Academic literature on the topic 'Chance-constrained optimisation'
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Journal articles on the topic "Chance-constrained optimisation"
Charles, Vincent, and A. Udhayakumar. "Genetic algorithm for chance constrained reliability stochastic optimisation problems." International Journal of Operational Research 14, no. 4 (2012): 417. http://dx.doi.org/10.1504/ijor.2012.047513.
Full textMurray, Andrew, Michael Cashmore, Ashwin Arulselvan, and Jeremy Frank. "Joint Chance Constrained Probabilistic Simple Temporal Networks via Column Generation (Extended Abstract)." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (July 17, 2022): 305–7. http://dx.doi.org/10.1609/socs.v15i1.21794.
Full textIbrahim, Sarmad, Aaron Cramer, Xiao Liu, and Yuan Liao. "PV inverter reactive power control for chance-constrained distribution system performance optimisation." IET Generation, Transmission & Distribution 12, no. 5 (March 13, 2018): 1089–98. http://dx.doi.org/10.1049/iet-gtd.2017.0484.
Full textGeletu, Abebe, Michael Klöppel, Hui Zhang, and Pu Li. "Advances and applications of chance-constrained approaches to systems optimisation under uncertainty." International Journal of Systems Science 44, no. 7 (July 2013): 1209–32. http://dx.doi.org/10.1080/00207721.2012.670310.
Full textLópez, Julio, Jose R. S. Mantovani, and Javier Contreras. "Reactive power planning under conditional-value-at-risk assessment using chance-constrained optimisation." IET Generation, Transmission & Distribution 9, no. 3 (February 19, 2015): 231–40. http://dx.doi.org/10.1049/iet-gtd.2014.0224.
Full textWang, Xinwei, Alexander E. I. Brownlee, Michal Weiszer, John R. Woodward, Mahdi Mahfouf, and Jun Chen. "A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties." Transportation Research Part C: Emerging Technologies 132 (November 2021): 103382. http://dx.doi.org/10.1016/j.trc.2021.103382.
Full textLiu, Chunyang, Xiuli Wang, Jingli Guo, Minghuang Huang, and Xiong Wu. "Chance-constrained scheduling model of grid-connected microgrid based on probabilistic and robust optimisation." IET Generation, Transmission & Distribution 12, no. 11 (June 19, 2018): 2499–509. http://dx.doi.org/10.1049/iet-gtd.2017.1039.
Full textZhao, Xia, Xiaobin Ye, Lun Yang, Rongrong Zhang, and Wei Yan. "Chance constrained dynamic optimisation method for AGC units dispatch considering uncertainties of the offshore wind farm." Journal of Engineering 2019, no. 16 (March 1, 2019): 2112–19. http://dx.doi.org/10.1049/joe.2018.8558.
Full textZhao, Yi, Qingwan Xue, Zhichao Cao, and Xi Zhang. "A Two-Stage Chance Constrained Approach with Application to Stochastic Intermodal Service Network Design Problems." Journal of Advanced Transportation 2018 (December 24, 2018): 1–18. http://dx.doi.org/10.1155/2018/6051029.
Full textSarfraz, Ali, Muhammad Shahzad Pansota, Nabeel Abdulhadi M. Fahal, Ahsan Sarfaraz, and Haseeb Javed. "Analytical Solution of Stochastic Real-time Power Dispatch with Large Scale Wind Farms." Pakistan Journal of Engineering and Technology 4, no. 3 (September 30, 2021): 18–26. http://dx.doi.org/10.51846/vol4iss3pp18-26.
Full textDissertations / Theses on the topic "Chance-constrained optimisation"
Sassi, Achille. "Numerical methods for hybrid control and chance-constrained optimization problems." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLY005/document.
Full textThis thesis is devoted to the analysis of numerical methods in the field of optimal control, and it is composed of two parts. The first part is dedicated to new results on the subject of numerical methods for the optimal control of hybrid systems, controlled by measurable functions and discontinuous jumps in the state variable simultaneously. The second part focuses on a particular application of trajectory optimization problems for space launchers. Here we use some nonlinear optimization methods combined with non-parametric statistics techniques. This kind of problems belongs to the family of stochastic optimization problems and it features the minimization of a cost function in the presence of a constraint which needs to be satisfied within a desired probability threshold
Excoffier, Mathilde. "Chance-Constrained Programming Approaches for Staffing and Shift-Scheduling Problems with Uncertain Forecasts : application to Call Centers." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112244/document.
Full textThe staffing and shift-scheduling problems in call centers consist in deciding how many agents handling the calls should be assigned to work during a given period in order to reach the required Quality of Service and minimize the costs. These problems are subject to a growing interest, both for their interesting theoritical formulation and their possible applicative effects. This thesis aims at proposing chance-constrained approaches considering uncertainty on demand forecasts.First, this thesis proposes a model solving the problems in one step through a joint chance-constrained stochastic program, providing a cost-reducing solution. A continuous-based approach leading to an easily-tractable optimization program is formulated with random variables following continuous distributions, a new continuous relation between arrival rates and theoritical real agent numbers and constraint linearizations. The global risk level is dynamically shared among the periods during the optimization process, providing reduced-cost solution. The resulting solutions respect the targeted risk level while reducing the cost compared to other approaches.Moreover, this model is extended so that it provides a better representation of real situations. First, the queuing system model is improved and consider the limited patience of customers. Second, another formulation of uncertainty is proposed so that the period correlation is considered.Finally, another uncertainty representation is proposed. The distributionally robust approach provides a formulation while assuming that the correct probability distribution is unknown and belongs to a set of possible distributions defined by given mean and variance. The problem is formulated with a joint chance constraint. The risk at each period is a decision variable to be optimized. A deterministic equivalent problem is proposed. An easily-tractable mixed-integer linear formulation is obtained through piecewise linearizations
Sheikh, Hussin Siti Aida. "Employees Provident Fund (EPF) Malaysia : generic models for asset and liability management under uncertainty." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7505.
