Determining the Weights of Integer Linear Programming Model using the Analytic Hierarchical Process (AHP)

Ansseif A.latif Ansseif, Aseel Nasser Sabti, Zaid A.Mohammed
Journal of Production and Industrial Engineering
Volume 5: issue2, July-December 2024, pp 48-58

Author's Information
Ansseif A.latif Ansseif 1 
Corresponding Author
1College of Administration and Economics, Al-Iraqia University, Baghdad, Iraq./span>
nsseif.latif@aliraqia.edu.iq

Fryal Jassim Abd Al-Razaq2Zaid A.Mohammed3
2College of Administration and Economics, Al-Iraqia University, Baghdad, Iraq.
Article -- Peer Reviewed
Published online – 30 December 2024

Open Access article under Creative Commons License

Cite this article – Ansseif A.latif Ansseif, Aseel Nasser Sabti, Zaid A.Mohammed,“Determining the Weights of Integer Linear Programming Model using the Analytic Hierarchical Process (AHP)”, Journal of Production and Industrial Engineering, RAME Publishers, vol. 5, Issue 2, pp. 48-58, 2024.
https://doi.org/10.26706/jpie.5.2.20240902

Abstract:
This research presents a well-studied quantitative method based on Integer Linear Programming (ILP) in order to determine the effect of the available alternatives in the company's products through the relative importance weights of the sample variables and based on the Analytic Hierarchical Process (AHP) without resorting to speculative methods in the process of calculating and weighting the weights of the decision variables. investigated sample. Therefore, we will discuss the application of the (AHP) method through a questionnaire prepared for the purpose of determining the Pairwise Comparisons Matrices and the relative importance of a set of criteria and alternatives in the research sample, and thus using it to feed the mathematical model (ILP) to know the effect of these alternatives on the company's products as well maximize the weighting of the available alternatives to take the optimal decision.
Index Terms:
Analytic Hierarchical Process (AHP); Integer Linear Programming (ILP); Pairwise Comparisons Matrices; Eigenvector
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