Process Parameters Optimization Of Micro Electric Discharge Machining Process Using Genetic Algorithm

Micro Electric Discharge Machining (micro EDM) is a non-traditional machining process which can be used for drilling micro holes in high strength to weight ratio materials like Titanium super alloy. However, the process control parameters of the machine have to be set at an optimal setting in order to achieve the desired responses. This present research study deals with the single and multiobjective optimization of micro EDM process using Genetic Algorithm. Mathematical models using Response Surface Methodology (RSM) is used to correlate the response and the parameters. The desired responses are minimum tool wear rate and minimum overcut while the independent control parameters considered are pulse on time, peak current and flushing pressure. In the multiobjective problem, the responses conflict with each other. This research provides a Pareto optimal set of solution points where each solution is a non dominated solution among the group of predicted solution points thus allowing flexibility in operating the machine while maintaining the standard quality

Exploring the Effect of Dimensional Tolerance of the Inserts During Multi-Objective Optimization of Face Hard Milling Using Genetic Algorithm

Surface roughness and dimensional deviation are critical quality dimensions of machined products and several machining parameters including tool insert dimensional tolerance affect them. Machining performance studies involving dimensional tolerance of the insert during machining, particularly hard face milling do not have considerable attention of the researchers. Therefore, the aim of the present work is to investigate the effect of the dimensional tolerance of the insert along with other machining parameters such as spindle speed, feed per tooth, and depth of cut on the roughness and dimensional deviation simultaneously. Experiments were conducted as per standard L18 mixed orthogonal array on a CNC vertical milling machine. Significance of machining parameters with respect to roughness and dimensional deviation was determined using Analysis of variance (ANOVA). Results revealed that among several machining parameters, feed per tooth greatly affects surface roughness and dimensional deviation. Optimum machining parameters that give minimum values of surface roughness and dimensional deviation simultaneously was obtained using Genetic Algorithm (GA)

Polynomial algorithms for p-dispersion problems in a 2d Pareto Front

Having many best compromise solutions for bi-objective optimization problems, this paper studies p-dispersion problems to select $p\geqslant 2$ representative points in the Pareto Front(PF). Four standard variants of p-dispersion are considered. A novel variant, denoted Max-Sum-Neighbor p-dispersion, is introduced for the specific case of a 2d PF. Firstly, it is proven that $2$-dispersion and $3$-dispersion problems are solvable in $O(n)$ time in a 2d PF. Secondly, dynamic programming algorithms are designed for three p-dispersion variants, proving polynomial complexities in a 2d PF. The Max-Min p-dispersion problem is proven solvable in $O(pn\log n)$ time and $O(n)$ memory space. The Max-Sum-Min p-dispersion problem is proven solvable in $O(pn^3)$ time and $O(pn^2)$ space. The Max-Sum-Neighbor p-dispersion problem is proven solvable in $O(pn^2)$ time and $O(pn)$ space. Complexity results and parallelization issues are discussed in regards to practical implementation

Genetic algorithms for condition-based maintenance optimization under uncertainty

International audienceThis paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GA), when the parameters of the maintenance model are affected by uncertainty and the fitness values are represented by Cumulative Distribution Functions (CDFs). The main issues addressed to tackle this problem are the development of a method to rank the uncertain fitness values, and the definition of a novel Pareto dominance concept. The GA-based methods are applied to a practical case study concerning the setting of a condition-based maintenance policy on the degrading nozzles of a gas turbine operated in an energy production plant

VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain

[EN] Many-objective optimization methods have proven successful in the integration of research attributes demanded for urban vulnerability assessment models. However, these techniques suffer from the curse of the dimensionality problem, producing an excessive burden in the decision-making process by compelling decision-makers to select alternatives among a large number of candidates. In other fields, this problem has been alleviated through cluster analysis, but there is still a lack in the application of such methods for urban vulnerability assessment purposes. This work addresses this gap by a novel combination of visual analytics and cluster analysis, enabling the decision-maker to select the set of indicators best representing urban vulnerability accordingly to three criteria: expert¿s preferences, goodness of fit, and robustness. Based on an assessment framework previously developed, VisualUVAM affords an evaluation of urban vulnerability in Spain at regional, provincial, and municipal scales, whose results demonstrate the effect of the governmental structure of a territory over the vulnerability of the assessed entities.This research was funded by the Spanish Ministry of Economy and Competitiveness, along with FEDER, grant number Project: BIA2017-85098-R".Salas, J.; Yepes, V. (2019). VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain. Sustainability. 11(8):2191-01-2191-17. https://doi.org/10.3390/su11082191S2191-012191-17118Rigillo, M., & Cervelli, E. (2014). Mapping Urban Vulnerability: The Case Study of Gran Santo Domingo, Dominican Republic. Advanced Engineering Forum, 11, 142-148. doi:10.4028/www.scientific.net/aef.11.142Malekpour, S., Brown, R. R., & de Haan, F. J. (2015). Strategic planning of urban infrastructure for environmental sustainability: Understanding the past to intervene for the future. Cities, 46, 67-75. doi:10.1016/j.cities.2015.05.003Salas, J., & Yepes, V. (2018). Urban vulnerability assessment: Advances from the strategic planning outlook. Journal of Cleaner Production, 179, 544-558. doi:10.1016/j.jclepro.2018.01.088Moraci, F., Errigo, M., Fazia, C., Burgio, G., & Foresta, S. (2018). Making Less Vulnerable Cities: Resilience as a New Paradigm of Smart Planning. Sustainability, 10(3), 755. doi:10.3390/su10030755De Gregorio Hurtado, S. (2017). Is EU urban policy transforming urban regeneration in Spain? Answers from an analysis of the Iniciativa Urbana (2007–2013). Cities, 60, 402-414. doi:10.1016/j.cities.2016.10.015Salas, J., & Yepes, V. (2019). MS-ReRO and D-ROSE methods: Assessing relational uncertainty and evaluating scenarios’ risks and opportunities on multi-scale infrastructure systems. Journal of Cleaner Production, 216, 607-623. doi:10.1016/j.jclepro.2018.12.083Dor, A., & Kissinger, M. (2017). A multi-year, multi-scale analysis of urban sustainability. Environmental Impact Assessment Review, 62, 115-121. doi:10.1016/j.eiar.2016.05.004Rega, C., Singer, J. P., & Geneletti, D. (2018). Investigating the substantive effectiveness of Strategic Environmental Assessment of urban planning: Evidence from Italy and Spain. Environmental Impact Assessment Review, 73, 60-69. doi:10.1016/j.eiar.2018.07.004Salas, J., & Yepes, V. (2018). A discursive, many-objective approach for selecting more-evolved urban vulnerability assessment models. Journal of Cleaner Production, 176, 1231-1244. doi:10.1016/j.jclepro.2017.11.249Penadés-Plà, V., García-Segura, T., Martí, J., & Yepes, V. (2016). A Review of Multi-Criteria Decision-Making Methods Applied to the Sustainable Bridge Design. Sustainability, 8(12), 1295. doi:10.3390/su8121295Zio, E., & Bazzo, R. (2011). A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems. European Journal of Operational Research, 210(3), 624-634. doi:10.1016/j.ejor.2010.10.021Ishibuchi, H., Akedo, N., & Nojima, Y. (2015). Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems. IEEE Transactions on Evolutionary Computation, 19(2), 264-283. doi:10.1109/tevc.2014.2315442A fast and effective method for pruning of non-dominated solutions in many-objective problems https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750253049&partnerID=40&md5=f46109796025a884fd054d73e71c308eTaboada, H. A., Baheranwala, F., Coit, D. W., & Wattanapongsakorn, N. (2007). Practical solutions for multi-objective optimization: An application to system reliability design problems. Reliability Engineering & System Safety, 92(3), 314-322. doi:10.1016/j.ress.2006.04.014Kasprzyk, J. R., Nataraj, S., Reed, P. M., & Lempert, R. J. (2013). Many objective robust decision making for complex environmental systems undergoing change. Environmental Modelling & Software, 42, 55-71. doi:10.1016/j.envsoft.2012.12.007Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268-281. doi:10.1016/j.gloenvcha.2006.02.006A new decision sciences for complex systems http://people.physics.anu.edu.au/~tas110/Teaching/Lectures/L1/Material/Lempert02.pdfThomas, J., & Kielman, J. (2009). Challenges for Visual Analytics. Information Visualization, 8(4), 309-314. doi:10.1057/ivs.2009.26Andrienko, G., Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S. I., … Tominski, C. (2010). Space, time and visual analytics. International Journal of Geographical Information Science, 24(10), 1577-1600. doi:10.1080/13658816.2010.508043Santos, J., Ferreira, A., & Flintsch, G. (2017). A multi-objective optimization-based pavement management decision-support system for enhancing pavement sustainability. Journal of Cleaner Production, 164, 1380-1393. doi:10.1016/j.jclepro.2017.07.027Análisis urbanístico de barrios vulnerables https://www.fomento.gob.es/MFOM/LANG_CASTELLANO/DIRECCIONES_GENERALES/ARQ_VIVIENDA/SUELO_Y_POLITICAS/OBSERVATORIO/Analisis_urba_Barrios_Vulnerables/Informes_CCAA.htmBirkmann, J., Garschagen, M., & Setiadi, N. (2014). New challenges for adaptive urban governance in highly dynamic environments: Revisiting planning systems and tools for adaptive and strategic planning. Urban Climate, 7, 115-133. doi:10.1016/j.uclim.2014.01.006Besagni, G., & Borgarello, M. (2019). The socio-demographic and geographical dimensions of fuel poverty in Italy. Energy Research & Social Science, 49, 192-203. doi:10.1016/j.erss.2018.11.007Khalil, N., Kamaruzzaman, S. N., & Baharum, M. R. (2016). Ranking the indicators of building performance and the users’ risk via Analytical Hierarchy Process (AHP): Case of Malaysia. Ecological Indicators, 71, 567-576. doi:10.1016/j.ecolind.2016.07.032Pellicer, E., Sierra, L. A., & Yepes, V. (2016). Appraisal of infrastructure sustainability by graduate students using an active-learning method. Journal of Cleaner Production, 113, 884-896. doi:10.1016/j.jclepro.2015.11.010Sierra, L. A., Yepes, V., & Pellicer, E. (2018). A review of multi-criteria assessment of the social sustainability of infrastructures. Journal of Cleaner Production, 187, 496-513. doi:10.1016/j.jclepro.2018.03.022Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26. doi:10.1016/0377-2217(90)90057-

