The objective of this study was to propose a multi-criteria optimization and decision-making technique to solve food engineering problems. This technique was demonstrated using experimental data obtained on osmotic dehydration of carrot cubes in a sodium chloride solution. The Aggregating Functions Approach, the Adaptive Random Search Algorithm, and the Penalty Functions Approach were used in this study to compute the initial set of non-dominated or Pareto-optimal solutions. Multiple non-linear regression analysis was performed on a set of experimental data in order to obtain particular multi-objective functions (responses), namely water loss, solute gain, rehydration ratio, three different colour criteria of rehydrated product, and sensory evaluation (organoleptic quality). Two multi-criteria decision-making approaches, the Analytic Hierarchy Process (AHP) and the Tabular Method (TM), were used simultaneously to choose the best alternative among the set of non-dominated solutions. The multi-criteria optimization and decision-making technique proposed in this study can facilitate the assessment of criteria weights, giving rise to a fairer, more consistent, and adequate final compromised solution or food process. This technique can be useful to food scientists in research and education, as well as to engineers involved in the improvement of a variety of food engineering processes.
Abakarov A. Paper presented at 11th International Congress on Engineering and Food. Cosmosware; 2011. p. 1525–6.
2.
Abakarov A, Nuñez M. Thermal food processing optimization: algorithms and software. Journal of Food Engineering. 2012.
3.
Abakarov A, Sushkov Y, Almonacid S, Simpson R. Multiobjective Optimization Approach: Thermal Food Processing. Journal of Food Science. 2009. p. 471-E487.
4.
Azarpazhooh E, Ramaswamy H. Modeling and Optimization of Microwave Osmotic Dehydration of Apple Cylinders Under Continuous-Flow Spray Mode Processing Conditions. Food and Bioprocess Technology. 2012. p. 1486–501.
5.
Barbosa-Cánovas G, Vega-Mercado H. Physical, chemical, and microbiological characteristics of dehydrated foods. Chapman & Hall, International Thompson Publishing; 1996. p. 29–99.
6.
Belton V, Stewart T. Multiple criteria decision analysis: an integrated approach. Kluwer Academic Pub; 2002.
7.
Beuthe M, Scannella G. Comparative analysis of UTA multicriteria methods. European Journal of Operational Research. 2001. p. 246–62.
8.
Bevilacqua M, ’amore D, Polonara A, F. A multi-criteria decision approach to choosing the optimal blanchingfreezing system. Journal of Food Engineering. 2004. p. 253–63.
9.
Brockhoff D, Zitzler E. Objective Reduction in Evolutionary Multiobjective Optimization: Theory and Applications. Evolutionary Computation. 2009. p. 135–66.
10.
Carmone F, Kara A, Zanakis S. A Monte Carlo investigation of incomplete pairwise comparison matrices in AHP. European Journal of Operational Research. 1997. p. 538–53.
11.
Costa C, Chagas M. 12th Mini Euro Confer-ence. European Journal of Operational Research. 2004. p. 323–31.
12.
Deb K. Multi-objective optimization using evolutionary algorithms. Wiley-Interscience series in systems and optimization. John Wiley & Sons; 2001.
13.
Deb K, Saxena D. Proc. of 2006 IEEE Congress on Evolutionary Computation. IEEE Press; 2006. p. 3353–60.
14.
Del Castillo E. Process optimization: a statistical approach. Springer; 2007.
15.
Derringer G, Suich R. Simultaneousoptimization of Several Response Variables. Journal of Food Engineering. 1980. p. 214–9.
16.
Derringer G. A Balancing Act -Optimizing a Products Properties. Quality Progress. 1994. p. 51–8.
17.
Erdogdu F. Optimization in Food Engineering. Journal of Food Engineering. 2003. p. 1–777.
18.
Eren I, Kaymak-Ertekin F. Optimization of osmotic dehydration of potato using response surface methodology. Journal of Food Engineering. 2007. p. 344–52.
19.
Multiple Criteria Decision Analysis: State of the Art Surveys. 2005. p. 1–1085.
20.
Fogliatto F, Albin S. An AHP-based procedure for sensory data collection and analysis in quality and reliability applica-tions. Food Quality and Preference. 2003. p. 375–85.
21.
Goñi S, Salvadori V. Proceedings of the 11th International Congress on Engineering and Food. Paper presented at 11th International Congress on Engineering and Food. Cosmosware; 2011. p. 1425.
