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Journal articles on the topic 'Multi-objective methodology'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Mohamed, Nejlaoui, Najlawi Bilel, and Ali Sulaiman Alsagri. "A multi-objective methodology for multi-criteria engineering design." Applied Soft Computing 91 (June 2020): 106204. http://dx.doi.org/10.1016/j.asoc.2020.106204.

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Frank, Christopher P., Renaud A. Marlier, Olivia J. Pinon-Fischer, and Dimitri N. Mavris. "Evolutionary multi-objective multi-architecture design space exploration methodology." Optimization and Engineering 19, no. 2 (2018): 359–81. http://dx.doi.org/10.1007/s11081-018-9373-x.

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3

Moselhi, Osama, and Nazila Roofigari-Esfahan. "Project schedule compression: a multi-objective methodology." Construction Innovation 13, no. 4 (2013): 374–93. http://dx.doi.org/10.1108/ci-03-2011-0010.

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4

Lin, Chuang, Jiwei Huang, Ying Chen, and Laizhong Cui. "Thinking and methodology of multi-objective optimization." International Journal of Machine Learning and Cybernetics 9, no. 12 (2018): 2117–27. http://dx.doi.org/10.1007/s13042-018-0866-x.

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Cuco, Ana Paula Curty, Fabiano L. de Sousa, and Antônio J. Silva Neto. "A multi-objective methodology for spacecraft equipment layouts." Optimization and Engineering 16, no. 1 (2014): 165–81. http://dx.doi.org/10.1007/s11081-014-9252-z.

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Priyadarshini, Manisha, Ajit Behera, Chandan Kumar Biswas, Biswajit Swain, and Swadhin Patel. "Multi-objective optimization of WEDM process by hybrid methodology." Materials Today: Proceedings 33 (2020): 5511–13. http://dx.doi.org/10.1016/j.matpr.2020.03.321.

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7

Harkouss, Fatima, Farouk Fardoun, and Pascal Henry Biwole. "Multi-objective optimization methodology for net zero energy buildings." Journal of Building Engineering 16 (March 2018): 57–71. http://dx.doi.org/10.1016/j.jobe.2017.12.003.

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8

Yao, Shiqing, Zhibin Jiang, Na Li, Na Geng, and Xiao Liu. "A decentralised multi-objective scheduling methodology for semiconductor manufacturing." International Journal of Production Research 49, no. 24 (2011): 7227–52. http://dx.doi.org/10.1080/00207543.2010.535040.

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9

Shea, John E., and Thomas M. West. "A methodology for curriculum development using multi-objective programming." Computers & Industrial Engineering 31, no. 1-2 (1996): 25–28. http://dx.doi.org/10.1016/0360-8352(96)00070-8.

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10

Teleb, Radi, and Farhad Azadivar. "A methodology for solvng multi-objective simulation-optimization problems." European Journal of Operational Research 72, no. 1 (1994): 135–45. http://dx.doi.org/10.1016/0377-2217(94)90336-0.

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YAHAGI, Hiroyuki, Yusuke KARIYA, Masato TAKEHISA, Tatsunori HARA, and Jun OTA. "Multi-Level and Multi-Objective Design Methodology of Taxiways for Large Airports." Journal of Mechanical Systems for Transportation and Logistics 6, no. 1 (2012): 1–14. http://dx.doi.org/10.1299/jmtl.6.1.

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Jilla, Cyrus D., and David W. Miller. "Multi-Objective, Multidisciplinary Design Optimization Methodology for Distributed Satellite Systems." Journal of Spacecraft and Rockets 41, no. 1 (2004): 39–50. http://dx.doi.org/10.2514/1.9206.

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Cha, Youngpil, and Mooyoung Jung. "Satisfaction assessment of multi-objective schedules using neural fuzzy methodology." International Journal of Production Research 41, no. 8 (2003): 1831–49. http://dx.doi.org/10.1080/1352816031000074937.

