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Journal articles on the topic 'Multiple criteria optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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1

Deming, Stanley N. "Multiple-criteria optimization." Journal of Chromatography A 550 (January 1991): 15–25. http://dx.doi.org/10.1016/s0021-9673(01)88527-7.

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2

Kostreva, Michael M., and Wodzimierz Ogryczak. "Linear optimization with multiple equitable criteria." RAIRO - Operations Research 33, no. 3 (July 1999): 275–97. http://dx.doi.org/10.1051/ro:1999112.

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3

Thoai, Nguyen V. "Criteria and dimension reduction of linear multiple criteria optimization problems." Journal of Global Optimization 52, no. 3 (August 14, 2011): 499–508. http://dx.doi.org/10.1007/s10898-011-9764-4.

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4

Żak, Jacek, Maciej Hojda, and Grzegorz Filcek. "Multiple Criteria Optimization of the Carpooling Problem." Transportation Research Procedia 37 (2019): 139–46. http://dx.doi.org/10.1016/j.trpro.2018.12.176.

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5

CEREN, TUNCER ŞAKAR, and MURAT KÖKSALAN. "EFFECTS OF MULTIPLE CRITERIA ON PORTFOLIO OPTIMIZATION." International Journal of Information Technology & Decision Making 13, no. 01 (January 2014): 77–99. http://dx.doi.org/10.1142/s0219622014500047.

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We study the effects of considering different criteria simultaneously on portfolio optimization. Using a single-period optimization setting, we use various combinations of expected return, variance, liquidity and Conditional Value at Risk criteria. With stocks from Borsa Istanbul, we make computational studies to show the effects of these criteria on objective and decision spaces. We also consider cardinality and weight constraints and study their effects on the results. In general, we observe that considering alternative criteria results in enlarged regions in the efficient frontier that may be of interest to the decision maker. We discuss the results of our experiments and provide insights.
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6

Donaghue, J. "MO-D-BRC-04: Multiple-Criteria Optimization Planning." Medical Physics 43, no. 6Part29 (June 2016): 3697. http://dx.doi.org/10.1118/1.4957203.

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7

Matsuyama, Y. "Harmonic competition: a self-organizing multiple criteria optimization." IEEE Transactions on Neural Networks 7, no. 3 (May 1996): 652–68. http://dx.doi.org/10.1109/72.501723.

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8

Zak, Jacek. "Multiple criteria evaluation and optimization of transportation systems." Journal of Advanced Transportation 43, no. 2 (April 2009): 91–94. http://dx.doi.org/10.1002/atr.5670430202.

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9

Zak, Jacek. "Multiple criteria evaluation and optimization of transportation systems." Journal of Advanced Transportation 43, no. 4 (October 2009): 367–70. http://dx.doi.org/10.1002/atr.5670430406.

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10

Şakar, Ceren Tuncer, and Murat Köksalan. "Erratum: "Effects of multiple criteria on portfolio optimization"." International Journal of Information Technology & Decision Making 13, no. 02 (March 2014): 445. http://dx.doi.org/10.1142/s0219622014920011.

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11

Slany, Wolfgang. "Scheduling as a fuzzy multiple criteria optimization problem." Fuzzy Sets and Systems 78, no. 2 (March 1996): 197–222. http://dx.doi.org/10.1016/0165-0114(95)00168-9.

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12

JURÍK, Pavol, Peter SCHMIDT, and Jaroslav KULTAN. "Optimization of Sensor Network Topology Using Multiple Criteria." Studies in Informatics and Control 30, no. 2 (June 25, 2021): 101–10. http://dx.doi.org/10.24846/v30i2y202109.

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13

Korhonen, Pekka, Herbert Moskowitz, Jyrki Wallenius, and Stanley Zionts. "An interactive approach to multiple criteria optimization with multiple decision-makers." Naval Research Logistics Quarterly 33, no. 4 (November 1986): 589–602. http://dx.doi.org/10.1002/nav.3800330405.

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14

Wang, Zeng Zhong, Li Chu Fan, and Mark Hastak. "Multi-Criteria Optimization of Bridge Management." Advanced Materials Research 255-260 (May 2011): 4080–84. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.4080.

