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Journal articles on the topic 'Optimal control management'

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Author: Grafiati

Published: 11 March 2023

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1

James, M. R. "Optimal Quantum Control Theory." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (May 3, 2021): 343–67. http://dx.doi.org/10.1146/annurev-control-061520-010444.

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This article explains some fundamental ideas concerning the optimal control of quantum systems through the study of a relatively simple two-level system coupled to optical fields. The model for this system includes both continuous and impulsive dynamics. Topics covered include open- and closed-loop control, impulsive control, open-loop optimal control, quantum filtering, and measurement feedback optimal control.

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2

Oliynyk, Viktor, Fedir Zhuravka, Tetiana Bolgar, and Olha Yevtushenko. "Optimal control of continuous life insurance model." Investment Management and Financial Innovations 14, no. 4 (December 8, 2017): 21–29. http://dx.doi.org/10.21511/imfi.14(4).2017.03.

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The problems of mixed life insurance and insurance in the case of death are considered in the article. The actuarial present value of life insurance is found by solving a system of differential equations. The cases of both constant effective interest rates and variables, depending on the time interval, are examined. The authors used the Pontryagin maximum principle method as the most efficient one, in order to solve the problem of optimal control of the mixed life insurance value. The variable effective interest rate is considered as the control parameter. Some numerical results were given.

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3

Alhuthali, Ahmed, Adedayo Oyerinde, and Akhil Datta-Gupta. "Optimal Waterflood Management Using Rate Control." SPE Reservoir Evaluation & Engineering 10, no. 05 (October 1, 2007): 539–51. http://dx.doi.org/10.2118/102478-pa.

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Summary Field-scale rate optimization problems often involve highly complex reservoir models, production-and-facilities related constraints, and a large number of unknowns. These factors make optimal reservoir management through rate- and flood-front control difficult without efficient optimization tools. Some aspects of the optimization problem have been studied before mainly using an optimal control theory. However, the applications to date have been rather limited to small problems because of the computation time and the complexities associated with the formulation and solution of adjoint equations. Field-scale rate optimization for maximizing waterflood sweep efficiency under realistic field conditions has remained largely unexplored. This paper proposes a practical and efficient approach for computing optimal injection and production rates, thereby managing the waterflood front to maximize sweep efficiency and delaying the arrival time to minimize water cycling. Our work relies on equalizing the arrival times of the waterflood front at all producers within selected subregions of a waterflood project. The arrival-time optimization has favorable quasilinear properties, and the optimization proceeds smoothly even if our initial conditions are far from the solution. Furthermore, the sensitivity of the arrival time with respect to injection and production rates can be calculated analytically using a single-flow simulation. This makes our approach computationally efficient and suitable for large-scale field applications. The arrival time optimization ensures appropriate rate allocation and flood-front management by delaying the water breakthrough at the producing wells. Several examples are presented to support the robustness and efficiency of the proposed optimization scheme. These include several 2D-synthetic examples for validation purposes and a 3D field application. In addition, we demonstrate the potential of the approach to optimize the flow profile along injection/production segments of horizontal-smart wells. Introduction Waterflooding is by far the most commonly used method to improve oil recovery after primary depletion. In spite of its many favorable characteristics, reservoir heterogeneity—particularly permeability contrast—can have an adverse impact on the performance of waterflooding. The presence of high-permeability streaks can severely reduce the sweep efficiency, leading to an early water arrival at the producers and bypassed oil. Also, an increased cost is associated with water recycling and handling. One approach to counteract the impact of heterogeneity and improve waterflood sweep efficiency is optimal rate allocation to the injectors and producers (Asheim 1988; Sudaryanto and Yortsos 2001; Brouwer et al. 2001; Brouwer and Jansen 2004; Grinestaff 1999; Grinestaff and Caffrey 2000). Through optimal rate control, we can manage the propagation of the flood front, delay water breakthrough at the producers, and also increase the recovery efficiency. Previous efforts to optimize waterflooding relied on optimal control theorem to allocate injection/production rates for fixed well configurations. Asheim (1988) investigated the optimization of waterflood based on maximizing net present value (NPV) for multiple vertical injectors and one producer where the rate profiles change throughout the optimization time. Sudaryanto and Yortsos (2001) used maximizing the displacement efficiency at water breakthrough as the objective for the optimization with two injectors and one producer. The optimal injection policy was found to be bang bang type. That is, the injectors were operated only at their extreme values—either at the maximum allowable injection rate or fully shut. The optimization then involved finding the switch time between the two injectors to ensure simultaneous water arrival at the producing well. Brouwer et al. (2001) studied the static optimization of waterflooding with two horizontal smart wells containing permanent downhole well-control valves and measurement equipment. The static optimization implies that the flow rates of the inflow-control valves (ICVs) along the well segments were kept constant during the waterflooding process until the water arrived at the producer. Various heuristic algorithms were utilized to minimize the impact of high-permeability streaks on the waterflood performance through rate control. The results indicated that the optimal rate allocation can be obtained by reducing the distribution of water-arrival times at various segments along the producer. Subsequently, Brouwer and Jansen (2004) extended their work to dynamic optimization of waterflooding with smart wells using the optimal control theory. The optimization was performed on one horizontal producer and one horizontal injector. Each well is equipped with 45 ICVs. The objective was to maximize the NPV, and it was achieved through changing the rate profile along the well segments throughout the optimization period. Both rate-constrained and bottomhole-pressure-constrained well conditions were studied.

