Installation of a raw factory using the AHP method

Authors

  • Filipe Madeira Instituto Politécnico de Santarém
  • Henrique Madeira Instituto Politécnico de Santarém
  • Rute Vieira Instituto Politécnico de Santarém

DOI:

https://doi.org/10.25746/ruiips.v7.i1.18310

Keywords:

animal raw factory localization, AHP, decision problems, multicriteria, prioritization of alternatives

Abstract

This article aims to present a case study of a real decision problem, with the main objective of assisting an animal production company, on the installation of a new factory.

 

When evaluating the various alternatives, organizations seek clear, objective and, if possible, mathematically represented criteria. However, decision-making involves a cognitive mental process resulting from the selection of the most appropriate course of action, based on various tangible and intangible criteria (Saaty, 2000) arbitrarily chosen by decision makers. In addition to the various criteria, we also have the participation of several decision makers with the common objective of choosing of the best place to guarantee the highest growth of the company. Several methods have been developed to aid decision-making in this scenario.

 

In the described case, we used one of those methods, namely AHP (Analytic Hierarchy Process), to hierarchize the possible location for the installation of the new factory, being the alternatives the following: Carregado, Benavente and Covilhã.

 

In 1980, Professor L. Saaty of the University of Pittsburgh, created AHP, which allows using qualitative and/or quantitative criteria in the evaluation process. This method starts with the construction of a decision tree where the criteria, sub-criteria and the various alternatives are detailed. In a second step, those criteria are compared among the various elements at the same level of hierarchy, using a matrix created by the author - Fundamental Ratio Scale in pair-wise comparison of Saaty -, with values from 1 to 9, depending on their relevance, being 1 of equal importance and the 9 of absolute importance. The third step in the model is to calculate the relative priority of each criteria and to analyze the consistency of the judgments of the decision makers (i.e. the consistency of their responses/ comparisons). At final step, the composite priority matrix for the various alternatives is obtained, reflecting the alternatives hierarchy.

 

According to Forman and Peniwati (1998), the behavior of a decision makers’ group is a factor that will determine how the information will be analyzed and aggregated. Should the group acts as a unit (when individuals in a group essentially desire the best for the organization they represent, regardless of their own preferences, values and goals, acting in tune and making their judgments so that the group behaves as a new individual), a variant of the AHP known as Individual Judgment Aggregation (IJA) is used. For groups that prefer to maintain individual analysis, there is Individual Priority Aggregation (IPA) alternative. In both cases it is possible to assign different weights to the decision makers in the process or to consider them with same degree of importance in terms of the decision. In both cases, the group decision is treated mathematically.

 

In the application of the AHP to our case, two decision makers considered 3 criteria subdivided into several sub-criteria: implementation (sub-criteria: suppliers, cost of installation and distance to current clients), labor (sub-criteria: availability and qualification) and incentives (sub-criteria: social security, and taxes). In this case study, decision-makers' consensus was chosen, using IJA.

 

The application of this method to this real case allowed us to create a model of analysis and support decision, contributing with another tool for this complex decision process.

Published

2019-07-16

How to Cite

Madeira, F., Madeira, H., & Vieira, R. (2019). Installation of a raw factory using the AHP method. Revista Da UI_IPSantarém, 7(1), 59–67. https://doi.org/10.25746/ruiips.v7.i1.18310