Modeling the Optimal Diet Problem for Renal Patients with Fuzzy Analysis of Nutrients
International Journal of Management and Fuzzy Systems
Volume 1, Issue 1, June 2015, Pages: 7-14
Received: May 8, 2015;
Accepted: Jul. 1, 2015;
Published: Jul. 2, 2015
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Hossein Eghbali, Department of Industrial Engineering, Eyvanekey nonprofit institution of higher education, Eyvanekey, Iran
Elahe Abdoos, Department of Industrial Engineering, Eyvanekey nonprofit institution of higher education, Eyvanekey, Iran
Sahar Ataee Ashtiani, Department of Industrial Engineering, Eyvanekey nonprofit institution of higher education, Eyvanekey, Iran
Masoud Ahmadvand, Department of Civil Engineering, Eyvanekey nonprofit institution of higher education, Eyvanekey, Iran
This research is intended to analyze optimum nutrition of renal patients in fuzzy environment. One of the important factors in optimized nutrition of renal patients is the daily amount of received nutritious materials. High consumption of proteins, phosphor, salt and potassium enhances the disability of kidneys in these patients. There are several factors for which the accurate determination of nutritious materials present in different foods is impossible. Our purpose in this paper is to present a proper diet model for renal patients in the fuzzy environment. In most studies the daily nutrient intake decisions were made based on crisp data. By prescribing a diet based on crisp data, some of realities are neglected. For the same reason, we dealt with renal patient's diet problem through fuzzy approach. we have provided diet problem as multi-objective fuzzy linear programming problem in which minimization of Protein, Phosphor, Sodium, Potassium are considered as our objectives. Results indicated uncertainty about amount of nutrients and their intake affects diet quality making it more realistic. This research consists of two parts. In the first part, multi-objective fuzzy linear programming problem was investigated and in the second part, practical example of multi-objective fuzzy linear programming problem in relation to optimized diet of human will be presented and solved.
Sahar Ataee Ashtiani,
Modeling the Optimal Diet Problem for Renal Patients with Fuzzy Analysis of Nutrients, International Journal of Management and Fuzzy Systems.
Vol. 1, No. 1,
2015, pp. 7-14.
Bellman, R. E., and Zadeh, L. A., (1970), Decision-making in a fuzzy environment, Management Science 17, B141–B164.
Chaudhuri, A., and De, K.,(2011), Fuzzy multi-objective linear programming for traveling salesman problem, African Journal of Mathematics and Computer Science Research Vol. 4(2), pp. 64-70.
Eghbali, H., Eghbali, M., and Vahidian, A., (2012), Optimizing Human Diet Problem Based on Price and Taste Using Multi-Objective Fuzzy Linear Programming Approach, An International Journal of Optimization and Control: Theories & Applications Vol.2, No.2, pp.139-151.
Ikizler, TA., (2004), Protein and energy: recommended intake and nutrient supplementation in chronic dialysis patients. Semin Dial 17:471-478.
Jana, B., and Kumar Roy, T., (2005), Multi-objective Fuzzy Linear Programming and Application in Transportation Model,Tamsui Oxford Journal of Mathematical Scieces 21(2), 243- 268.
Morais A, Silva A, Faintuch J, et al., (2005), Correlation of nutritional status and food intake in hemodialysis patients. Clinics 60:185-192.
Raffensperger,J.F., (2008), the least-cost low-carbohydrate diet is expensive, Nutr. Res, 28, 6-12.
Sakawa, M., and Yano, H., (1985), Interactive decision making for multi-objective linear fractional programming problems with fuzzy parameters. Cybernetics Systems 16, 377-394.
Tanaka, H., Okuda, T., and Asai, K., (1974), On fuzzy mathematical programming, The Journal of Cybernetics, 3, 37-46.
USDA National Nutrient Database [http://www.nal.usda.gov/fnic/foodcomp/search/]
Wirsam, B., Hahn, A., Uthus, EO., Leitzmann, C., (1997), Fuzzy Sets and Fuzzy Decision Making in Nutrition, European Journal of Clinical Nutrition 51, 286-296.
Zimmermann, H. J., (1987) Fuzzy set, decision making and expert systems, Kluwer Academi Publisher.
Zimmermann, H. J., (2001), Fuzzy set theory and applications.