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4

Casola, William H., Rangesan Narayanan, Christopher Duffy, and A. Bruce Bishop. "Optimal Control Model for Groundwater Management." Journal of Water Resources Planning and Management 112, no. 2 (March 1986): 183–97. http://dx.doi.org/10.1061/(asce)0733-9496(1986)112:2(183).

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5

Mukuddem-Petersen, Janine, and Mark A. Petersen. "Bank management via stochastic optimal control." Automatica 42, no. 8 (August 2006): 1395–406. http://dx.doi.org/10.1016/j.automatica.2006.03.012.

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6

Tyagi, N. K. "Optimal Water Management Strategies for Salinity Control." Journal of Irrigation and Drainage Engineering 112, no. 2 (May 1986): 81–97. http://dx.doi.org/10.1061/(asce)0733-9437(1986)112:2(81).

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7

Wagner, Michael R. "Robust Inventory Management: An Optimal Control Approach." Operations Research 66, no. 2 (April 2018): 426–47. http://dx.doi.org/10.1287/opre.2017.1669.

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8

Delprat, Sebastien, Theo Hofman, and Sebastien Paganelli. "Hybrid Vehicle Energy Management: Singular Optimal Control." IEEE Transactions on Vehicular Technology 66, no. 11 (November 2017): 9654–66. http://dx.doi.org/10.1109/tvt.2017.2746181.

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9

Gatsis, Konstantinos, Alejandro Ribeiro, and George J. Pappas. "Optimal Power Management in Wireless Control Systems." IEEE Transactions on Automatic Control 59, no. 6 (June 2014): 1495–510. http://dx.doi.org/10.1109/tac.2014.2305951.

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10

Shastri, Yogendra, Urmila Diwekar, and Heriberto Cabezas. "Optimal Control Theory for Sustainable Environmental Management." Environmental Science & Technology 42, no. 14 (July 2008): 5322–28. http://dx.doi.org/10.1021/es8000807.

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11

Ghezzi, Luca L. "Bond management and max–min optimal control." Applied Mathematics and Computation 112, no. 1 (June 2000): 33–40. http://dx.doi.org/10.1016/s0096-3003(99)00033-8.

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12

Corriga, G., D. Salimbeni, S. Sanna, and G. Usai. "An optimal control problem in forest management." Applied Mathematical Modelling 12, no. 3 (June 1988): 328–32. http://dx.doi.org/10.1016/0307-904x(88)90041-8.

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13

Morales Medina, Alejandro Ivan, Falco Creemers, Erjen Lefeber, and Nathan van de Wouw. "Optimal Access Management for Cooperative Intersection Control." IEEE Transactions on Intelligent Transportation Systems 21, no. 5 (May 2020): 2114–27. http://dx.doi.org/10.1109/tits.2019.2913589.

