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Books on the topic 'Multi-objective Design'

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Published: 14 December 2024

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1

Liu, Aying. A multi-objective and multi-design evaluation procedure for environmental protection forestry. Portsmouth: University of Portsmouth, Department of Economics, 1997.

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2

Wang, Lihui, Amos H. C. Ng, and Kalyanmoy Deb, eds. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-652-8.

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3

Silvano, Cristina, William Fornaciari, and Eugenio Villar, eds. Multi-objective Design Space Exploration of Multiprocessor SoC Architectures. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8837-9.

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4

C, Ng Amos H., Deb Kalyanmoy, and SpringerLink (Online service), eds. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. London: Springer-Verlag London Limited, 2011.

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5

United States. National Aeronautics and Space Administration., ed. Multi-objective decision-making under uncertainty: Fuzzy logic methods. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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6

United States. National Aeronautics and Space Administration., ed. Multi-objective decision-making under uncertainty: Fuzzy logic methods. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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7

S, Rao S. Applications of fuzzy theories to multi-objective system optimization. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1991.

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8

Center, Lewis Research, and United States. National Aeronautics and Space Administration., eds. Multi objective controller design for linear systems via optimal interpolation. [Columbus, Ohio]: Ohio State University, 1996.

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9

Center, Lewis Research, and United States. National Aeronautics and Space Administration., eds. Multi objective controller design for linear systems via optimal interpolation. [Columbus, Ohio]: Ohio State University, 1996.

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10

Saravanos, D. A. Multi-objective shape and material optimization of composite structures including damping. [Washington, D.C.]: NASA, 1990.

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11

C, Chamis C., and United States. National Aeronautics and Space Administration., eds. Multi-objective shape and material optimization of composite structures including damping. [Washington, D.C.]: NASA, 1990.

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12

C, Chamis C., and United States. National Aeronautics and Space Administration., eds. Multi-objective shape and material optimization of composite structures including damping. [Washington, D.C.]: NASA, 1990.

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13

Ng, Wai-Yin, ed. Interactive Multi-Objective Programming as a Framework for Computer-Aided Control System Design. Berlin/Heidelberg: Springer-Verlag, 1989. http://dx.doi.org/10.1007/bfb0028891.

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14

Ng, W. Y. Interactive multi-objective programming as a framework for computer-aided control system design. Berlin: Springer-Verlag, 1989.

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15

Rocha, Frederico A. E., Ricardo M. F. Martins, Nuno C. C. Lourenço, and Nuno C. G. Horta. Electronic Design Automation of Analog ICs combining Gradient Models with Multi-Objective Evolutionary Algorithms. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02189-8.

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16

Zheng, Maosheng, and Jie Yu. Robust Design and Assessment of Product and Production by Means of Probabilistic Multi-objective Optimization. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2661-5.

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17

Nedjah, Nadia, Luiza de Macedo Mourelle, and Heitor Silvério Lopes. Evolutionary Multi-Objective System Design. Taylor & Francis Group, 2020.

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18

Nedjah, Nadia, Heitor Silverio Lopes, and Luiza De Macedo Mourelle. Evolutionary Multi-Objective System Design: Theory and Applications. Taylor & Francis Group, 2020.

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19

Nedjah, Nadia, Heitor Silverio Lopes, and Luiza De Macedo Mourelle. Evolutionary Multi-Objective System Design: Theory and Applications. Taylor & Francis Group, 2017.

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20

Sudhoff, Scott D. Power Magnetic Devices: A Multi-Objective Design Approach. Wiley & Sons, Limited, John, 2022.

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21

Sudhoff, Scott D. Power Magnetic Devices: A Multi-Objective Design Approach. Wiley & Sons, Incorporated, John, 2014.

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22

Sudhoff, Scott D. Power Magnetic Devices: A Multi-Objective Design Approach. Wiley & Sons, Incorporated, John, 2021.

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23

Sudhoff, Scott D. Power Magnetic Devices: A Multi-Objective Design Approach. Wiley & Sons, Incorporated, John, 2014.

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24

Sudhoff, Scott D. Power Magnetic Devices: A Multi-Objective Design Approach. Wiley & Sons, Incorporated, John, 2014.

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25

Koziel, Slawomir, and Adrian Bekasiewicz. Multi-Objective Design of Antennas Using Surrogate Models. World Scientific Publishing Co Pte Ltd, 2016.

