs during operation because of unreasonable start-up depths of the pumps; this will reduce the service lives of the pumps. To solve this problem, an optimization method for minimizing pump start-up and shutoff times is proposed. In this method, the operation of pumps in pumping station was optimized by constructing a mathematical optimization model. The storm water management model (SWMM) and particle swarm optimization (PSO) method were used to solve the problem and the optimal start-up depth of each pump is obtained. Nine pumping stations in Beijing were selected as a case study and this method was applied for multistage pumping station optimization and single pumping station optimization in the case study. Results from the case study demonstrate that the multistage pumping station optimization acquired a small number of pump start-up/shutoff times, which were from 8 to 114 in different rainfall scenarios. Compared with the multistage pumping station optimization, the single pumping station optimization had a bigger number of pump start-up/shutoff times, which were from 1 to 133 times, and the pump operating time was also longer, from 72 min to 7542 min. Therefore, the multistage pumping station optimization method was more suitable to reduce the frequency of pump start-up/shutoffs.
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11

Shi, Shuo, Lan Lan Guo, Jian Ting Sun, and Guang Sheng Du. "Optimization Design of the Spinning Multistage Centrifugal." Applied Mechanics and Materials 644-650 (September 2014): 567–70. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.567.

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This paper analyses the structure of the original spinning multistage centrifugal fan. Because of low pressure and low efficiency, optimization design was carried out for the original fan. Based on the aerodynamic calculations theory, the structure and pneumatic of single stage was studied. The influence factors are Inlet-flow loss, equivalent divergent angle, pre-swirl, Resonant frequency etc. Pneumatic recalculation and experiment are carried out for the multistage fan. Results show that after modification pressure of the multistage fan has increased raises 15.3 %.
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12

Xiao, Guoxian, Stephen Malkin, and Kourosh Danai. "Autonomous System for Multistage Cylindrical Grinding." Journal of Dynamic Systems, Measurement, and Control 115, no. 4 (December 1, 1993): 667–72. http://dx.doi.org/10.1115/1.2899194.

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An optimization strategy is presented for cylindrical plunge grinding operations. The optimization strategy is designed to minimize cycle time while satisfying production constraints. Monotonicity analysis together with local linearization are used to simplify the non-linear optimization problem and determine the process variables for the optimal cycle. At the end of each cycle, the uncertain parameters of the process are estimated from sensory data so as to provide a more accurate estimation of the optimal process variables for the subsequent cycle. The optimization strategy is validated both in simulation and for actual grinding tests.
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13

Jarosz, Piotr, Jan Kusiak, Stanisław Małecki, Piotr Oprocha, Łukasz Sztangret, and Marek Wilkus. "A Methodology for Optimization in Multistage Industrial Processes: A Pilot Study." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/182679.

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The paper introduces a methodology for optimization in multistage industrial processes with multiple quality criteria. Two ways of formulation of optimization problem and four different approaches to solve the problem are considered. Proposed methodologies were tested first on a virtual process described by benchmark functions and next were applied in optimization of multistage lead refining process.
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14

Dentcheva, Darinka, and Andrzej Ruszczyński. "Subregular recourse in nonlinear multistage stochastic optimization." Mathematical Programming 189, no. 1-2 (January 13, 2021): 249–70. http://dx.doi.org/10.1007/s10107-020-01612-z.

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15

Pichler, Alois, and Alexander Shapiro. "Mathematical Foundations of Distributionally Robust Multistage Optimization." SIAM Journal on Optimization 31, no. 4 (January 2021): 3044–67. http://dx.doi.org/10.1137/21m1390517.

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16

Hamersztein, A., A. Davidesko, and N. Tzabar. "Numerical optimization of a multistage sorption compressor." Journal of Physics: Conference Series 2116, no. 1 (November 1, 2021): 012113. http://dx.doi.org/10.1088/1742-6596/2116/1/012113.

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Abstract Sorption compressors are driven by thermal cycles and have no moving parts, excluding some passive check valves. Such compressors are suitable for powering Joule-Thomson (JT) cryocoolers and can provide reliable and vibration free active cooling system with a potential for high reliability and long operating life. The thermal cycle consists of cooling and heating a sorbent material which is installed in a sorption cell, where the heating is obtained by an inner electric heater and cooling is obtained by the surrounding via the sorption cell envelope. The investigation and optimization of the sorption cells were conducted in previous work, at steady state conditions, by a one-dimensional heat and mass transfer numerical model. The current paper presents a dynamic numerical model of sorption compressors which consist of several sorption cells. The numerical model allows one to three compression stages, with any number of sorption cells at each stage. The model enables the investigation of dimensional parameters and operational parameters, and provides the low and high pressures, pressure fluctuations, and compressor’s efficiency. The current investigation focuses on a three-stage compressor for nitrogen, with low and high pressures of 0.2 and 8 MPa, respectively, and a mass flow rate of about 11 mg/s.
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17

Bampis, Evripidis, Dimitris Christou, Bruno Escoffier, Alexander Kononov, and Kim Thang Nguyen. "A simple rounding scheme for multistage optimization." Theoretical Computer Science 907 (March 2022): 1–10. http://dx.doi.org/10.1016/j.tcs.2022.01.009.