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Lin, Kuo-Wei, Che-Chung Wang, and Jung-Hsing Chen. "Application of integrated robust designation methodology in multi-objective optimization." Journal of Information and Optimization Sciences 33, no. 4-5 (2012): 527–51. http://dx.doi.org/10.1080/02522667.2012.10700159.

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Li, Yunpeng, and Utpal Roy. "A multi-objective optimisation methodology towards product design for sustainability." International Journal of Strategic Engineering Asset Management 3, no. 2 (2018): 154. http://dx.doi.org/10.1504/ijseam.2018.092234.

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16

Reynoso-Meza, Gilberto, Xavier Blasco, Javier Sanchis, and Sergio García-Nieto. "A Multi-objective Optimization Design Methodology for SISO PID Controllers." IFAC Proceedings Volumes 45, no. 3 (2012): 406–11. http://dx.doi.org/10.3182/20120328-3-it-3014.00069.

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17

Burke, Edmund K., Jingpeng Li, and Rong Qu. "A Pareto-based search methodology for multi-objective nurse scheduling." Annals of Operations Research 196, no. 1 (2009): 91–109. http://dx.doi.org/10.1007/s10479-009-0590-8.

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18

Ho, Tien, and Vishy Karri. "Optimising multi-objective control methodology of a hydrogen powered car." International Journal of Hydrogen Energy 36, no. 10 (2011): 6269–80. http://dx.doi.org/10.1016/j.ijhydene.2011.02.041.

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19

Sarvaghad-Moghaddam, Moein, Ali A. Orouji, and Monireh Houshmand. "A multi-objective synthesis methodology for majority/minority logic networks." Journal of Computational Electronics 16, no. 1 (2016): 162–79. http://dx.doi.org/10.1007/s10825-016-0938-7.

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20

Gaspar-Cunha, António, Paulo Costa, Wagner de Campos Galuppo, João Miguel Nóbrega, Fernando Duarte, and Lino Costa. "Multi-Objective Optimization of Plastics Thermoforming." Mathematics 9, no. 15 (2021): 1760. http://dx.doi.org/10.3390/math9151760.

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The practical application of a multi-objective optimization strategy based on evolutionary algorithms was proposed to optimize the plastics thermoforming process. For that purpose, in this work, differently from the other works proposed in the literature, the shaping step was considered individually with the aim of optimizing the thickness distribution of the final part originated from sheets characterized by different thickness profiles, such as constant thickness, spline thickness variation in one direction and concentric thickness variation in two directions, while maintaining the temperature constant. As far we know, this is the first work where such a type of approach is proposed. A multi-objective optimization strategy based on Evolutionary Algorithms was applied to the determination of the final part thickness distribution with the aim of demonstrating the validity of the methodology proposed. The results obtained considering three different theoretical initial sheet shapes indicate clearly that the methodology proposed is valid, as it provides solutions with physical meaning and with great potential to be applied in real practice. The different thickness profiles obtained for the optimal Pareto solutions show, in all cases, that that the different profiles along the front are related to the objectives considered. Also, there is a clear improvement in the successive generations of the evolutionary algorithm.

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21

Li, Hong, Jie Duan, Dengyue Zhang, and Jinghui Yang. "A Distributed Energy Resources Aggregation Model Based on Multi-Scenario and Multi-Objective Methodology." Applied Sciences 9, no. 17 (2019): 3586. http://dx.doi.org/10.3390/app9173586.

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Aggregation technology can integrate distributed energy resources (DERs) into resource aggregation (RA) to achieve efficient utilization of resources. This paper studies a DERs aggregation model to construct a RA. Firstly, considering the uncertainty of the output of distributed generation (DG), the characteristics of DG are analyzed and the daily eigenvalues are extracted. The contour coefficient is introduced and the improved K-means algorithm is used to cluster the daily eigenvectors to get the multiple probability scenarios in a single season. Then, in order to obtain a RA with lower daily average cost, better power generation characteristics and higher regional aggregation degree, the DERs aggregation model based on multi-scenario and multi-objective is established considering multiple constraints. To obtain a compromise optimal solution, the cellular bat algorithm based on fuzzy membership degree (FMD-CBA) is used to solve the model. Finally, the validity of the multi-scenario and multi-objective model in a single season is verified by an example.

