TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) ranking skill for multi-criteria evaluation
Ranks alternatives by comparing their geometric distance to ideal and anti-ideal solutions for multi-criteria decisions.
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The TOPSIS Ranker skill implements the Technique for Order of Preference by Similarity to Ideal Solution methodology for multi-criteria decision analysis. It ranks alternatives based on their geometric distance from ideal and anti-ideal solutions, providing intuitive and mathematically sound rankings.
# Define decision matrix (alternatives x criteria)
decision_matrix = {
"alternatives": ["Option A", "Option B", "Option C", "Option D"],
"criteria": ["Cost", "Quality", "Time", "Risk"],
"values": [
[100000, 85, 12, 3], # Option A
[150000, 92, 8, 2], # Option B
[80000, 78, 15, 4], # Option C
[120000, 88, 10, 2] # Option D
],
"weights": [0.3, 0.35, 0.2, 0.15],
"criteria_type": ["cost", "benefit", "cost", "cost"] # minimize/maximize
}
C* = S- / (S+ + S-)
{
"decision_matrix": {
"alternatives": ["string"],
"criteria": ["string"],
"values": "2D array of numbers",
"weights": ["number"],
"criteria_type": ["benefit|cost"]
},
"options": {
"normalization_method": "vector|linear|max_min",
"sensitivity_analysis": "boolean",
"visualization": "boolean"
}
}
{
"ranking": [
{
"alternative": "string",
"rank": "number",
"closeness_coefficient": "number",
"distance_to_ideal": "number",
"distance_to_anti_ideal": "number"
}
],
"ideal_solution": "object",
"anti_ideal_solution": "object",
"sensitivity_results": {
"weight_sensitivity": "object",
"rank_stability": "object"
},
"visualization_path": "string"
}
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