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14

Be´nard, C., B. Guerrier, and M. M. Rosset-Loue¨rat. "Optimal Building Energy Management: Part II—Control." Journal of Solar Energy Engineering 114, no. 1 (February 1, 1992): 13–22. http://dx.doi.org/10.1115/1.2929976.

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15

Boucher, Randy, Wei Kang, and Qi Gong. "Galerkin Optimal Control." Journal of Optimization Theory and Applications 169, no. 3 (March 21, 2016): 825–47. http://dx.doi.org/10.1007/s10957-016-0918-x.

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16

Piazza, Adriana, and Santanu Roy. "Deforestation and optimal management." Journal of Economic Dynamics and Control 53 (April 2015): 15–27. http://dx.doi.org/10.1016/j.jedc.2015.01.004.

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17

Biswas, Haider, and Ahad Ali. "Production and process management: An optimal control approach." Yugoslav Journal of Operations Research 26, no. 3 (2016): 331–42. http://dx.doi.org/10.2298/yjor141015008k.

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Optimal control and efficient management of industrial products are the key for sustainable development in industrial and process engineering. It is well-known that proper maintenance of process performance, ensuring the quality products after a long time operation of the system, is desirable in any industry. Nonlinear dynamical systems may play crucial role to appropriately design the model and obtain optimal control strategy in production and process management. This paper deals with a mathematical model in terms of ordinary differential equations (ODEs) that describe control of production and process arising in industrial engineering. The optimal control technique in the form of maximum principle, used to control the quality products in the operation processes, is applied to analyze the model. It is shown that the introduction of state constraint can be advantageous for obtaining good products during the longer operation process. We investigate the model numerically, using some known nonlinear optimal control solvers, and we present the simulation results to illustrate the significance of introducing state constraint onto the dynamics of the model.

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18

Miller, Boris M., and Daniel J. McInnes. "Management of dam systems via optimal price control." Procedia Computer Science 4 (2011): 1373–82. http://dx.doi.org/10.1016/j.procs.2011.04.148.

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19

Zhao, Bing, and Larry W. Mays. "Estuary Management by Stochastic Linear Quadratic Optimal Control." Journal of Water Resources Planning and Management 121, no. 5 (September 1995): 382–91. http://dx.doi.org/10.1061/(asce)0733-9496(1995)121:5(382).

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20

Heymann, Benjamin, J. Frédéric Bonnans, Pierre Martinon, Francisco J. Silva, Fernando Lanas, and Guillermo Jiménez-Estévez. "Continuous optimal control approaches to microgrid energy management." Energy Systems 9, no. 1 (January 5, 2017): 59–77. http://dx.doi.org/10.1007/s12667-016-0228-2.

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21

Zhang, Qing Feng. "Optimal Control of Air Traffic Networks." Advanced Materials Research 945-949 (June 2014): 3300–3303. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.3300.

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This article will focus on the study of the dynamic dead reckoning algorithm .Dead reckoning technology is the basis for high- traffic and high -density complex airspace management , air traffic is an important component of automated decision-making system . In airspace management , all of the traffic management strategies can be generated by the aircraft 's forecast track . Depending on the precise spatial location and route on expected over time , dead reckoning implementation will significantly reduce the uncertainty in the future of aircraft flight paths , which makes the airspace and airport resources are efficiently used , the safety of an aircraft in the airspace the problem is further protection.

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22

Gammell, Jonathan D., and Marlin P. Strub. "Asymptotically Optimal Sampling-Based Motion Planning Methods." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (May 3, 2021): 295–318. http://dx.doi.org/10.1146/annurev-control-061920-093753.

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Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This article summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.

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23

Abdel-Hameed, Mohamed, and Yasmin Nakhi. "Optimal control of dams." Applied Stochastic Models and Data Analysis 11, no. 1 (March 1995): 25–34. http://dx.doi.org/10.1002/asm.3150110105.

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24

Sharomi, Oluwaseun, and Tufail Malik. "Optimal control in epidemiology." Annals of Operations Research 251, no. 1-2 (March 20, 2015): 55–71. http://dx.doi.org/10.1007/s10479-015-1834-4.