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22

Mlinarić, Danijel, tunjo Perić, and Josip Matejaš. "Multi-objective programming methodology for solving economic diplomacy resource allocation problem." Croatian Operational Research Review 10, no. 1 (2019): 165–74. http://dx.doi.org/10.17535/crorr.2019.0015.

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23

Madić, Miloš, Miroslav Radovanović, Margareta Coteata, Predrag Janković, and Dušan Petković. "Multi-Objective Optimization of Laser Cutting Using ROV-Based Taguchi Methodology." Applied Mechanics and Materials 809-810 (November 2015): 405–10. http://dx.doi.org/10.4028/www.scientific.net/amm.809-810.405.

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Multi-objective optimization of laser cutting for simultaneous improvement of performance characteristics is of great practical importance. In this study a range of value (ROV)-based Taguchi methodology is proposed for multi-objective optimization of laser cutting, i.e. surface roughness, kerf width and burr height in CO2laser cutting of AISI 304 stainless steel. Laser cutting experiment was conducted based on Taguchi’s L27experimental design by varying the laser power, cutting speed, assist gas pressure and focus position at three levels. In the proposed methodology based on the experimental data signal to noise ratios as per Taguchi’s method were calculated for each experimental trial upon which decision matrix was defined. Subsequently, multi-criteria decision making problem was solved by the ROV method. The proposed ROV-based Taguchi methodology has relatively simple computational procedure and can be easily applied by engineers for solving different multi-objective optimization problems that occur in real manufacturing environment.

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24

Cococcioni, Marco, Massimo Pappalardo, and Yaroslav D. Sergeyev. "Lexicographic multi-objective linear programming using grossone methodology: Theory and algorithm." Applied Mathematics and Computation 318 (February 2018): 298–311. http://dx.doi.org/10.1016/j.amc.2017.05.058.

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25

Chi, Guoyi, Shuangquan Hu, Yanhui Yang, and Tao Chen. "Response surface methodology with prediction uncertainty: A multi-objective optimisation approach." Chemical Engineering Research and Design 90, no. 9 (2012): 1235–44. http://dx.doi.org/10.1016/j.cherd.2011.12.012.

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26

Wang, Shuo, and Hsiao-Dong Chiang. "Multi-objective service restoration of distribution systems using user-centered methodology." International Journal of Electrical Power & Energy Systems 80 (September 2016): 140–49. http://dx.doi.org/10.1016/j.ijepes.2016.01.021.

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27

Vojinovic, Z., S. Sahlu, A. S. Torres, et al. "Multi-objective rehabilitation of urban drainage systems under uncertainties." Journal of Hydroinformatics 16, no. 5 (2014): 1044–61. http://dx.doi.org/10.2166/hydro.2014.223.

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Urban drainage systems are subject to many drivers which can affect their performance and functioning. Typically, climate change, urbanisation and population growth along with aging of pipes may lead to uncontrollable discharges and surface flooding. So far, many researchers and practitioners concerned with optimal design and rehabilitation of urban drainage systems have applied deterministic approaches which treat input parameters as fixed values. However, due to the variety of uncertainties associated with input parameters, such approaches can easily lead to either over-dimensioning or under-dimensioning of drainage networks. The present paper deals with such issues and describes a methodology that has been developed to accommodate the effects of uncertainties into the design and rehabilitation of drainage systems. The paper presents a methodology that can take into account uncertainties from climate change, urbanisation, population growth and aging of pipes. The methodology is applied and tested on a case study of Dhaka, Bangladesh. The urban drainage network optimisation problem is posed as a multi-objective problem for which the objective functions are formulated to minimise damage costs and intervention costs. Two approaches were evaluated and the results show that both approaches are capable of identifying optimal Pareto fronts.

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28

Brauers, Willem Karel M., Edmundas Kazimieras Zavadskas, Friedel Peldschus, and Zenonas Turskis. "MULTI‐OBJECTIVE DECISION‐MAKING FOR ROAD DESIGN." TRANSPORT 23, no. 3 (2008): 183–93. http://dx.doi.org/10.3846/1648-4142.2008.23.183-193.

