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Product and process design can be largely improved using modern modeling, simulation, and optimization techniques.{{citation needed|date=February 2017}} The key question in optimal design is measuring what is good or desirable about a design. Before looking for optimal designs, it is important to identify characteristics that contribute the most to the overall value of the design. A good design typically involves multiple criteria/objectives such as capital cost/investment, operating cost, profit, quality and/or product recovery, efficiency, process safety, operation time, etc. Therefore, in practical applications, the performance of process and product design is often measured with respect to multiple objectives. These objectives are typically conflicting, i.e., achieving the optimal value for one objective requires some compromise on one or more objectives.
For example, when designing a paper mill, one can seek to decrease the amount of capital invested in a paper mill and enhance the quality of paper simultaneously. If the design of a paper mill is defined by large storage volumes and paper quality is defined by quality parameters, then the problem of optimal design of a paper mill can include objectives such as i) minimization of expected variation of those quality parameters from their nominal values, ii) minimization of the expected time of breaks and iii) minimization of the investment cost of storage volumes. Here, the maximum volume of towers is a design variable. This example of optimal design of a paper mill is a simplification of the model used in.<ref name=RoRiPi11>{{Cite journal | last1 = Ropponen | first1 = A. | last2 = Ritala | first2 = R. | last3 = Pistikopoulos | first3 = E. N. | doi = 10.1016/j.compchemeng.2010.12.012 | title = Optimization issues of the broke management system in papermaking | journal = Computers & Chemical Engineering | volume = 35 | issue = 11 | pages = 2510 | year = 2011 }}</ref> Multi-objective design optimization has also been implemented in engineering systems in the circumstances such as control cabinet layout optimization,<ref>{{cite arXiv|last1=Pllana |first1=Sabri |last2=Memeti |first2=Suejb |last3=Kolodziej |first3=Joanna |title=Customizing Pareto Simulated Annealing for Multi-objective Optimization of Control Cabinet Layout |eprint=1906.04825 |class=cs.OH |year=2019}}</ref> airfoil shape optimization using scientific workflows,<ref>{{Cite web |title=Bi-level airfoil optimization |url=https://www.pseven.io/blog/use-cases/bi-level-airfoil-optimization.html |access-date=2025-01-14 |website=www.pseven.io |language=en}}</ref><ref>{{cite journal |last1=Nguyen |first1=Hoang Anh |last2=van Iperen |first2=Zane |last3=Raghunath |first3=Sreekanth |last4=Abramson |first4=David |last5=Kipouros |first5=Timoleon |last6=Somasekharan |first6=Sandeep |title=Multi-objective optimisation in scientific workflow |journal=Procedia Computer Science |date=2017 |volume=108 |pages=1443–1452 |hdl=1826/12173|doi=10.1016/j.procs.2017.05.213|doi-access=free |hdl-access=free }}</ref> design of nano-[[CMOS]],<ref>{{Cite journal|title = Multiobjective design optimization of a nano-CMOS voltage-controlled oscillator using game theoretic-differential evolution|journal = Applied Soft Computing|date = 2015-07-01|pages = 293–299|volume = 32|doi = 10.1016/j.asoc.2015.03.016|first1 = T.|last1 = Ganesan|first2 = I.|last2 = Elamvazuthi|first3 = P.|last3 = Vasant}}</ref> [[System on a chip|system on chip]] design, design of solar-powered irrigation systems,<ref>{{Cite book|publisher = Springer International Publishing|date = 2013-01-01|isbn = 978-3-319-00541-6|pages = 147–154|series = Advances in Intelligent Systems and Computing|doi = 10.1007/978-3-319-00542-3_15|first1 = T.