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Optimizing investment funding levels and combinations of treatment types and timings, as an aid to management decision making, are vital functions of any bridge management system (BMS). Bridge management is always involved the bridge performance assessment which is essentially a multiple criteria decision making (MCDM) problem, which involves multiple assessment criteria such as traffic safety enhancement, preservation of bridge condition, protection from extreme events, agency cost minimization and user cost minimization. In this paper, a multi-criteria optimization methodology was developed not only to take due cognizance of costs but also to include a wide range of performance criteria to facilitate balanced decision-support practices in bridge management. The proposed methodology uses the AHP to determine the weights of criteria, linguistic terms to assess bridge performance under each criterion and aggregate bridge performance under different criteria into an overall performance score for each bridge structure. A numerical example illustrated the integrated methodology and the optimization process.
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15

Beausoleil, Ricardo P. "Bounded Variables nonlinear Multiple Criteria Optimization using Scatter search." Revista de Matemática: Teoría y Aplicaciones 11, no. 1 (May 6, 2011): 17. http://dx.doi.org/10.15517/rmta.v11i1.235.

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16

Ehrgott, Matthias, and Kathrin Klamroth. "Connectedness of efficient solutions in multiple criteria combinatorial optimization." European Journal of Operational Research 97, no. 1 (February 1997): 159–66. http://dx.doi.org/10.1016/s0377-2217(96)00116-6.

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17

Molostvov, Vitaly. "Multiple criteria optimization for stochastic systems with uncertain parameters." Model Assisted Statistics and Applications 6, no. 3 (August 26, 2011): 231–37. http://dx.doi.org/10.3233/mas-2011-0205.

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18

Yin, Luzhong, and Wei Yang. "Optimality criteria method for topology optimization under multiple constraints." Computers & Structures 79, no. 20-21 (August 2001): 1839–50. http://dx.doi.org/10.1016/s0045-7949(01)00126-2.

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19

MALAKOOTI, B. B. "Assembly line balancing with buffers by multiple criteria optimization." International Journal of Production Research 32, no. 9 (September 1994): 2159–78. http://dx.doi.org/10.1080/00207549408957065.

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20

Castro, Carlos E., Mauricio Cabrera Ríos, José M. Castro, and Blaine Lilly. "Multiple criteria optimization with variability considerations in injection molding." Polymer Engineering & Science 47, no. 4 (2007): 400–409. http://dx.doi.org/10.1002/pen.20639.

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21

Ho, William, Carman K. M. Lee, and George To Sum Ho. "Multiple criteria optimization of contemporary logistics distribution network problems." OR Insight 23, no. 1 (February 8, 2010): 27–43. http://dx.doi.org/10.1057/ori.2009.7.

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22

Misra, Krishna B., and Usha Sharma. "An efficient approach for multiple criteria redundancy optimization problems." Microelectronics Reliability 31, no. 2-3 (January 1991): 303–21. http://dx.doi.org/10.1016/0026-2714(91)90216-t.

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23

Nie, Bin, Dan Liao, Jing Ding, and Yao Dong He. "Multiple-Response Surface Approach: Modeling and Optimization." Advanced Materials Research 339 (September 2011): 321–25. http://dx.doi.org/10.4028/www.scientific.net/amr.339.321.

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Many products and processes have multidimensional characteristics and criteria. As these responses involve common parameters and processes, the response data are correlated. The quality and reliability improvement of such products and processes will typically involve multiple-response optimizations to find optimal operating conditions. Many of the current multiple-response optimization approaches assume a single-response uncertainty in the response models, and the uncertainty in the parameter estimates of the models. In this paper, we consider a Bayesian Model Average (BMA) approach to the modeling and optimization of variability of the predictions and the uncertainty of the model parameters. We further propose a Mahalanobis distance (MD) approach to account for the correlations among the response and the variation in the estimation of the response model.
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24

Kim, Nam H., Ting Dong, David Weinberg, and Jonas Dalidd. "Generalized Optimality Criteria Method for Topology Optimization." Applied Sciences 11, no. 7 (April 2, 2021): 3175. http://dx.doi.org/10.3390/app11073175.

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In this article, a generalized optimality criteria method is proposed for topology optimization with arbitrary objective function and multiple inequality constraints. This algorithm uses sensitivity information to update both the Lagrange multipliers and design variables. Different from the conventional optimality criteria method, the proposed method does not satisfy constraints at every iteration. Rather, it improves the Lagrange multipliers and design variables such that the optimality criteria are satisfied upon convergence. The main advantages of the proposed method are its capability of handling multiple constraints and computational efficiency. In numerical examples, the proposed method was found to be more than 100 times faster than the optimality criteria method and more than 1000 times faster than the method of moving asymptotes.
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25

Costa, Nuno, João Lourenço, and Zulema Lopes Pereira. "Responses modeling and optimization criteria impact on the optimization of multiple quality characteristics." Computers & Industrial Engineering 62, no. 4 (May 2012): 927–35. http://dx.doi.org/10.1016/j.cie.2011.12.015.

