It assists stakeholders and researchers with better understanding of weight sensitivity for characterising, reporting and minimising uncertainty in the AHP-based spatial MCDM. A case study with in-depth discussion is provided to demonstrate the new toolset. With increased efficiency, improved flexibility and enhanced visualization capability, the spatial framework was developed as AHP-SA2 within a GIS platform. (2010) to a more comprehensive framework to analyze weight sensitivity caused by both direct and indirect weight changes using the one-at-a-time (OAT) technique. The steps taken in the AHP method are 23 : 1) Normalization on the pairwise comparison matrix which is intended as follows : a) The sum of each value in the columns of the comparison matrix. This study developed a unique methodology which extends the AHP-SA model proposed by Chen et al. The Analytical Hierarchy Process (AHP) decision-making process is basically choosing the best alternative 22. This problem can solve by using two methods that support the decision of the Analytic Hierarchy Process (AHP) for the process of weighting criteria and Simple Additive Weighting (SAW) to rank each alternative. For assigning weights to the alternatives as well as criteria for. To get quality crop yields, according to the environmental requirements, each type of food plant grows. AHP has broad applications in operations research, quality engineering, and design-for-six-sigma (DFSS) situations. Thomas Saaty developed AHP as a decision-making method in the 1970s. AHP helps decision-makers choose the best solution from several options and selection criteria. Corresponding weight sensitivity on multi-criteria evaluation results is generally difficult to be quantitatively assessed and spatially visualized. Here, Decisions Matrix and Pair wise Comparison. SAS/IML® can be used to implement the Analytic Hierarchy Process (AHP). During an MCDM process, the weights can be changed directly by adjusting the output from a pairwise comparison matrix, or indirectly by recalculating the matrix after varying its input. Criteria weights determined from pairwise comparisons are often the greatest contributor to the uncertainties in the AHP-based multi-criteria decision making (MCDM).
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