22.
Goñi S, Salvadori V. Modelbased multi-objective optimization of beef roasting. Journal of Food Engineering. 2012. p. 92–101.
23.
Grandzol J. Improving the faculty selection process in higher education: a case for the analytic hierarchy process. IR Applications. 2005. p. 1–13.
24.
Hadiyanto H, Boom R, Van Straten G, Boxtel V, Esveld A, D. Journal of Food Engineering. 2009. p. 709–29.
25.
Joshua D, David D. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation. 2000. p. 149–72.
26.
Keeney R, Raiffa H. Decisions with Multiple Objectives, Preferences and Value Trade Offs. Cambridge University Press; 1993.
27.
Kiranoudis C, Markatos N. Pareto design of conveyor-belt dryers. Journal of Food Engineering. 2000. p. 145–55.
28.
Marler R, Arora J. Survey of multiobjective optimization methods for engineering. Structural and Multidisciplinary Optimization. 2004. p. 369–95.
29.
Mehnen J, Trautmann H. Proceedings of the 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’06). CIRP International Seminar on Intelligent Computation in Manufacturing Engineering. 2006. p. 293–8.
30.
Miller G. The Magical Number 7, Plus or Minus 2 -Some Limits on our Capacity for Processing Information. Psychological Review. 1956. p. 81–97.
31.
Noshad M, Mohebbi M, Shahidi F, Mortazavi S. Multi-Objective Optimization of Osmotic-Ultrasonic Pretreatments and Hot-Air Drying of Quince Using Response Surface Methodology. Food Bioprocess Technology. 2012. p. 2098–110.
32.
Purshouse R, Fleming P, Fonseca C, Fleming, Zitzler E, Deb, et al. LECTURE NOTES IN COMPUTER SCIENCE. 2nd International Conference on Evolutionary Multi-Criterion Optimization. Proceedings. 2003. p. 16–30.
33.
Saaty T. Efficient and robust multiobjective optimization of food processing: A novel approach with application to thermal sterilization. European Journal of Operational Research. 1990. p. 317–24.
34.
Seng C, Rangaiah G. Optimization in Food Engineering. 2008. p. 153–78.
35.
Sharma S, Rangaiah G, Cheah K. Food and Bioproducts Processing. 2012. p. 123–34.
36.
Siinivas N, Deb K. Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation. 1994. p. 221–48.
37.
Singh B, Paramjit S, Nanda V, Bera M. Optimization of Osmotic Dehydration Process of Carrot Cubes in Sodium Chloride Solution. International Journal of Food Engineering. 2008.
38.
Statnikov R, Matusov J. Multicriteria Optimization and Engineering. Chapman & Hall; 1995.
39.
Statnikov R, Bordetsky A, Statnikov A. Multicriteria analysis of real-life engineering optimization problems: statement and solution. Nonlinear Analysistheory Methods & Applications. 2005. p. 685-E696.
40.
Steuer R. Multiple criteria optimization: theory, computation and application. JohnWiley& Sons; 1985.
41.
Sushkov Y. Multi-objective optimization of multi-regime systems. Digital System Architecture. 1984. p. 21–4.
42.
Tan K, Khor E, Lee T. Multiobjective evolutionary algorithms and applications. Advanced Information and Knowledge Processing. 2005.
43.
Thakur M, Wang L, Hurburgh C. A multi-objective optimization approach to balancing cost and traceability in bulk grain handling. Journal of Food Engineering. 2010. p. 193–200.
44.
Treitz M, Schollenberger I, Schrader B, Geldermann J, Rentz O. RadTech Europe 2005, Conference Proceeding. 2005. p. 155–60.
45.
Vansnick J. On the Problem of Weights in Multiple Criteria Decision-Making (the Noncompensatory Approach). European Journal of Operational Research. 1986. p. 288–94.
46.
Vassilev V, Genova K, Vassileva M. A Brief Survey of Multicriteria Decision Making Methods and Software Systems. Cybernetics and Information Technologies. 2005. p. 3–14.
47.
Winterfeldt V, Edwards D, W. Decision Analysis and Behavioral Research. Cambridge University Press; 1986.
48.
Xu S, Xiufang C, Da-Wen S, Zhigljavsky A, Zilinskas A. Stochastic Global Optimization. 2001. p. 1–262.
49.
Zitzler E, Thiele L. Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach. IEEE Transactions on Evolutionary Computation. 1999. p. 257–71.
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