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26

Pleshivtseva, Yuliya, Edgar Rapoport, Bernard Nacke, Alexander Nikanorov, Paolo Di Barba, Michele Forzan, Sergio Lupi, and Elisabetta Sieni. "Design concepts of induction mass heating technology based on multiple-criteria optimization." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 36, no. 2 (March 6, 2017): 386–400. http://dx.doi.org/10.1108/compel-05-2016-0216.

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Purpose The purpose of this paper is to describe main ideas and demonstrate results of the research activities carried out by the authors in the field of design concepts of induction mass heating technology based on multiple-criteria optimization. The main goal of the studies is the application of different optimization methods and numerical finite element method (FEM) codes for field analysis to solve the multi-objective optimization problem that is mathematically formulated in terms of the most important optimization criteria, for example, maximum temperature uniformity, maximum energy efficiency and minimum scale formation. Design/methodology/approach Standard genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA) and alternance method of parametric optimization based on the optimal control theory are applied as effective tools for the practice-oriented problems for multiple-criteria optimization of induction heaters’ design based on non-linear coupled electromagnetic and temperature field analysis. Different approaches are used for combining FEM codes for interconnected field analysis and optimization algorithms into the automated optimization procedure. Findings Optimization procedures are tested and investigated for two- and three-criteria optimization problems solution on the examples of induction heating of a graphite disk, induction heating of aluminum and steel billets prior to hot forming. Practical implications Solved problems are based on the design of practical industrial applications. The developed optimization procedures are planned to be applied to the wide range of real-life problems of the optimal design and control of different electromagnetic devices and systems. Originality/value The paper describes main ideas and results of the research activities carried out by the authors during past years in the field of multiple-criteria optimization of induction heaters’ design based on numerical coupled electromagnetic and temperature field analysis. Implementing the automated procedure that combines a numerical FEM code for coupled field analysis with an optimization algorithm and its subsequent application for designing induction heaters makes the proposed approach specific and original. The paper also demonstrates that different optimization strategies used (standard GA, NSGA-II and the alternance method of optimal control theory) are effective for real-life industrial applications for multiple-criteria optimization of induction heaters design.
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27

Salas-Molina, Francisco, David Pla-Santamaria, Ana Garcia-Bernabeu, and Javier Reig-Mullor. "A Compact Representation of Preferences in Multiple Criteria Optimization Problems." Mathematics 7, no. 11 (November 11, 2019): 1092. http://dx.doi.org/10.3390/math7111092.

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A critical step in multiple criteria optimization is setting the preferences for all the criteria under consideration. Several methodologies have been proposed to compute the relative priority of criteria when preference relations can be expressed either by ordinal or by cardinal information. The analytic hierarchy process introduces relative priority levels and cardinal preferences. Lexicographical orders combine both ordinal and cardinal preferences and present the additional difficulty of establishing strict priority levels. To enhance the process of setting preferences, we propose a compact representation that subsumes the most common preference schemes in a single algebraic object. We use this representation to discuss the main properties of preferences within the context of multiple criteria optimization.
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28

Singh, Bismark. "Fairness criteria for allocating scarce resources." Optimization Letters 14, no. 6 (March 13, 2020): 1533–41. http://dx.doi.org/10.1007/s11590-020-01568-1.

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Abstract We develop an optimization model to provide a fair allocation of multiple resources to multiple users. All resources might not be suitable to all users. We develop a notion of fairness, and then provide a general class of functions achieving it. Next, we develop more restricted notions of fairness—special cases of which exist in literature. Finally, we distinguish between scarce and abundant resources, and show that if a resource is abundant, all users seeking it achieve the maximum possible coverage.
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29

FILATOVAS, Ernestas, and Olga KURASOVA. "A Decision Support System for Solving Multiple Criteria Optimization Problems." Informatics in Education 10, no. 2 (October 15, 2011): 213–24. http://dx.doi.org/10.15388/infedu.2011.14.

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30

Cook, Laurence W., and Jerome P. Jarrett. "Optimization Using Multiple Dominance Criteria for Aerospace Design Under Uncertainty." AIAA Journal 56, no. 12 (December 2018): 4965–76. http://dx.doi.org/10.2514/1.j056951.

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31

Mitsel', A. A., and V. O. Nochevkina. "Multiple criteria optimization of annual production plan of the enterprise." Regional Economics: Theory and Practice 16, no. 12 (December 14, 2018): 2369–82. http://dx.doi.org/10.24891/re.16.12.2369.

