Multi-Criteria Decision Making (MCDM) Using Matlab and Excel

Explore the simple additive weighting (saw), also known as weighted linear combination, a multi-attribute decision method that computes scores by weighted criteria and data normalization.

Compute weights that sum to one, build a seven-criteria decision matrix for five candidates, normalize for benefit attributes, apply weights, and rank to select the best.

Demonstrate multi-criteria decision making in Excel and MATLAB by selecting a hotel using weights, a decision matrix, normalization, and ranking via AHP or similar methods (example 2).

This example applies multi-criteria decision making to rank manufacturing systems using Matlab and Excel, by selecting criteria, assigning weights, building a decision matrix, normalizing data, and ranking four alternatives.

Learn how to apply the analytic hierarchy process (AHP) to multi-criteria decision making with pairwise comparisons, rating scales, and consistency ratio to derive weights and rankings in Excel or Matlab.

Apply AHP in Excel to build and normalize a four-criteria decision matrix, compute weights, and assess consistency with the consistency ratio (CR) in a structured MCDM workflow.

Demonstrates a general AHP framework using a 15 by 15 matrix in a spreadsheet to derive criteria weights, normalize data, and compute the consistency ratio and lambda.

this lecture demonstrates implementing the analytic hierarchy process in MATLAB, calculating weights and the consistency ratio from a decision matrix and pairwise comparisons, with a function and example.

Explore AHP in a multi-criteria decision making framework to rank four alternatives by pairwise criteria and alternatives, with Matlab and Excel yielding identical weights and selecting A4.

Apply multi-criteria decision making (mcdm) with MATLAB and Excel to determine weights across the goal, criteria, subcriteria, and alternatives, and rank A4 as the best option.

Explore the analytical network process (ANP) as a flexible, network-based extension of AHP for multi-criteria decision making, emphasizing a super matrix, pairwise comparisons, and interdependencies among criteria, subcriteria, and alternatives.

learn how to build a super matrix for AHP with goal, criteria, and alternatives, apply weights, and use MATLAB to obtain the limited matrix and final weights.

Learn to construct and normalize a four-layer supermatrix in AHP, compute weights of alternatives with respect to criteria and subcriteria, and compare results in Matlab and Excel.

Demonstrate ANP with a supermatrix for AHP, linking criteria C1, C2, C3, performing pairwise comparisons to derive weights, then normalize and compute final weights in Matlab and Excel.

Explore how ANP converts an HP model into ANP with internal relations among criteria and sub-criteria, builds a weighted, normalized supermatrix, and yields the limited metrics.

This lecture introduces TOPSIS, a multi-criteria decision making method ranking alternatives by distance to the ideal solution, using weighted normalization, positive and negative ideal solutions, and separation measures.

Apply TOPSIS in Excel to select the best alternative by normalizing a six-by-seven decision matrix, weighting criteria, computing A+ and A- (positive and negative ideals), and ranking alternatives.

Learn to implement TOPSIS in MATLAB to solve multi-criteria decision making problems, including building and normalizing the decision matrix, applying weights, and computing AP, AM, S+, S-, and C.

Explore the electre method, an outranking multi-criteria decision approach, by normalizing the decision matrix, weighting criteria, and building concordance and discordance matrices to rank alternatives.

Learn to implement electre in Excel by normalizing data, computing concordance and discordance sets, building matrices, and ranking alternatives for optimal decision.

Implement electre in Matlab to solve multi-criteria decisions with a weighted normalized decision matrix. Build concordance and discordance sets and matrices, compute thresholds, and identify the optimal alternative.

Introduce the Promethee method, an outrank-based MCDM approach that ranks alternatives using pairwise preferences, weights, normalization, and positive and negative outranking flows.

Learn to implement PROMETHEE in Excel for MCDM, using normalization, deviations, and the preference function across six criteria and four alternatives, then compute outranking flows and rank.

Implement Promethee in Matlab by transforming a decision matrix with weights, normalizing data, and computing the preference function, preference index, and net outranking to rank alternatives.

Explore the Vikor method for multi-criteria decision making, ranking alternatives by closeness to an ideal solution using s, r, and q. Learn steps to implement the core approach in Excel.

Apply the Vikor method in Excel to compute s, r, and q, weight the criteria, and identify a compromise solution among alternatives 2 and 4.

Translates a VIKOR MCDM workflow from Excel to Matlab, building the decision matrix and weights, then computes f best, f worst, L, S, R, and Q to rank alternatives.

Dematel method models cause-and-effect in complex systems using expert panels and pairwise comparisons. Compute a total relation matrix, apply normalization, and derive an impact relationship diagram.

Dematel in Excel shows aggregating expert opinions into an A matrix, normalizing to D, computing T from I minus D, and building causal effect diagram for four criteria.

Aggregate expert opinions into a single matrix and compute the DEMATEL prominence and relation values in Matlab to generate the causal effects diagram.

Explore grey relational analysis (gra) for mcdm, including normalization of the decision matrix, deviation sequences, delta min/max, and grey relational coefficients with weights to rank alternatives.

Learn to implement grey relational analysis in Excel to rank four cars across four attributes by normalizing data, computing relational coefficients and grades, and selecting the top alternative.

Explore how grey relational analysis is implemented in Matlab to solve multi-criteria decision making problems, including data processing, normalization, delta calculations, and ranking alternatives.