Книги: "Optimisation probabiliste"

1

Peter, Whittle, and Kelly F. P, eds. Probability, statistics, and optimisation: A tribute to Peter Whittle. Chichester: Wiley, 1994.

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2

Ross, Sheldon M. Applied probability models with optimization applications. New York: London, 1992.

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3

Ross, Sheldon M. Applied probability models with optimization applications. New York: Dover Publications, 1992.

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4

Sironi, Paolo. Modern portfolio management: From Markowitz to probabilistic scenario optimisation : goal-based and long-term portfolio choice. London: Risk Books, 2015.

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5

Arora, Rajesh Kumar. Optimization: Algorithms and applications. Boca Raton: Taylor & Francis Group, 2015.

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6

L, Aarts E. H., and Lenstra J. K, eds. Local search in combinatorial optimization. Chichester [England]: Wiley, 1997.

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7

Onn, Shmuel. Nonlinear discrete optimization: An algorithmic theory. Zürich, Switzerland: European Mathematical Society Publishing House, 2010.

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8

Hansen, Eldon R. Global optimization using interval analysis. New York: M. Dekker, 1992.

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9

Davis, M. H. A. Markov models and optimization. London: Chapman & Hall, 1993.

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10

Whittle, Peter. Networks: Optimisation and Evolution (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge University Press, 2007.

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11

Pham, Huyên. Optimisation et Contrôle Stochastique Appliqués à la Finance. Springer London, Limited, 2007.

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12

Theodore, Louis, and Kelly Behan. Introduction to Optimization for Chemical and Environmental Engineers. Taylor & Francis Group, 2018.

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13

Theodore, Louis, and Kelly Behan. Introduction to Optimization for Chemical and Environmental Engineers. Taylor & Francis Group, 2018.

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14

Theodore, Louis, and Kelly Behan. Introduction to Optimization for Chemical and Environmental Engineers. Taylor & Francis Group, 2018.

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15

Calvo, Oscar Osvaldo Marquez. Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems. Taylor & Francis Group, 2020.

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16

Calvo, Oscar Osvaldo Marquez. Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems. Taylor & Francis Group, 2020.

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17

Calvo, Oscar Osvaldo Marquez. Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems. Taylor & Francis Group, 2020.

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18

Calvo, Oscar Osvaldo Marquez. Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems. Taylor & Francis Group, 2020.

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19

Calvo, Oscar Osvaldo Marquez. Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems. Taylor & Francis Group, 2020.

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20

Arora, Rajesh Kumar. Optimization: Algorithms and Applications. Taylor & Francis Group, 2015.

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21

Arora, Rajesh Kumar. Optimization: Algorithms and Applications. Taylor & Francis Group, 2015.

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22

Williamson, John H., Antti Oulasvirta, Per Ola Kristensson, and Nikola Banovic, eds. Bayesian Methods for Interaction and Design. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781108874830.

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Анотація:

Intended for researchers and practitioners in interaction design, this book shows how Bayesian models can be brought to bear on problems of interface design and user modelling. It introduces and motivates Bayesian modelling and illustrates how powerful these ideas can be in thinking about human-computer interaction, especially in representing and manipulating uncertainty. Bayesian methods are increasingly practical as computational tools to implement them become more widely available, and offer a principled foundation to reason about interaction design. The book opens with a self-contained tutorial on Bayesian concepts and their practical implementation, tailored for the background and needs of interaction designers. The contributed chapters cover the use of Bayesian probabilistic modelling in a diverse set of applications, including improving pointing-based interfaces; efficient text entry using modern language models; advanced interface design using cutting-edge techniques in Bayesian optimisation; and Bayesian approaches to modelling the cognitive processes of users.

23

Jr, Shaler Stidham. Optimal Design of Queuing Systems. Chapman & Hall/CRC, 2009.

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24

Sastry, Kumara, Martin Pelikan, and Erick Cantú-Paz. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer, 2010.

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25

(Editor), Martin Pelikan, Kumara Sastry (Editor), and Erick Cantú-Paz (Editor), eds. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence). Springer, 2006.

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26

Davim, J. Paulo, and Kaushik Kumar. Optimization Using Evolutionary Algorithms and Metaheuristics: Applications in Engineering. Taylor & Francis Group, 2019.

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27

Davim, J. Paulo, and Kaushik Kumar. Optimization Using Evolutionary Algorithms and Metaheuristics: Applications in Engineering. Taylor & Francis Group, 2019.

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28

Davim, J. Paulo, and Kaushik Kumar. Optimization Using Evolutionary Algorithms and Metaheuristics: Applications in Engineering. Taylor & Francis Group, 2019.

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29

Davim, J. Paulo, and Kaushik Kumar. Optimization Using Evolutionary Algorithms and Metaheuristics: Applications in Engineering. Taylor & Francis Group, 2019.

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30

Boots, Barry, Kokichi Sugihara, Sung Nok Chiu, and Atsuyuki Okabe. Spatial Tessellations: Concepts and Applications of Voronoi Diagrams (Wiley Series in Probability and Statistics). 2nd ed. Wiley, 2000.