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26

Nedjah, Nadia, Heitor Silverio Lopes, and Luiza De Macedo Mourelle. Evolutionary Multi-Objective System Design: Theory and Applications. Taylor & Francis Group, 2020.

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27

Nedjah, Nadia, Heitor Silverio Lopes, and Luiza De Macedo Mourelle. Evolutionary Multi-Objective System Design: Theory and Applications. Taylor & Francis Group, 2020.

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28

Filion, Yves R. Multi-objective stochastic design of water distribution systems. 2006.

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29

Sudhoff, Scott D. Power Magnetic Devices: A Multi-Objective Design Approach. Wiley & Sons, Incorporated, John, 2021.

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30

Sudhoff, Scott D. Power Magnetic Devices: A Multi-Objective Design Approach. Wiley & Sons, Incorporated, John, 2014.

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31

Sudhoff, Scott D. Power Magnetic Devices: A Multi-Objective Design Approach. Wiley & Sons, Incorporated, John, 2021.

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32

Evolutionary Multi-Objective System Design: Theory and Applications. Taylor & Francis Group, 2020.

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33

The Integrated Multi-Objective Multi-Disciplinary Jet Engine Design Optimization Program. Storming Media, 1999.

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34

Deb, Kalyanmoy, Lihui Wang, and Amos H. C. Ng. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, 2014.

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35

Deb, Kalyanmoy, Lihui Wang, and Amos H. C. Ng. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, 2011.

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36

Multi-objective decision-making under uncertainty: Fuzzy logic methods. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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37

Schumacher, Michael. Objective Coordination in Multi-Agent System Engineering: Design and Implementation. Springer, 2003.

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38

Villar, Eugenio, Cristina Silvano, and William Fornaciari. Multi-objective Design Space Exploration of Multiprocessor SoC Architectures: The MULTICUBE Approach. Springer, 2011.

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39

Multi-objective design space exploration of multiporcessor SoC architectures: The MULTICUBE approach. New York: Springer, 2011.

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40

Villar, Eugenio, Cristina Silvano, and William Fornaciari. Multi-objective Design Space Exploration of Multiprocessor SoC Architectures: The MULTICUBE Approach. Springer, 2014.

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41

Multi-objective shape and material optimization of composite structures including damping. [Washington, D.C.]: NASA, 1990.

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42

Martins, Ricardo M. F., Nuno C. C. Lourenço, Frederico A. E. Rocha, and Nuno C. G. Horta. Electronic Design Automation of Analog ICs Combining Gradient Models with Multi-Objective Evolutionary Algorithms. Springer London, Limited, 2013.

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43

Horta, Nuno C. G., Frederico A. E. Rocha, Ricardo M. F. Martins, and Nuno C. C. Lourenço. Electronic Design Automation of Analog ICs combining Gradient Models with Multi-Objective Evolutionary Algorithms. Springer, 2013.

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44

Robust Design and Assessment of Product and Production by Means of Probabilistic Multi-Objective Optimization. Springer, 2024.

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45

Objective Coordination in Multi-Agent System Engineering: Design and Implementation (Lecture Notes in Computer Science). Springer, 2001.

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46

Optimal Design of Complex Mechanical Systems: With Applications to Vehicle Engineering. Springer, 2006.

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47

Sobczyk, Eugeniusz Jacek. Uciążliwość eksploatacji złóż węgla kamiennego wynikająca z warunków geologicznych i górniczych. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN, 2022. http://dx.doi.org/10.33223/onermin/0222.