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Multi‐objective analysis is a popular tool to solve many economic, managerial and construction problems. The objective of this research is to develop and implement a methodology for multi‐objective optimization of multi‐alternative decisions in road construction. After a rough overview of the articles dealing with the multi‐objective decision and assessment of road design alternatives described by discrete values, Multi‐Objective Optimization on the basis of the Ratio Analysis (MOORA) method was selected. This method focuses on a matrix of alternative responses on the objectives. A case study demonstrates the concept of multi‐objective optimization of road design alternatives and the best road design alternative is determined.

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29

Aly, Shady, Jan Tyrychtr, and Ivan Vrana. "Optimizing Design of Smart Workplace through Multi-Objective Programming." Applied Sciences 11, no. 7 (2021): 3042. http://dx.doi.org/10.3390/app11073042.

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Smart environments have proven very supportive to the improvement of the performance of people in different workplaces. Plenty of applications have been introduced spanning different settings including healthcare, ambient assisted living, homes, offices, and manufacturing environment, etc. However, subjectivity and ambiguity prevail in the majority of research, and still, up to date, rare approaches found quantitatively and objectively constructing or assessing the impact of smart enabling technologies on the performance of the subject environment. Further, no approaches have considered optimizing the adoption of those smart technologies with respect to objectives achievement. This article presents a novel optimization methodology for designing a smart workplace environment in conditions of ambiguity or fuzziness. The methodology begins with defining and weighing the overall goals and objectives of the workplace. The Prometthe multi-criterion decision-making technique is used to weigh the operational objectives with respect to the overall workplace goals. Next, the relation among basic building blocks of the model; namely: the operational objectives, smartness features, and smart enabling technologies are quantified, utilizing fuzzy relations. Then, the fuzzy goal programming techniques will be utilized to optimize the impact relation values while considering the budget constraint. The proposed optimization methodology is implemented on the development and optimization of the smart clinic, as a typical instance of the workplace.

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30

Mumtaz, Jabir, Zhang Li, Muhammad Imran, et al. "Multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm." Advances in Mechanical Engineering 11, no. 4 (2019): 168781401982958. http://dx.doi.org/10.1177/1687814019829588.

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31

Giassi, Alessandro, Fouad Bennis, and Jean-Jacques Maisonneuve. "Multi-Objective Optimization Using Asynchronous Distributed Applications." Journal of Mechanical Design 126, no. 5 (2004): 767–74. http://dx.doi.org/10.1115/1.1767817.

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In the context of concurrent engineering, this paper presents a quite innovative approach to the collaborative optimization process, which couples a multi-objective genetic algorithm with an asynchronous communication tool. This optimization method allows the collaborative and multi-sites design to be performed without requiring significant investments or changes in the company organization. To illustrate this methodology, the collaboration of three European companies on the optimization of a ship hull is described. The hull shape is automatically optimised distributing the elements of the optimization loop among three distant sites. Our study demonstrates that when multi-objective optimization is carried out in a distributed manner it can provide a powerful tool for concurrent product design.

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32

Zhao, Li-Mei, Lun-Jun Chen, Feng He, and Yu Luo. "Multi-Objective Optimization of Impact Crusher Rotor Based on Response Surface Methodology." Open Mechanical Engineering Journal 8, no. 1 (2014): 519–25. http://dx.doi.org/10.2174/1874155x01408010519.

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In this study, a method of multi-objective optimization is proposed to improve the quality of crushed materials and vibration performance of the rotor. This method is driven by the first order natural frequency and the radius of the rotor. The Central Composite Design (CCD) experiment method was used to guide the selection of appropriate structure finite element analysis samples in design space. The quadratic polynomials were employed to construct response surface (RS) model based on the response outputs of these samples obtained by analyzing the first order natural frequency, the harmonic and mass with the software ANSYS. Well-distributed samples were generated in the design space by shifted Hamersley sampling method. The prominent points were selected by the weighing method as initial samples. The multiobjective genetic algorithm was used to obtain the Pareto optimal solution set. Through optimization, the first order natural frequency was increased by 5.5%; the radius of the rotor was enlarged by 2.5% and the amplitude of the vibration was decreased by 11% at the position of bearing. At the same time, the rotor mass did not change much. The results show strong engineering practicability of the proposed method.

