Extended Goal Programming DASH Diet Plan for Stroke Patients

Authors

  • Iwuji, Anayo Charles Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria.
  • Okoroafor, Promise Izuchukwu University of Ibadan, Ibadan
  • Owo Awa, Josephine Ezinne Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria

Keywords:

Extended Goal Programming, Goal Programming Variants, Stroke Diet, DASH eating plan, Diet Optimization.

Abstract

Communication in Physical Sciences, 2024, 11(4): 828-837

Authors: Iwuji, Anayo Charles,  Okoroafor, Promise Izuchukwu*, Owo Awa, Josephie Ezinne

Received: 14 March 2024/Accepted: 10 September  2024

Goal Programming (GP) optimizes decisions in diet planning by computing efficient solutions that minimize deviations from the recommended nutrient goals target levels. Extended Goal Programming (EGP) enhances the flexibility of the GP model by using additional maximal deviation parameters that create a balance between efficiency and equity in the model. This work presents an EGP 2000-calorie daily Dietary Approaches to Stop Hypertension (DASH) diet plan for stroke patients. The proposed diet plan minimizes deviations from daily recommended nutrient targets, addressing the dual role of diet in stroke prevention and recovery. Data from the recommended food chart and nutrient levels were collected from the Nutritional Epidemiology Institute and DASH diet plan bulletins while the food samples and weights were obtained from Abia State, Nigeria. This study achieves three objectives: formulating an EGP diet model, presenting an efficient diet plan, and comparing results with those of other GP model variants. LINGO software is used in the analysis. The diet plan obtained showed six goals targets out of nine were achieved. A comparison of the EGP diet plan with the Chebyshev GP diet plan highlights the EGP’s flexibility and efficiency than the latter. 

Downloads

Download data is not yet available.

Author Biographies

Iwuji, Anayo Charles, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria.

Department of Statistics

Okoroafor, Promise Izuchukwu, University of Ibadan, Ibadan

Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine

Owo Awa, Josephine Ezinne, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria

Department of Statistics

References

Abdallah, M., & Kapelan, Z. (2017). Iterative extended lexicographic goal programming method for fast and optimal pump scheduling in water distribution networks. Journal of Water Resources Planning and Management, 143, 11 https://doi.org/10.1061/(ASCE)WR.1943-5452.0000843.

Alam, T. (2022). Modeling and analyzing a multi-objective financial planning model using goal programming. Appl. Syst. Innov, 5, 128 https://doi.org/10.3390/asi5060128

Gerdessen, J. C., & De Vries, J. H. (2015), Diet models with linear goal programming: impact of achievement functions. European Journal of Clinical Nutrition, 69, 11, pp. 1272-1278. DOI: 10.1038/ejcn.2015.56.

Ignizio, J. P. (1978). A Review of Goal Programming: A Tool for Multiobjective Analysis. The Journal of the Operational Research Society, 29, 11, pp. 1109-1119.

Ijiri, Y.(1965) Management Goals and Accounting for Control. Rand-McNally, Chicago.

Iwuji, A. C., & Agwu, E. U. (2017). A weighted goal programming model for the Dash diet problem: comparison with the linear programming DASH diet model. American Journals of Operations Research, 7,. Pp. 307-322.10. DOI;4236/ajor.2017.75023

Jones, D. F., Florentino, H., Cantane, D., & Oliveira, R. (2016). An extended goal programming methodology for analysis of a network encompassing multiple objectives and stakeholders. European Journal of Operational Research, 225, 3, pp. 845-855.

Jones, D. F., And Wall, G. (2016) An extended goal programming model for site selection in the offshore wind farm sector. Annals of Operations Research 245, 1, pp. 121-135.

Koenen, M.F.,Balvert, M. & Fleuren, H. (2022). Bi-objective goal programming for balancing cost vs nutritional adequacy. Frontiers in Nutrition, 1-22. 9:1056205. doi: 10.3389/fnut.2022.1056205

Larsson S.C. (2017). Dietary Approaches for Stroke Prevention. Stroke, 48, 10, pp. 2905-2911. Doi: 10.1161/STROKEAHA.117.017383.

Lin, C.-L. (2021). Stroke and diets – A review. Tzu Chi Medical Journal, 33(3), 238. https://doi.org/10.4103/tcmj.tcmj_168_20

Muhammad et al. (2015). Multi-objective Compromise Allocation in Multivariate Stratified Sampling Using Extended Lexicographic Goal Programming with Gamma Cost Function. J Math Model Algor, 14, 2, pp. 255–265. https://doi.org/10.1007/s10852-014-9270

Oliveira, W. A., Fiorotto, D. J., Song, X., & Jones, D. F. (2021). An extended goal programming model for the multiobjective integrated lot-sizing and cutting stock problem. European Journal of Operational Research, 295, 3, pp. 996-1007.

Romero, C. (1991). On Misconceptions in Goal Programming. The Journal of the Operational Research Society, 42, pp. 927-928.

Sinha, B., & Sen, N. (2011). Goal programming approach to the tea industry of Barak Valley of Assam. Applied Mathematical Sciences, 5, 29, pp. 1409-1419.

Downloads

Published

2024-09-19