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31

Castillo, Enrique del. Process Optimization. Springer, 2008.

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32

Castillo, Enrique del, and Enrique Del Castillo. Process Optimization: A Statistical Approach (International Series in Operations Research & Management Science). Springer, 2007.

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33

Sierksma, Gerard, and Yori Zwols. Linear and Integer Optimization: Theory and Practice, Third Edition. Chapman and Hall/CRC, 2015.

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34

Castillo, Enrique del. Process Optimization: A Statistical Approach. Springer London, Limited, 2007.

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35

Sierksma, Gerard, and Yori Zwols. Linear and Integer Optimization: Theory and Practice, Third Edition. Taylor & Francis Group, 2015.

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36

Sierksma, Gerard, and Yori Zwols. Linear and Integer Optimization: Theory and Practice, Third Edition. Taylor & Francis Group, 2015.

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37

Sierksma, Gerard, and Yori Zwols. Linear and Integer Optimization: Theory and Practice, Third Edition. Taylor & Francis Group, 2015.

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38

Sierksma, Gerard, and Yori Zwols. Linear and Integer Optimization: Theory and Practice, Third Edition. Taylor & Francis Group, 2015.

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39

Ali, Irfan, Ali Akbar Shaikh, Aquil Ahmed, and Leopoldo E. Cárdenas Barrón. Optimal Decision Making in Operations Research and Statistics. Taylor & Francis Group, 2021.

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40

Chiulli, Roy M. Quantitative Analysis: An Introduction (Automation and Production Systems, Methodologies and Applications, Vol 2). CRC, 1999.

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41

Ali, Irfan, Ali Akbar Shaikh, Leopoldo E. Cárdenas-Barrón, and Aquil Ahmed. Optimal Decision Making in Operations Research and Statistics: Methodologies and Applications. Taylor & Francis Group, 2021.

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42

Kwon, Roy H. Introduction to Linear Organization and Extensions with MATLAB. Taylor & Francis Group, 2013.

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43

Ali, Irfan, Ali Akbar Shaikh, Leopoldo E. Cárdenas-Barrón, and Aquil Ahmed. Optimal Decision Making in Operations Research and Statistics: Methodologies and Applications. Taylor & Francis Group, 2021.

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44

Ali, Irfan, Ali Akbar Shaikh, Leopoldo E. Cárdenas-Barrón, and Aquil Ahmed. Optimal Decision Making in Operations Research and Statistics: Methodologies and Applications. Taylor & Francis Group, 2021.

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45

Kulak, Dariusz. Wieloaspektowa metoda oceny stanu gleb leśnych po przeprowadzeniu procesów pozyskania drewna. Publishing House of the University of Agriculture in Krakow, 2017. http://dx.doi.org/10.15576/978-83-66602-28-1.

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Анотація:

Presented reasearch aimed to develop and analyse the suitability of the CART models for prediction of the extent and probability of occurrence of damage to outer soil layers caused by timber harvesting performed under varied conditions. Having employed these models, the author identified certain methods of logging works and conditions, under which they should be performed to minimise the risk of damaging forest soils. The analyses presented in this work covered the condition of soils upon completion of logging works, which was investigated in 48 stands located in central and south-eastern Poland. In the stands selected for these studies a few felling treatments were carried out, including early thinning, late thinning and final felling. Logging works were performed with use of the most popular technologies in Poland. Trees were cut down with chainsaws and timber was extracted by means of various skidding methods: with horses, semi-suspended skidding with the use of cable yarding systems, farm tractors equipped with cable winches or tractors of a skidder type, and forwarding employing farm tractors with trailers loaded mechanically by cranes or manually. The analyses also included mechanised forest operation with the use of a harvester and a forwarder. The information about the extent of damage to soil, in a form of wheel-ruts and furrows, gathered in the course of soil condition inventory served for construction of regression tree models using the CART method (Classification and Regression Trees), based on which the area, depth and the volume of soil damage under analysis, wheel-ruts and furrows, were determined, and the total degree of all soil disturbances was assessed. The CART classification trees were used for modelling the probability of occurrence of wheel-ruts and furrows, or any other type of soil damage. Qualitative independent variables assumed by the author for developing the models included several characteristics describing the conditions under which the logging works were performed, mensuration data of the stands and the treatments conducted there. These characteristics covered in particular: the season of the year when logging works were performed, the system of timber harvesting employed, the manner of timber skidding, the means engaged in the process of timber harvesting and skidding, habitat type, crown closure, and cutting category. Moreover, the author took into consideration an impact of the quantitative independent variables on the extent and probability of occurrence of soil disturbance. These variables included the following: the measuring row number specifying a distance between the particular soil damage and communication tracks, the age of a stand, the soil moisture content, the intensity of a particular cutting treatment expressed by units of harvested timber volume per one hectare of the stand, and the mean angle of terrain inclination. The CART models developed in these studies not only allowed the author to identify the conditions, under which the soil damage of a given degree is most likely to emerge, or determine the probability of its occurrence, but also, thanks to a graphical presentation of the nature and strength of relationships between the variables employed in the model construction, they facilitated a recognition of rules and relationships between these variables and the area, depth, volume and probability of occurrence of forest soil damage of a particular type. Moreover, the CART trees served for developing the so-called decision-making rules, which are especially useful in organising logging works. These rules allow the organisers of timber harvest to plan the management-related actions and operations with the use of available technical means and under conditions enabling their execution in such manner as to minimise the harm to forest soils. Furthermore, employing the CART trees for modelling soil disturbance made it possible to evaluate particular independent variables in terms of their impact on the values of dependent variables describing the recorded disturbance to outer soil layers. Thanks to this the author was able to identify, amongst the variables used in modelling the properties of soil damage, these particular ones that had the greatest impact on values of these properties, and determine the strength of this impact. Detailed results depended on the form of soil disturbance and the particular characteristics subject to analysis, however the variables with the strongest influence on the extent and probability of occurrence of soil damage, under the conditions encountered in the investigated stands, enclosed the following: the season of the year when logging works were performed, the volume-based cutting intensity of the felling treatments conducted, technical means used for completion of logging works, the soil moisture content during timber harvest, the manner of timber skidding, dragged, semi-suspended or forwarding, and finally a distance between the soil damage and transportation ducts. The CART models proved to be very useful in designing timber harvesting technologies that could minimise the risk of forest soil damage in terms of both, the extent of factual disturbance and the probability of its occurrence. Another valuable advantage of this kind of modelling is an opportunity to evaluate an impact of particular variables on the extent and probability of occurrence of damage to outer soil layers. This allows the investigator to identify, amongst all of the variables describing timber harvesting processes, those crucial ones, from which any optimisation process should start, in order to minimise the negative impact of forest management practices on soil condition.