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18

Norsell, Martin. "Multistage Trajectory Optimization with Radar Range Constraints." Journal of Aircraft 42, no. 4 (July 2005): 849–57. http://dx.doi.org/10.2514/1.8544.

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19

Joghataie, Abdolreza, and Majid Ghasemi. "Fuzzy Multistage Optimization of Large-Scale Trusses." Journal of Structural Engineering 127, no. 11 (November 2001): 1338–47. http://dx.doi.org/10.1061/(asce)0733-9445(2001)127:11(1338).

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20

Kiyota, Masaru, Upali Vandebona, and Hiroshi Tanoue. "Multistage Optimization of Reconstruction Sequence of Highways." Journal of Transportation Engineering 125, no. 5 (September 1999): 456–62. http://dx.doi.org/10.1061/(asce)0733-947x(1999)125:5(456).

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21

Bohlin, Markus, Sara Gestrelius, Florian Dahms, Matúš Mihalák, and Holger Flier. "Optimization Methods for Multistage Freight Train Formation." Transportation Science 50, no. 3 (August 2016): 823–40. http://dx.doi.org/10.1287/trsc.2014.0580.

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22

Lin, C.-Y., and W.-T. Chen. "Stochastic multistage algorithms for multimodal structural optimization." Computers & Structures 74, no. 2 (January 2000): 233–41. http://dx.doi.org/10.1016/s0045-7949(99)00016-4.

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23

Khalil, H., and K. Rose. "Multistage vector quantizer optimization for packet networks." IEEE Transactions on Signal Processing 51, no. 7 (July 2003): 1870–79. http://dx.doi.org/10.1109/tsp.2003.812731.

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24

Davari-Ardakani, Hamed, Majid Aminnayeri, and Abbas Seifi. "Multistage portfolio optimization with stocks and options." International Transactions in Operational Research 23, no. 3 (April 30, 2015): 593–622. http://dx.doi.org/10.1111/itor.12174.

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25

Galkina, O. A. "Investigation of Multistage Stochastic Portfolio Optimization Problems." Cybernetics and Systems Analysis 52, no. 6 (November 2016): 857–66. http://dx.doi.org/10.1007/s10559-016-9887-1.

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26

Pflug, Georg Ch, and Alois Pichler. "A Distance For Multistage Stochastic Optimization Models." SIAM Journal on Optimization 22, no. 1 (January 2012): 1–23. http://dx.doi.org/10.1137/110825054.

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27

Reus, Lorenzo, and John M. Mulvey. "Multistage stochastic optimization for private equity investments." Journal of Asset Management 16, no. 5 (July 16, 2015): 342–62. http://dx.doi.org/10.1057/jam.2015.20.

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28

Babad, H. R. "An infinite-horizon multistage dynamic optimization problem." Journal of Optimization Theory and Applications 86, no. 3 (September 1995): 529–52. http://dx.doi.org/10.1007/bf02192158.

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29

Sun, Y., L. Y. Li, and X. Y. Dong. "Multistage affinity cross-flow filtration: process optimization." Bioprocess Engineering 16, no. 4 (1997): 229. http://dx.doi.org/10.1007/s004490050313.

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30

Kunde, Christian, and Achim Kienle. "Global optimization of multistage binary separation networks." Chemical Engineering and Processing - Process Intensification 131 (September 2018): 164–77. http://dx.doi.org/10.1016/j.cep.2018.06.024.

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31

Petrová, Barbora. "Multistage portfolio optimization with multivariate dominance constraints." Computational Management Science 16, no. 1-2 (August 20, 2018): 17–46. http://dx.doi.org/10.1007/s10287-018-0334-9.

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32

Escudero, Laureano F., María Araceli Garín, and Aitziber Unzueta. "Cluster Lagrangean decomposition in multistage stochastic optimization." Computers & Operations Research 67 (March 2016): 48–62. http://dx.doi.org/10.1016/j.cor.2015.09.005.

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33

Lan, Guanghui, and Alexander Shapiro. "Numerical Methods for Convex Multistage Stochastic Optimization." Foundations and Trends® in Optimization 6, no. 2 (2024): 63–144. http://dx.doi.org/10.1561/2400000044.

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34

Chen, Yue, Jiwen Cui, and Xun Sun. "A Vibration Suppression Method for the Multistage Rotor of an Aero-Engine Based on Assembly Optimization." Machines 9, no. 9 (September 5, 2021): 189. http://dx.doi.org/10.3390/machines9090189.