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32

Moraes, H. B., J. M. Vasconcellos, and P. M. Almeida. "Multiple criteria optimization applied to high speed catamaran preliminary design." Ocean Engineering 34, no. 1 (January 2007): 133–47. http://dx.doi.org/10.1016/j.oceaneng.2005.12.009.

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33

Engau, Alexander. "Proper Efficiency and Tradeoffs in Multiple Criteria and Stochastic Optimization." Mathematics of Operations Research 42, no. 1 (January 2017): 119–34. http://dx.doi.org/10.1287/moor.2016.0796.

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34

Marcotte, Odile, and Richard M. Soland. "An Interactive Branch-and-Bound Algorithm for Multiple Criteria Optimization." Management Science 32, no. 1 (January 1986): 61–75. http://dx.doi.org/10.1287/mnsc.32.1.61.

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35

Harris, S. L., and D. A. Mellichamp. "Controller tuning using optimization to meet multiple closed-loop criteria." AIChE Journal 31, no. 3 (March 1985): 484–87. http://dx.doi.org/10.1002/aic.690310317.

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36

Mehlawat, Mukesh Kumar, Pankaj Gupta, and Witold Pedrycz. "A New Possibilistic Optimization Model for Multiple Criteria Assignment Problem." IEEE Transactions on Fuzzy Systems 26, no. 4 (August 2018): 1775–88. http://dx.doi.org/10.1109/tfuzz.2017.2751006.

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37

Liu, Yong-Jun, Wei-Guo Zhang, and Wei-Jun Xu. "Fuzzy multi-period portfolio selection optimization models using multiple criteria." Automatica 48, no. 12 (December 2012): 3042–53. http://dx.doi.org/10.1016/j.automatica.2012.08.036.

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38

Silverman, Joe, Ralph E. Steuer, and Alan W. Whisman. "A multi-period, multiple criteria optimization system for manpower planning." European Journal of Operational Research 34, no. 2 (March 1988): 160–70. http://dx.doi.org/10.1016/0377-2217(88)90351-7.

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39

Ramanathan, Ramakrishnan. "ABC inventory classification with multiple-criteria using weighted linear optimization." Computers & Operations Research 33, no. 3 (March 2006): 695–700. http://dx.doi.org/10.1016/j.cor.2004.07.014.

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40

Ahmadi, Mohammad H., Mohammad-Ali Ahmadi, Amir H. Mohammadi, Mehdi Mehrpooya, and Michel Feidt. "Thermodynamic optimization of Stirling heat pump based on multiple criteria." Energy Conversion and Management 80 (April 2014): 319–28. http://dx.doi.org/10.1016/j.enconman.2014.01.031.

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41

Dutta, D., J. R. Rao, and R. N. Tiwari. "Fuzzy approaches for multiple criteria linear fractional optimization: A comment." Fuzzy Sets and Systems 54, no. 3 (March 1993): 347–49. http://dx.doi.org/10.1016/0165-0114(93)90381-q.

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42

Pérez-Domínguez, Luis, Luis Alberto Rodríguez-Picón, Alejandro Alvarado-Iniesta, David Luviano Cruz, and Zeshui Xu. "MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making." Complexity 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/2602376.

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The multiobjective optimization on the basis of ratio analysis (MOORA) method captures diverse features such as the criteria and alternatives of appraising a multiple criteria decision-making (MCDM) problem. At the same time, the multiple criteria problem includes a set of decision makers with diverse expertise and preferences. In fact, the literature lists numerous approaches to aid in this problematic task of choosing the best alternative. Nevertheless, in the MCDM field, there is a challenge regarding intangible information which is commonly involved in multiple criteria decision-making problem; hence, it is substantial in order to advance beyond the research related to this field. Thus, the objective of this paper is to present a fused method between multiobjective optimization on the basis of ratio analysis and Pythagorean fuzzy sets for the choice of an alternative. Besides, multiobjective optimization on the basis of ratio analysis is utilized to choose the best alternatives. Finally, two decision-making problems are applied to illustrate the feasibility and practicality of the proposed method.
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43

Wang, Haoyu, Zhanfeng Shen, Zihan Zhang, Zeyu Xu, Shuo Li, Shuhui Jiao, and Yating Lei. "Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria." Remote Sensing 13, no. 14 (July 15, 2021): 2782. http://dx.doi.org/10.3390/rs13142782.

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Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.
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44

Ndaruhadi, P. Y. M. Wibowo, and Bambang Santosa. "Modeling and Optimization for Multiple Criteria of Hole Quality in Hardened Steel Drilling." Applied Mechanics and Materials 835 (May 2016): 236–41. http://dx.doi.org/10.4028/www.scientific.net/amm.835.236.