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25

Sharp, J. A. "Capital Investment -- An Optimal Control Perspective." Journal of the Operational Research Society 41, no. 11 (November 1990): 1053. http://dx.doi.org/10.2307/2582901.

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26

Sharp, J. A. "Capital Investment—An Optimal Control Perspective." Journal of the Operational Research Society 41, no. 11 (November 1, 1990): 1053–63. http://dx.doi.org/10.1038/sj/jors/0411107.

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27

Sharp, J. A. "Capital Investment — An Optimal Control Perspective." Journal of the Operational Research Society 41, no. 11 (November 1990): 1053–63. http://dx.doi.org/10.1057/jors.1990.164.

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28

Wu, C. Z., and K. L. Teo. "Global impulsive optimal control computation." Journal of Industrial & Management Optimization 2, no. 4 (2006): 435–50. http://dx.doi.org/10.3934/jimo.2006.2.435.

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29

Hampton, Michelle. "Maintain optimal staffing with position control." Nursing Management (Springhouse) 48, no. 1 (January 2017): 7–8. http://dx.doi.org/10.1097/01.numa.0000511190.33666.0b.

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30

Sun, Pei Hong, Lei Tang, and Li Ying Tang. "Application of Optimal Control in Inventory Management of Production." Applied Mechanics and Materials 29-32 (August 2010): 2503–8. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2503.

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Countering the inventory management problem of manufacturing enterprise, according to the optimal control theory, considering the numbers of products as control variables and the stocks as the state variables, this essay establishes systemic real-time dynamic model, gives the objective function, and makes use of dynamic programming method to solve the optimal control and obtains the optimal inventory, which provides a theoretical foundation for the production and inventory management of manufacturing enterprise.

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31

Westphal, H. "On the Optimal Supervisory Control of Plant-Wide Control and Management Systems." IFAC Proceedings Volumes 28, no. 10 (July 1995): 697–702. http://dx.doi.org/10.1016/s1474-6670(17)51601-8.

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32

Shah, Sudhir A. "Optimal management of durable pollution." Journal of Economic Dynamics and Control 29, no. 6 (June 2005): 1121–64. http://dx.doi.org/10.1016/j.jedc.2004.07.001.

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33

Yu, Furong, Wenxi Lu, Ping Li, Xin Xin, and Jun Li. "Dynamic optimal control for groundwater optimization management with covariates." Journal of Hydroinformatics 14, no. 2 (June 30, 2011): 386–94. http://dx.doi.org/10.2166/hydro.2011.076.

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It is well known that obtaining optimal solutions for groundwater management models with covariates is a challenging task, especially for dynamic planning and management. Here, a theory and method of dealing with mutual-feed joint variation in groundwater management models is described. Specifically, an equation expressing the inherent connection between covariates and groundwater level was developed. This equation was integrated into a mathematical simulation model of groundwater, after which a groundwater dynamic optimization management model with covariates was constructed using the state transition equation method and solved with differential dynamic programming algorithms. Finally, the above theory and method were applied to a hypothetical groundwater system. For the same groundwater system, a groundwater management model with covariates was developed and the results of the two optimization methods were found to be nearly identical, which validated the theory and methods put forth here.

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34

Miller, Boris M., and Daniel J. McInnes. "Management of a Large Dam via Optimal Price Control." IFAC Proceedings Volumes 44, no. 1 (January 2011): 12432–38. http://dx.doi.org/10.3182/20110828-6-it-1002.00786.

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35

Kim, Eun-Gab, Jin-Ho Byun, and Jae-Hyun Pae. "Optimal Control for Cash Management with Investment and Retrieval." IE interfaces 24, no. 4 (December 1, 2011): 396–407. http://dx.doi.org/10.7232/ieif.2011.24.4.396.

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36

Vinod, Ben. "Revenue management for the optimal control of group traffic." Journal of Revenue and Pricing Management 12, no. 4 (December 28, 2012): 295–304. http://dx.doi.org/10.1057/rpm.2012.49.