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Abstract:
Hard coal mining is characterised by features that pose numerous challenges to its current operations and cause strategic and operational problems in planning its development. The most important of these include the high capital intensity of mining investment projects and the dynamically changing environment in which the sector operates, while the long-term role of the sector is dependent on factors originating at both national and international level. At the same time, the conditions for coal mining are deteriorating, the resources more readily available in active mines are being exhausted, mining depths are increasing, temperature levels in pits are rising, transport routes for staff and materials are getting longer, effective working time is decreasing, natural hazards are increasing, and seams with an increasing content of waste rock are being mined. The mining industry is currently in a very difficult situation, both in technical (mining) and economic terms. It cannot be ignored, however, that the difficult financial situation of Polish mining companies is largely exacerbated by their high operating costs. The cost of obtaining coal and its price are two key elements that determine the level of efficiency of Polish mines. This situation could be improved by streamlining the planning processes. This would involve striving for production planning that is as predictable as possible and, on the other hand, economically efficient. In this respect, it is helpful to plan the production from operating longwalls with full awareness of the complexity of geological and mining conditions and the resulting economic consequences. The constraints on increasing the efficiency of the mining process are due to the technical potential of the mining process, organisational factors and, above all, geological and mining conditions. The main objective of the monograph is to identify relations between geological and mining parameters and the level of longwall mining costs, and their daily output. In view of the above, it was assumed that it was possible to present the relationship between the costs of longwall mining and the daily coal output from a longwall as a function of onerous geological and mining factors. The monograph presents two models of onerous geological and mining conditions, including natural hazards, deposit (seam) parameters, mining (technical) parameters and environmental factors. The models were used to calculate two onerousness indicators, Wue and WUt, which synthetically define the level of impact of onerous geological and mining conditions on the mining process in relation to: —— operating costs at longwall faces – indicator WUe, —— daily longwall mining output – indicator WUt. In the next research step, the analysis of direct relationships of selected geological and mining factors with longwall costs and the mining output level was conducted. For this purpose, two statistical models were built for the following dependent variables: unit operating cost (Model 1) and daily longwall mining output (Model 2). The models served two additional sub-objectives: interpretation of the influence of independent variables on dependent variables and point forecasting. The models were also used for forecasting purposes. Statistical models were built on the basis of historical production results of selected seven Polish mines. On the basis of variability of geological and mining conditions at 120 longwalls, the influence of individual parameters on longwall mining between 2010 and 2019 was determined. The identified relationships made it possible to formulate numerical forecast of unit production cost and daily longwall mining output in relation to the level of expected onerousness. The projection period was assumed to be 2020–2030. On this basis, an opinion was formulated on the forecast of the expected unit production costs and the output of the 259 longwalls planned to be mined at these mines. A procedure scheme was developed using the following methods: 1) Analytic Hierarchy Process (AHP) – mathematical multi-criteria decision-making method, 2) comparative multivariate analysis, 3) regression analysis, 4) Monte Carlo simulation. The utilitarian purpose of the monograph is to provide the research community with the concept of building models that can be used to solve real decision-making problems during longwall planning in hard coal mines. The layout of the monograph, consisting of an introduction, eight main sections and a conclusion, follows the objectives set out above. Section One presents the methodology used to assess the impact of onerous geological and mining conditions on the mining process. Multi-Criteria Decision Analysis (MCDA) is reviewed and basic definitions used in the following part of the paper are introduced. The section includes a description of AHP which was used in the presented analysis. Individual factors resulting from natural hazards, from the geological structure of the deposit (seam), from limitations caused by technical requirements, from the impact of mining on the environment, which affect the mining process, are described exhaustively in Section Two. Sections Three and Four present the construction of two hierarchical models of geological and mining conditions onerousness: the first in the context of extraction costs and the second in relation to daily longwall mining. The procedure for valuing the importance of their components by a group of experts (pairwise comparison of criteria and sub-criteria on the basis of Saaty’s 9-point comparison scale) is presented. The AHP method is very sensitive to even small changes in the value of the comparison matrix. In order to determine the stability of the valuation of both onerousness models, a sensitivity analysis was carried out, which is described in detail in Section Five. Section Six is devoted to the issue of constructing aggregate indices, WUe and WUt, which synthetically measure the impact of onerous geological and mining conditions on the mining process in individual longwalls and allow for a linear ordering of longwalls according to increasing levels of onerousness. Section Seven opens the research part of the work, which analyses the results of the developed models and indicators in individual mines. A detailed analysis is presented of the assessment of the impact of onerous mining conditions on mining costs in selected seams of the analysed mines, and in the case of the impact of onerous mining on daily longwall mining output, the variability of this process in individual fields (lots) of the mines is characterised. Section Eight presents the regression equations for the dependence of the costs and level of extraction on the aggregated onerousness indicators, WUe and WUt. The regression models f(KJC_N) and f(W) developed in this way are used to forecast the unit mining costs and daily output of the designed longwalls in the context of diversified geological and mining conditions. The use of regression models is of great practical importance. It makes it possible to approximate unit costs and daily output for newly designed longwall workings. The use of this knowledge may significantly improve the quality of planning processes and the effectiveness of the mining process.
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