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Drilling process has many applications including making molds and dies, all requires different quality of the drilled hole. The aim of this study is to establish models and optimization of cutting parameter to get the best hole quality, including enlargement diameter, circularity error and surface roughness in drilling hardened steel. Drilling experiments have been performed using different cutting parameters (i.e. cutting speed and feed rate) and employ and uncoated carbide drill under flooded cooling. The experimental results show that both of the cutting speed and feed rate significantly affect all responses. Models for responses have been developed for investigation in this study, and their optimizations have been obtained, showing better quality of the drilled hole produced at higher cutting speed and lower feed rate. Desirability for the optimum criteria is 0.944 at the highest cutting speed (60 m/min) and lowest feed rate (0.05 mm/rev).
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45

Wierzbicki, Andrzej P., Marek Makowski, and Janusz Granat. "Robustness Testing of Model Based Multiple Criteria Decisions: Fundamentals and Applications." International Journal of Information Technology & Decision Making 14, no. 05 (September 2015): 1035–62. http://dx.doi.org/10.1142/s0219622015500157.

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Robustness or insensitivity is a desirable property of decisions; however, most texts on robustness and/or sensitivity analysis do not define it precisely. A broad literature in this field concentrates on robust design of decisions (including robust optimization). This paper focuses on robustness testing, that is, checking whether a design has actually resulted in robust properties of the system if some of basic assumptions are changed. We propose a general framework of such robustness testing and show that robustness is a property of the relation between three (classes of) models: a basic model of the decision situation, a second model of possible perturbations of the first model, and a model of implementation of the decision, optionally taking into account some measurements of the impact of perturbations. Typical approaches to robustness or sensitivity analysis assume tacitly that the first two models can be combined and analyze parameters deviations in one combined model. However, the role of the first two models can be asymmetric if some optimization of the decision is performed on the first model. We extend this framework, intended originally for single criteria (scalar) optimization to multiple criteria (vector) optimization. The proposed approach is illustrated by diverse examples.
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46

Zhai, Jia, Haitao Zheng, Manying Bai, and Yunyun Jiang. "Multiperiod portfolio selection models under uncertain measure and with multiple criteria." Journal of Intelligent & Fuzzy Systems 40, no. 3 (March 2, 2021): 5071–86. http://dx.doi.org/10.3233/jifs-201769.

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This paper explores a multiperiod portfolio optimization problem under uncertain measure involving background risk, liquidity constraints and V-shaped transaction costs. Unlike traditional studies, we establish multiperiod mean-variance portfolio optimization models with multiple criteria in which security returns, background asset returns and turnover rates are assumed to be uncertain variables that can be estimated by experienced experts. When the returns of the securities and background assets follow normal uncertainty distributions, we use the deterministic forms of the multiperiod portfolio optimization model. The uncertain multiperiod portfolio selection models are practical but complicated. Therefore, the models are solved by employing a genetic algorithm. The uncertain multiperiod model with multiple criteria is compared with an uncertain multiperiod model without background risk and an uncertain multiperiod model without liquidity constraint respectively, we discuss how background risk and liquidity affect optimal terminal wealth. Finally, we give two numerical examples to demonstrate the effectiveness of the proposed approach and models.
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47

Chen, Ye, Keith W. Hipel, and D. Marc Kilgour. "A multiple criteria sequential sorting procedure." Journal of Industrial & Management Optimization 4, no. 3 (2008): 407–23. http://dx.doi.org/10.3934/jimo.2008.4.407.

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48

Villarreal Marroquín, María Guadalupe, Matilde Luz Sánchez Peña, Carlos E. Castro, José M. Castro, and Mauricio Cabrera-Ríos. "Use of data envelopment analysis and clustering in multiple criteria optimization." Intelligent Data Analysis 12, no. 1 (February 18, 2008): 89–101. http://dx.doi.org/10.3233/ida-2008-12106.

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49

Tang, Loon‐Ching, and Pierre Paoli. "A spreadsheet‐based multiple criteria optimization framework for quality function deployment." International Journal of Quality & Reliability Management 21, no. 3 (April 2004): 329–47. http://dx.doi.org/10.1108/02656710410522766.

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50

SEVASTJANOV, P., and L. DYMOVA. "Stock screening with use of multiple criteria decision making and optimization☆." Omega 37, no. 3 (June 2009): 659–71. http://dx.doi.org/10.1016/j.omega.2008.04.002.

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