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37

DOHI, Tadashi, Naoto KAIO, and Shunji OSAKI. "Optimal Control of Production Management Systems with Diffusion Demand." Transactions of the Institute of Systems, Control and Information Engineers 4, no. 7 (1991): 286–93. http://dx.doi.org/10.5687/iscie.4.286.

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38

Lobo Pereira, Fernando, Fernando Arménio Fontes, Maria Margarida Ferreira, Maria do Rosário Pinho, Vilma Alves Oliveira, Eduardo Costa, and Geraldo Nunes Silva. "An Optimal Control Framework for Resources Management in Agriculture." Conference Papers in Mathematics 2013 (September 23, 2013): 1–15. http://dx.doi.org/10.1155/2013/769598.

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An optimal control framework to support the management and control of resources in a wide range of problems arising in agriculture is discussed. Lessons extracted from past research on the weed control problem and a survey of a vast body of pertinent literature led to the specification of key requirements to be met by a suitable optimization framework. The proposed layered control structure—including planning, coordination, and execution layers—relies on a set of nested optimization processes of which an “infinite horizon” Model Predictive Control scheme plays a key role in planning and coordination. Some challenges and recent results on the Pontryagin Maximum Principle for infinite horizon optimal control are also discussed.

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39

Sacco, Pier Luigi. "Optimal control of input-output systems via demand management." Economics Letters 39, no. 3 (July 1992): 269–73. http://dx.doi.org/10.1016/0165-1765(92)90259-2.

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40

Donkers, M. C. F., J. Van Schijndel, W. P. M. H. Heemels, and F. P. T. Willems. "Optimal control for integrated emission management in diesel engines." Control Engineering Practice 61 (April 2017): 206–16. http://dx.doi.org/10.1016/j.conengprac.2016.03.006.

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41

Mehta, Shefali V., Robert G. Haight, Frances R. Homans, Stephen Polasky, and Robert C. Venette. "Optimal detection and control strategies for invasive species management." Ecological Economics 61, no. 2-3 (March 2007): 237–45. http://dx.doi.org/10.1016/j.ecolecon.2006.10.024.

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42

Edholm, Christina J., Brigitte Tenhumberg, Chris Guiver, Yu Jin, Stuart Townley, and Richard Rebarber. "Management of invasive insect species using optimal control theory." Ecological Modelling 381 (August 2018): 36–45. http://dx.doi.org/10.1016/j.ecolmodel.2018.04.011.

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43

Zhang, Wei, and Jianghai Hu. "Dynamic buffer management using optimal control of hybrid systems." Automatica 44, no. 7 (July 2008): 1831–40. http://dx.doi.org/10.1016/j.automatica.2007.10.036.

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44

Riaz, Ahmad, and Tariq Javed. "CHIKUNGUNYA OUTBREAK IN PAKISTAN; OPTIMAL CONTROL, AND MANAGEMENT PREVENTION." JOURNAL OF MICROBIOLOGY AND MOLECULAR GENETICS 3, no. 3 (December 31, 2022): 67–69. http://dx.doi.org/10.52700/jmmg.v3i3.71.

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In recent decades, climatic changes have led to extreme weather events andprimarily influenced human health by outbreaks of epidemics and infectious disease. Asian countries including Pakistan is fundamentally effected by these substantial climate changes earthquake in 2005 and mega floods in 2010, affected over 20 million people following acute diarrhea, skin diseases and suspected malaria being the most common (Patz et al., 1996).These extreme climatic changes are precursors that triggers natural disasters and infectious disease recent outbreaks including malaria, dengue and chikungunya.

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45

Lin, Xinfan, Youngki Kim, Shankar Mohan, Jason B. Siegel, and Anna G. Stefanopoulou. "Modeling and Estimation for Advanced Battery Management." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 393–426. http://dx.doi.org/10.1146/annurev-control-053018-023643.