A Clustering Procedure for Reducing the Number of Representative Solutions in the Pareto Front of Multiobjective Optimization Problems

none2E. Zio; R. BazzoZio, Enrico; R., Bazz

A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems

In many multiobjective optimization problems, the Pareto Fronts and Sets contain a large number of solutions and this makes it difficult for the decision maker to identify the preferred ones. A possible way to alleviate this difficulty is to present to the decision maker a subset of a small number of solutions representatives of the Pareto Front characteristics. In this paper, a two-steps procedure is presented, aimed at identifying a limited number of representative solutions to be presented to the decision maker. Pareto Front solutions are first clustered into "families", which are then synthetically represented by a "head-of-the-family" solution. Level Diagrams are then used to represent, analyse and interpret the Pareto Front reduced to its head-of-the-family solutions. The procedure is applied to a reliability allocation case study of literature, in decision-making contexts both without or with explicit preferences by the decision maker on the objectives to be optimized.Multiobjective optimization Subtractive clustering Level Diagrams Fuzzy preference assignment Genetic algorithms Redundancy allocation

A Posteriori And Interactive Approaches For Decision-making With Multiple Stochastic Objectives

Computer simulation is a popular method that is often used as a decision support tool in industry to estimate the performance of systems too complex for analytical solutions. It is a tool that assists decision-makers to improve organizational performance and achieve performance objectives in which simulated conditions can be randomly varied so that critical situations can be investigated without real-world risk. Due to the stochastic nature of many of the input process variables in simulation models, the output from the simulation model experiments are random. Thus, experimental runs of computer simulations yield only estimates of the values of performance objectives, where these estimates are themselves random variables. Most real-world decisions involve the simultaneous optimization of multiple, and often conflicting, objectives. Researchers and practitioners use various approaches to solve these multiobjective problems. Many of the approaches that integrate the simulation models with stochastic multiple objective optimization algorithms have been proposed, many of which use the Pareto-based approaches that generate a finite set of compromise, or tradeoff, solutions. Nevertheless, identification of the most preferred solution can be a daunting task to the decisionmaker and is an order of magnitude harder in the presence of stochastic objectives. However, to the best of this researcher’s knowledge, there has been no focused efforts and existing work that attempts to reduce the number of tradeoff solutions while considering the stochastic nature of a set of objective functions. In this research, two approaches that consider multiple stochastic objectives when reducing the set of the tradeoff solutions are designed and proposed. The first proposed approach is an a posteriori approach, which uses a given set of Pareto optima as input. The second iv approach is an interactive-based approach that articulates decision-maker preferences during the optimization process. A detailed description of both approaches is given, and computational studies are conducted to evaluate the efficacy of the two approaches. The computational results show the promise of the proposed approaches, in that each approach effectively reduces the set of compromise solutions to a reasonably manageable size for the decision-maker. This is a significant step beyond current applications of decision-making process in the presence of multiple stochastic objectives and should serve as an effective approach to support decisionmaking under uncertaint