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The assembly quality of the multistage rotor is an essential factor affecting its vibration level. The existing optimization methods for the assembly angles of the rotors at each stage can ensure the concentricity and unbalance meet the requirements, but it cannot directly ensure its vibration responses meet the indexes. Therefore, in this study, we first derived the excitation formulas of the geometric and mass eccentricities on the multistage rotor and introduced it into the dynamics model of the multistage rotor system. Then, the coordinate transfer model of the geometric and mass eccentricities errors, including assembly angles of the rotors at all stages, was established. Moreover, the mathematical relationship between the assembly angles of the rotors at all stages and the nodal vibration responses was established by combining the error transfer model with the dynamics model of the multistage rotor system. Furthermore, an optimization function was developed, which takes the assembly angles as the optimization variables and the maximum vibration velocity at the bearings as the optimization objective. Finally, a simplified four-stage high-pressure rotor system was assembled according to the optimal assembly angles calculated in the simulations. The experimental results showed that the maximum vibration velocity at the bearings under the optimal assembly was reduced by 69.6% and 45.5% compared with that under the worst assembly and default assembly. The assembly optimization method proposed in this study has a significant effect on the vibration suppression of the multistage rotor of an aero-engine.
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35

Yao, Wen, Xiaoqian Chen, Qi Ouyang, and Michel van Tooren. "A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization." Structural and Multidisciplinary Optimization 45, no. 4 (September 27, 2011): 559–74. http://dx.doi.org/10.1007/s00158-011-0714-z.

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36

Wu, Daqing, and Jianguo Zheng. "A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization." Discrete Dynamics in Nature and Society 2012 (2012): 1–22. http://dx.doi.org/10.1155/2012/578064.

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A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.
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37

Feng, Wei, Yiping Feng, and Qi Zhang. "Multistage robust mixed-integer optimization under endogenous uncertainty." European Journal of Operational Research 294, no. 2 (October 2021): 460–75. http://dx.doi.org/10.1016/j.ejor.2021.01.048.

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38

Goerigk, Marc, and Michael Hartisch. "Multistage robust discrete optimization via quantified integer programming." Computers & Operations Research 135 (November 2021): 105434. http://dx.doi.org/10.1016/j.cor.2021.105434.

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39

Shen, Jiangtao, Peng Wang, Huachao Dong, Jinglu Li, and Wenxin Wang. "A multistage evolutionary algorithm for many-objective optimization." Information Sciences 589 (April 2022): 531–49. http://dx.doi.org/10.1016/j.ins.2021.12.096.

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40

Chen, Shikui, Ying Xiong, and Wei Chen. "Multiresponse and Multistage Metamodeling Approach for Design Optimization." AIAA Journal 47, no. 1 (January 2009): 206–18. http://dx.doi.org/10.2514/1.38187.

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41

Huang, Yongxi, and Fei Xie. "Multistage Optimization of Sustainable Supply Chain of Biofuels." Transportation Research Record: Journal of the Transportation Research Board 2502, no. 1 (January 2015): 89–98. http://dx.doi.org/10.3141/2502-11.

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42

Bertsimas, Dimitris, and Iain Dunning. "Multistage Robust Mixed-Integer Optimization with Adaptive Partitions." Operations Research 64, no. 4 (August 2016): 980–98. http://dx.doi.org/10.1287/opre.2016.1515.

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43

Zhang, Roxin, Bao Truong, and Qinghong Zhang. "Multistage hierarchical optimization problems with multi-criterion objectives." Journal of Industrial & Management Optimization 7, no. 1 (2011): 103–15. http://dx.doi.org/10.3934/jimo.2011.7.103.

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44

WENTAO, WANG. "A MULTISTAGE DECISION OPERATOR METHOD FOR ENGINEERING OPTIMIZATION." Engineering Optimization 15, no. 4 (June 1990): 267–80. http://dx.doi.org/10.1080/03052159008941157.

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45

Xiao, J. C., Z. Lu, and K. J. Ma. "Multistage and multivariable optimization for hybrid spatial structures." IOP Conference Series: Materials Science and Engineering 10 (June 1, 2010): 012190. http://dx.doi.org/10.1088/1757-899x/10/1/012190.

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46

Swamy, Chaitanya, and David B. Shmoys. "Sampling-Based Approximation Algorithms for Multistage Stochastic Optimization." SIAM Journal on Computing 41, no. 4 (January 2012): 975–1004. http://dx.doi.org/10.1137/100789269.

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47

Zeng, Guo-Qiang, Yong-Zai Lu, and Wei-Jie Mao. "Multistage extremal optimization for hard travelling salesman problem." Physica A: Statistical Mechanics and its Applications 389, no. 21 (November 2010): 5037–44. http://dx.doi.org/10.1016/j.physa.2010.07.018.

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48

Buzzi-Ferraris, Guido, and Enrico Tronconi. "Operational optimization and sensitivity analysis of multistage separators." Industrial & Engineering Chemistry Process Design and Development 24, no. 1 (January 1985): 112–18. http://dx.doi.org/10.1021/i200028a020.

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49

Huang, Yongxi, Chien-Wei Chen, and Yueyue Fan. "Multistage optimization of the supply chains of biofuels." Transportation Research Part E: Logistics and Transportation Review 46, no. 6 (November 2010): 820–30. http://dx.doi.org/10.1016/j.tre.2010.03.002.

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50

Martins, Gustavo C., and Wouter A. Serdijn. "Multistage Complex-Impedance Matching Network Analysis and Optimization." IEEE Transactions on Circuits and Systems II: Express Briefs 63, no. 9 (September 2016): 833–37. http://dx.doi.org/10.1109/tcsii.2016.2534738.

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