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The commercialization of lithium-ion batteries enabled the widespread use of portable consumer electronics and serious efforts to electrify trans-portation. Managing the potent brew of lithium-ion batteries in the large quantities necessary for vehicle propulsion is still challenging. From space applications a billion miles from Earth to the daily commute of a hybrid electric automobile, these batteries require sophisticated battery management systems based on accurate estimation of battery internal states. This system is the brain of the battery and is responsible for estimating the state of charge, state of health, state of power, and temperature. The state estimation relies on accurate prediction of complex electrochemical, thermal, and mechanical phenomena, which increases the importance of model and parameter accuracy. Moreover, as the batteries age, how should the parameters of the model change to accurately represent the performance, and how can we leverage the limited sensor information from the measured terminal voltage and sparse surface temperatures available in a battery system? With a frugal sensor set, what is the optimal sensor placement? This article reviews estimation techniques and error bounds regarding sensor noise and modeling errors, and concludes with an outlook on the research that will be necessary to enable fast charging, repurposing of batteries for grid energy storage, degradation prediction, and fault detection.

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46

Doff-Sotta, Martin, Mark Cannon, and Marko Bacic. "Optimal energy management for hybrid electric aircraft." IFAC-PapersOnLine 53, no. 2 (2020): 6043–49. http://dx.doi.org/10.1016/j.ifacol.2020.12.1672.

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47

Ye, Jianxiong, and An Li. "Necessary optimality conditions for nonautonomous optimal control problems and its applications to bilevel optimal control." Journal of Industrial & Management Optimization 13, no. 5 (2017): 1–21. http://dx.doi.org/10.3934/jimo.2018101.

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48

Be´nard, C., B. Guerrier, and M. M. Rosset-Loue¨rat. "Optimal Building Energy Management: Part I—Modeling." Journal of Solar Energy Engineering 114, no. 1 (February 1, 1992): 2–12. http://dx.doi.org/10.1115/1.2929978.

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We analyze the advantages of solving building energy management problems with the techniques of optimal control. Our approach consists of describing the dynamic behavior of a heated building with a simple model and controlling the whole system by minimizing a criterion defined for a time horizon of a few days. The two control components are the heat delivered to the building, and the variable heat exchange through the building envelope. In Part I, input (control and meteorological data) and output (indoor temperature) are related through a simplified state-space representation of the building. Part II is devoted to the actual computation of the control input. Results are given for two categories of buildings: The first is characterized by important direct solar gains. The inside structure is of low thermal inertia and so is the heating system. The second type of building is well insulated, with less glazing and less solar gain. The heavy internal structure of the building and the distribution of heat give a large thermal inertia to the system.

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49

Schwertman, Neil C., and Thomas R. Ryan. "Implementing Optimal Attributes Control Charts." Journal of Quality Technology 29, no. 1 (January 1997): 99–104. http://dx.doi.org/10.1080/00224065.1997.11979729.

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50

Osipov, Vladimir. "Control and cost analysis in the process of management decision making for the commodities assortment." Problems and Perspectives in Management 16, no. 2 (May 24, 2018): 209–19. http://dx.doi.org/10.21511/ppm.16(2).2018.19.

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The relevance of this work is determined by the further development of the management system and its information support that improve the economic entity’s activity and increase the competitiveness. The goal of this research is to develop a method for the most optimal commodities assortment formation, which makes it possible to increase the efficiency of the enterprise in the existing production conditions. Previously applied methods were oriented only to one of the methods of determining the commodities assortment, which prevented managers from choosing the most optimal option. The method of the optimal commodities assortment formation was proposed for the first time in this article while comparing the methods of its determination on products profitability and the limiting factor. The object of research is resource consumption in relation to certain commodities assortment manufactured by the enterprise operating in the measuring devices production sector. The article is focused on the methods of the integrated management cost analysis aimed at implementing the concept of the most complete and timely information support for the resource consumption control and regulation. General scientific and special research methods are used for the purpose of its implementation. The use of special methods of economic analysis made it possible to develop the economic model of costs estimation in the process of the most optimal commodities assortment formation.Conclusions and results of the research show that the efficiency of the industrial enterprise largely depends on rational resource consumption. The successful solution of this task largely depends on the correct commodities assortment formation. In view of this, methods for determining the commodities assortment based on the analysis of its profitability and assessing the impact of the limiting factor, taking into account the full load capacity, are proposed to be used.

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