|last1 = Ganesan|first2 = I.|last2 = Elamvazuthi|first3 = Ku Zilati Ku|last3 = Shaari|first4 = P.|last4 = Vasant| title=Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems | chapter=Hypervolume-Driven Analytical Programming for Solar-Powered Irrigation System Optimization | volume=210 |editor-first = Ivan|editor-last = Zelinka|editor-first2 = Guanrong|editor-last2 = Chen|editor-first3 = Otto E.|editor-last3 = Rössler|editor-first4 = Vaclav|editor-last4 = Snasel|editor-first5 = Ajith|editor-last5 = Abraham}}</ref> optimization of sand mould systems,<ref>{{Cite book|publisher = Springer Berlin Heidelberg|date = 2013-01-01|isbn = 978-3-642-45317-5|pages = 145–163|series = Lecture Notes in Computer Science|first1 = T.|last1 = Ganesan|first2 = I.|last2 = Elamvazuthi|first3 = Ku Zilati Ku|last3 = Shaari|first4 = P.|last4 = Vasant| title=Transactions on Computational Science XXI | chapter=Multiobjective Optimization of Green Sand Mould System Using Chaotic Differential Evolution | volume=8160 |editor-first = Marina L.|editor-last = Gavrilova|editor-link=Marina Gavrilova|editor-first2 = C. J. Kenneth|editor-last2 = Tan|editor-first3 = Ajith|editor-last3 = Abraham|doi = 10.1007/978-3-642-45318-2_6}}</ref><ref>{{cite journal|title = Multi-objective optimization of green sand mould system using evolutionary algorithms|journal = The International Journal of Advanced Manufacturing Technology|date = 2011-05-07|issn = 0268-3768|pages = 9–17|volume = 58|issue = 1–4|doi = 10.1007/s00170-011-3365-8|first1 = B.|last1 = Surekha|first2 = Lalith K.|last2 = Kaushik|first3 = Abhishek K.|last3 = Panduy|first4 = Pandu R.|last4 = Vundavilli|first5 = Mahesh B.|last5 = Parappagoudar|s2cid = 110315544}}</ref> engine design,<ref>{{Cite web|title = MultiObjective Optimization in Engine Design Using Genetic Algorithms to Improve Engine Performance {{!}} ESTECO|url = http://www.esteco.com/modefrontier/multiobjective-optimization-engine-design-using-genetic-algorithms-improve-engine-perfo|website = www.esteco.com|access-date = 2015-12-01}}</ref><ref>{{cite book|chapter = Multi-Objective Robust Design Optimization of an Engine Mounting System|chapter-url = http://papers.sae.org/2005-01-2412/|date = 2005-05-16|___location = Warrendale, PA|first1 = E.|last1 = Courteille|first2 = F.|last2 = Mortier|first3 = L.|last3 = Leotoing|first4 = E.|last4 = Ragneau|doi = 10.4271/2005-01-2412|title = SAE Technical Paper Series|volume = 1| s2cid=20170456 |url = https://hal.archives-ouvertes.fr/hal-00913315/file/SAE_HAL.pdf}}</ref> optimal sensor deployment<ref>{{cite journal|last1=Domingo-Perez|first1=Francisco|last2=Lazaro-Galilea|first2=Jose Luis|last3=Wieser|first3=Andreas|last4=Martin-Gorostiza|first4=Ernesto|last5=Salido-Monzu|first5=David|last6=Llana|first6=Alvaro de la|title=Sensor placement determination for range-difference positioning using evolutionary multi-objective optimization|journal=Expert Systems with Applications|date=April 2016|volume=47|pages=95–105|doi=10.1016/j.eswa.2015.11.008}}</ref> and optimal controller design.<ref>{{Cite journal|title = Multiobjective model predictive control|journal = Automatica|date = 2009-12-01|pages = 2823–2830|volume = 45|issue = 12|doi = 10.1016/j.automatica.2009.09.032|first1 = Alberto|last1 = Bemporad|first2 = David|last2 = Muñoz de la Peña}}</ref><ref>{{cite journal|title = Multi-objective evolutionary algorithm for SSSC-based controller design|journal = Electric Power Systems Research|date = 2009-06-01|pages = 937–944|volume = 79|issue = 6|doi = 10.1016/j.epsr.2008.12.004|first = Sidhartha|last = Panda}}</ref>
=== {{anchor|MOGA}} Process optimization ===
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