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33

Murugesan, Mohanraj, Beom-Soo Kang, and Kyunghoon Lee. "Multi-Objective Design Optimization of Composite Stiffened Panel Using Response Surface Methodology." Composites Research 28, no. 5 (2015): 297–310. http://dx.doi.org/10.7234/composres.2015.28.5.297.

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34

Ooi, Jecksin, Michael Angelo B. Promentilla, Raymond R. Tan, Denny K. S. Ng, and Nishanth G. Chemmangattuvalappil. "A systematic methodology for multi-objective molecular design via Analytic Hierarchy Process." Process Safety and Environmental Protection 111 (October 2017): 663–77. http://dx.doi.org/10.1016/j.psep.2017.08.039.

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35

Joos, H. D. "A methodology for multi-objective design assessment and flight control synthesis tuning." Aerospace Science and Technology 3, no. 3 (1999): 161–76. http://dx.doi.org/10.1016/s1270-9638(99)80040-6.

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36

Zhongkai Li, Yixiong Feng, Jianrong Tan, and Zhe Wei. "A methodology to support product platform optimization using multi-objective evolutionary algorithms." Transactions of the Institute of Measurement and Control 30, no. 3-4 (2008): 295–312. http://dx.doi.org/10.1177/0142331207088190.

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37

Kumar, Sandeep, Rajiv Kumar Garg, and Shekhar Srivastava. "A multi-objective optimization of TIG welding parameters using Response Surface Methodology." International Journal of Computer Aided Engineering and Technology 13, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijcaet.2021.10021175.

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38

Jana, T. K., G. K. Bose, B. Sarkar, and J. Saha. "Multi-objective decision-making in single-pass turning using response surface methodology." International Journal of Computational Materials Science and Surface Engineering 4, no. 1 (2011): 87. http://dx.doi.org/10.1504/ijcmsse.2011.037354.

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39

Xia, Meng, Qiang Zhou, Jan Sykulski, Shiyou Yang, and Yanhong Ma. "A Multi-Objective Topology Optimization Methodology Based on Pareto Optimal Min-Cut." IEEE Transactions on Magnetics 56, no. 3 (2020): 1–5. http://dx.doi.org/10.1109/tmag.2019.2955386.

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40

Adly, Amr A., and Salwa K. Abd-El-Hafiz. "A performance-oriented power transformer design methodology using multi-objective evolutionary optimization." Journal of Advanced Research 6, no. 3 (2015): 417–23. http://dx.doi.org/10.1016/j.jare.2014.08.003.

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41

Contreras, Sergio F., Camilo A. Cortés, and Johanna M. A. Myrzik. "Probabilistic multi-objective microgrid planning methodology for optimizing the ancillary services provision." Electric Power Systems Research 189 (December 2020): 106633. http://dx.doi.org/10.1016/j.epsr.2020.106633.

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42

Han, Huaizhi, Bingxi Li, and Wei Shao. "Multi-objective optimization of outward convex corrugated tubes using response surface methodology." Applied Thermal Engineering 70, no. 1 (2014): 250–62. http://dx.doi.org/10.1016/j.applthermaleng.2014.05.016.

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43

Toledo, R., J. J. Aznárez, D. Greiner, and O. Maeso. "A methodology for the multi-objective shape optimization of thin noise barriers." Applied Mathematical Modelling 50 (October 2017): 656–75. http://dx.doi.org/10.1016/j.apm.2017.06.020.

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44

Zendehboudi, Alireza, and Xianting Li. "Desiccant-wheel optimization via response surface methodology and multi-objective genetic algorithm." Energy Conversion and Management 174 (October 2018): 649–60. http://dx.doi.org/10.1016/j.enconman.2018.07.078.