Full textPrigent, Sylvain. "Approche novatrice pour la conception et l’exploitation d’avions écologiques." Thesis, Toulouse, ISAE, 2015. http://www.theses.fr/2015ESAE0014/document.
Full textThe objective of this PhD work is to pose, investigate, and solve the highly multidisciplinary and multiobjective problem of environmentally efficient aircraft design and operation. In this purpose, the main three drivers for optimizing the environmental performance of an aircraft are the airframe, the engine, and the mission profiles. The figures of merit, which will be considered for optimization, are fuel burn, local emissions, global emissions, and climate impact (noise excluded). The study will be focused on finding efficient compromise strategies and identifying the most powerful design architectures and design driver combinations for improvement of environmental performances. The modeling uncertainty will be considered thanks to rigorously selected methods. A hybrid aircraft configuration is proposed to reach the climatic impact reduction objective
Helal, Nathalie. "An evidential answer for the capacitated vehicle routing problem with uncertain demands." Thesis, Artois, 2017. http://www.theses.fr/2017ARTO0208/document.
Full textThe capacitated vehicle routing problem is an important combinatorial optimisation problem. Its objective is to find a set of routes of minimum cost, such that a fleet of vehicles initially located at a depot service the deterministic demands of a set of customers, while respecting capacity limits of the vehicles. Still, in many real-life applications, we are faced with uncertainty on customer demands. Most of the research papers that handled this situation, assumed that customer demands are random variables. In this thesis, we propose to represent uncertainty on customer demands using evidence theory - an alternative uncertainty theory. To tackle the resulting optimisation problem, we extend classical stochastic programming modelling approaches. Specifically, we propose two models for this problem. The first model is an extension of the chance-constrained programming approach, which imposes certain minimum bounds on the belief and plausibility that the sum of the demands on each route respects the vehicle capacity. The second model extends the stochastic programming with recourse approach: it represents by a belief function for each route the uncertainty on its recourses (corrective actions) and defines the cost of a route as its classical cost (without recourse) plus the worst expected cost of its recourses. Some properties of these two models are studied. A simulated annealing algorithm is adapted to solve both models and is experimentally tested
Fleming, James. "Robust and stochastic MPC of uncertain-parameter systems." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310.
Full textWang, Chenghao. "Contribution à l’optimisation robuste de réseaux." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2632.
Full textThis Ph.D. Thesis is focused on proposing new optimization modeling and algorithmic approaches for dealing with real-world network optimization problems arising in the transportation and telecommunications fields. Since the focus has been on real-world applications, a relevant aspect that has been taken into account is data uncertainty, i.e. the fact that the value of a subset of input data of the problem is not exactly known when the problem is solved. More precisely, in the context of transportation problems, it was considered the flight level assignment problem, which arises in air traffic management. It aims at establishing the flight levels of a set of aircraft in order to improve the total assignment revenue, to reduce the total number of flight conflicts and also the total en-route delay. In this context, we proposed a new chance-constrained optimization problem and iterative constraint-generation heuristic which is based on both analytical and sampling methods. Besides transportation problems, this Thesis has also focused on the optimal design of 5th generation of wireless networks (5G) considering Superfluid and virtual architectures. Specifically, the 5G Superfluid architecture is based on atomic virtual entities called Reusable Functional Block (RFB). We investigated the problem of minimizing the total installation costs of a 5G Superfluid network (composed of virtual entities and realized over a physical network) while guaranteeing constraint on user coverage, downlink traffic performance and technical constraints on RFBs of different nature. To solve this hard problem, we proposed a Benders decomposition approach. Concerning instead the design of general virtual networks, we adopted a green paradigm that pursues energy-efficiency and tackled a state-of-the-art robust mixed integer linear programming formulation of the problem, by means of a new matheuris tic based on combining a genetic algorithm with exact large neighborhood searches. Results of computational tests executed considering realistic problem instances have shown the validity of all the new optimization modeling and algorithmic approaches proposed in this Thesis for the transportation and telecommunications problems sketched above
Xie, Yue. "Bio-Inspired Computing for Chance-Constrained Combinatorial Optimisation Problems." Thesis, 2021. https://hdl.handle.net/2440/134213.
Full textThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2021
Conference papers on the topic "Chance-constrained optimisation"
Uzuncan, Ezgi, and Mohammad Reza Hesamzadeh. "Optimal firm transmission access using chance-constrained optimisation for renewable integration." In 2015 IEEE Power & Energy Society General Meeting. IEEE, 2015. http://dx.doi.org/10.1109/pesgm.2015.7286493.
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