46

Tlelo-Cuautle, Esteban, Luis Gerardo de la Fraga, Alejandro Silva-Juárez, and Omar Guillén Guillén-Fernández. Optimization of Integer/Fractional Order Chaotic Systems by Metaheuristics and Their Electronic Realization. Taylor & Francis Group, 2021.

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47

Tlelo-Cuautle, Esteban, Luis Gerardo de la Fraga, Alejandro Silva-Juárez, and Omar Guillén Guillén-Fernández. Optimization of Integer/Fractional Order Chaotic Systems by Metaheuristics and Their Electronic Realization. Taylor & Francis Group, 2021.

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48

Tlelo-Cuautle, Esteban, Luis Gerardo de la Fraga, Omar Guillén-Fernández, and Alejandro Silva-Juárez. Optimization of Integer/fractional Order Chaotic Systems by Metaheuristics and Their Electronic Realization. Taylor & Francis Group, 2021.

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49

Belia, Evangelia, Lorenzo Benedetti, Bruce Johnson, Sudhir Murthy, Marc Neumann, Peter Vanrolleghem, and Stefan Weijers, eds. Uncertainty in Wastewater Treatment Design and Operation. IWA Publishing, 2021. http://dx.doi.org/10.2166/9781780401034.

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Uncertainty in Wastewater Treatment Design and Operation aims to facilitate the transition of the wastewater profession to the probabilistic use of simulators with the associated benefits of being better able to take advantage of opportunities and manage risk. There is a paradigm shift taking place in the design and operation of treatment plants in the water industry. The market is currently in transition to use modelling and simulation while still using conventional heuristic guidelines (safety factors). Key reasons for transition include: wastewater treatment simulation software advancements; stricter effluent requirements that cannot be designed for using traditional approaches, and increased pressure for more efficient designs (including energy efficiency, greenhouse gas emission control). There is increasing consensus among wastewater professionals that the performance of plants and the predictive power of their models (degree of uncertainty) is a critical component of plant design and operation. However, models and simulators used by designers and operators do not incorporate methods for the evaluation of uncertainty associated with each design. Thus, engineers often combine safety factors with simulation results in an arbitrary way based on designer ‘experience’. Furthermore, there is not an accepted methodology (outside modelling) that translates uncertainty to assumed opportunity or risk and how it is distributed among consultants/contractors and owners. Uncertainty in Wastewater Treatment Design and Operation documents how uncertainty, opportunity and risk are currently handled in the wastewater treatment practice by consultants, utilities and regulators. The book provides a useful set of terms and definitions relating to uncertainty and promotes an understanding of the issues and terms involved. It identifies the sources of uncertainty in different project phases and presents a critical review of the available methods. Real-world examples are selected to illustrate where and when sources of uncertainty are introduced and how models are implemented and used in design projects and in operational optimisation. Uncertainty in Wastewater Treatment Design and Operation defines the developments required to provide improved procedures and tools to implement uncertainty and risk evaluations in projects. It is a vital reference for utilities, regulators, consultants, and trained management dealing with certainty, opportunity and risk in wastewater treatment. ISBN: 9781780401027 (Paperback) ISBN: 9781780401034 (eBook) ISBN: 9781789062601 (ePub)

50

Zhao, Yun-Bin. Sparse Optimization Theory and Methods. Taylor & Francis Group, 2018.

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