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45

Srivastava, Shekhar, Sandeep Kumar, and Rajiv Kumar Garg. "A multi-objective optimisation of TIG welding parameters using response surface methodology." International Journal of Computer Aided Engineering and Technology 14, no. 4 (2021): 575. http://dx.doi.org/10.1504/ijcaet.2021.115363.

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46

Kamalova, Navruzov, Qian, and Lee. "Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer." Applied Sciences 9, no. 14 (2019): 2931. http://dx.doi.org/10.3390/app9142931.

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In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end.

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47

Sánchez-González, Juan, Oriol Sallent, Jordi Pérez-Romero, and Ramon Agustí. "A multi-cell multi-objective self-optimisation methodology based on genetic algorithms for wireless cellular networks." International Journal of Network Management 23, no. 4 (2013): 287–307. http://dx.doi.org/10.1002/nem.1831.

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48

Bose, Goutam Kumar, and Pritam Pain. "Metaheuristic Approach of Multi-Objective Optimization during EDM Process." International Journal of Mathematical, Engineering and Management Sciences 3, no. 3 (2018): 301–14. http://dx.doi.org/10.33889/ijmems.2018.3.3-022.

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In modern-day manufacturing Electric Discharge Machining (EDM) process has successfully placed itself in the domain of precision machining and generating complex geometries where secondary machining processes are eliminated. In this research paper, a die sinking EDM is applied to machine mild steel in order to measure the different multi-objective results like Material Removal Rate (MRR) and Over Cut (OC). This contradictory objective is accomplished by using the control parameters like a pulse on time, duty factor, gap current and spark gap employing copper tool with lateral flushing. Here the individual objective function of the responses is created through regression analysis. Primarily the contradictory objectives are optimized by employing Taguchi Methodology, then Regression analysis is done on the test results. Additionally, the experimental results are optimized using Response Surface Methodology (RSM). It is followed by a multi-objective optimization through Overlaid contour plots and Desirability functions to ascertain the best parametric combination amongst the set of feasible alternatives.

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49

Luque, Pablo, Daniel A. Mántaras, Álvaro Maradona, et al. "Multi-Objective Evolutionary Design of an Electric Vehicle Chassis." Sensors 20, no. 13 (2020): 3633. http://dx.doi.org/10.3390/s20133633.

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An iterative algorithm is proposed for determining the optimal chassis design of an electric vehicle, given a path and a reference time. The proposed algorithm balances the capacity of the battery pack and the dynamic properties of the chassis, seeking to optimize the tradeoff between the mass of the vehicle, its energy consumption, and the travel time. The design variables of the chassis include geometrical and inertial values, as well as the characteristics of the powertrain. The optimization is constrained by the slopes, curves, grip, and posted speeds of the different sections of the track. Particular service constraints are also considered, such as limiting accelerations due to passenger comfort or cargo safety. This methodology is applicable to any vehicle whose route and travel time are known in advance, such as delivery vehicles, buses, and race cars, and has been validated using telemetry data from an internal combustion rear-wheel drive race car designed for hill climb competitions. The implementation of the proposed methodology allows to reduce the weight of the battery pack by up to 20%, compared to traditional design methods.

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50

Hlobilová, Adéla, and Matěj Lepš. "Study on Multi-Objective Reconstruction of Random Media." Applied Mechanics and Materials 825 (February 2016): 153–60. http://dx.doi.org/10.4028/www.scientific.net/amm.825.153.

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This paper deals with a reconstruction of random media via multi-objective optimization. Two statistical descriptors, namely a two-point probability function and a two-point lineal path function, are repetitively evaluated for the original medium and the reconstructed image to appreciate the improvement in the optimization progress. Because of doubts of the weights setting in the weighted-sum method, purely multi-objective optimization routine Non-dominated Sorting Genetic Algorithm~II is utilized. Three operators are compared for creating new offspring populations that satisfy a prescribed volume fraction constraint. The main contribution is in the testing of the proposed methodology on several benchmark images.

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