Make informed decisions by assigning weights and scores to different criteria using the additive weighting method.
Additive Weighting Calculator
Enter the total number of decision criteria (e.g., 2-10).
Results Summary
—
Total Score Sum: —
Weighted Score Sum: —
Average Score: —
The additive weighting method calculates a weighted score for each option by summing the products of each criterion's score and its assigned weight. The formula is: Score = Σ (Score_i * Weight_i).
Detailed Scores
Option/Criterion
Weight
Score
Weighted Score
Detailed breakdown of scores and weighted scores for each criterion and option.
Weighted Score Comparison
Visual comparison of weighted scores across different options.
{primary_keyword}
The additive weighting method, also known as weighted sum model (WSM), is a simple yet powerful multi-criteria decision analysis (MCDA) technique. It's used to evaluate and rank multiple options based on a set of predefined criteria, each with a specific importance (weight). This method is particularly useful when you need to make a complex decision involving several factors that cannot be easily compared on a single scale. By assigning numerical values to both the criteria's importance and how well each option performs against them, the additive weighting method provides a quantitative basis for comparison, helping to identify the most suitable option.
Who should use it? Individuals and organizations facing choices where multiple, often conflicting, factors are involved. This includes project selection, vendor evaluation, hiring decisions, product development prioritization, and even personal choices like selecting a car or a place to live. If you have a decision with several important aspects and want a structured way to weigh them, the additive weighting method is an excellent tool.
Common misconceptions about the additive weighting method include the belief that it's overly simplistic or subjective. While it does involve subjective input (weights and scores), the method provides a *structured framework* for that subjectivity. It doesn't eliminate judgment but rather guides and quantifies it. Another misconception is that it requires complex mathematical expertise; in reality, the core calculation is a straightforward sum of products, making it accessible to most users. It's important to remember that the quality of the output is highly dependent on the quality of the input criteria, weights, and scores.
{primary_keyword} Formula and Mathematical Explanation
The core of the additive weighting method lies in its ability to transform diverse criteria into a single, comparable score for each option. This is achieved through a systematic process of assigning importance to each criterion and then scoring each option against those criteria.
The mathematical formula for the additive weighting method is as follows:
Overall Score for Option 'A' = Σ (Score of Option A for Criterion 'i' * Weight of Criterion 'i')
Where:
'A' represents a specific option being evaluated.
'i' represents a specific criterion used in the decision-making process.
'Score of Option A for Criterion i' is the performance rating of Option A on Criterion i. This is typically on a standardized scale (e.g., 1-5, 1-10).
'Weight of Criterion i' is the importance assigned to Criterion i. This is often expressed as a percentage or a decimal, summing up to 1 (or 100%).
'Σ' denotes the summation across all criteria (i = 1 to n, where n is the total number of criteria).
Variable Explanations and Table
To effectively use the additive weighting method, understanding each component is crucial:
Variable
Meaning
Unit
Typical Range
Number of Criteria (n)
The total count of factors considered in the decision.
Count
≥ 1
Weight (Wi)
The relative importance assigned to each criterion.
Decimal (0-1) or Percentage (0-100%)
Sum of weights = 1 (or 100%)
Score (SAi)
The performance rating of a specific option (A) on a specific criterion (i).
Numerical Scale (e.g., 1-5, 1-10)
Defined by user, e.g., 1 (poor) to 5 (excellent)
Weighted Score (WSAi)
The score of an option on a criterion, adjusted by the criterion's weight (SAi * Wi).
Product of units (e.g., Score Unit * Weight Unit)
Varies based on input ranges
Overall Score (Option A)
The sum of all weighted scores for a specific option across all criteria.
Sum of Weighted Scores
Varies based on input ranges
Explanation of variables used in the additive weighting method.
Practical Examples (Real-World Use Cases)
The additive weighting method is versatile. Here are two practical examples demonstrating its application:
Example 1: Selecting a New Office Location
A company needs to choose a new office location from three options (Option A, B, C). They identify three key criteria:
Cost (Weight: 40% or 0.40) – Lower cost is better.
Accessibility (Weight: 35% or 0.35) – Easier access is better.
Amenities (Weight: 25% or 0.25) – More amenities are better.
They assign scores (1-5, where 5 is best, but for cost, it's inverted: 1 is most expensive, 5 is cheapest) to each option:
Interpretation: Option C receives the highest overall score (4.10), making it the preferred choice based on these weighted criteria. Option B is a close second. Option A is the least preferred.
Example 2: Prioritizing Software Development Features
A software team must decide which features to develop next from three potential features (Feature X, Y, Z). Criteria and weights are:
User Demand (Weight: 50% or 0.50)
Development Effort (Weight: 30% or 0.30) – Lower effort is better, so scores are inverted (1=high effort, 5=low effort).
Interpretation: Feature Y has the highest weighted score (4.30), indicating it's the top priority. This is driven largely by high user demand and moderate effort. Feature X is second, and Feature Z is the lowest priority.
How to Use This Additive Weighting Calculator
Our Additive Weighting Calculator simplifies the process of making complex decisions. Follow these steps:
Set the Number of Criteria: First, decide how many factors (criteria) are important for your decision. Enter this number in the 'Number of Criteria' field.
Define Your Criteria: For each criterion, you will need to input:
Criterion Name: A clear label for the factor (e.g., "Price", "Speed", "Ease of Use").
Weight: How important is this criterion relative to others? Enter a value between 0 and 1 (e.g., 0.4 for 40%) or use percentages if the calculator supports it. Ensure all weights sum up to 1 (or 100%).
Scoring Direction: Indicate if higher scores are better (e.g., "Higher is Better") or if lower scores are better (e.g., "Lower is Better"). This is crucial for cost or effort criteria.
Define Your Options: The calculator will prompt you to enter the options you are considering. For each option, you'll assign a score (typically 1-5 or 1-10) for *each* criterion, based on the scoring direction you defined.
Calculate: Click the "Calculate" button. The calculator will perform the additive weighting for each option.
Interpret Results:
Main Result: The highest overall score indicates the preferred option based on your defined criteria and weights.
Intermediate Values: Review the total score sum, weighted score sum, and average score for context.
Detailed Scores Table: See the breakdown of how each option performed on each criterion and its contribution to the overall score.
Chart: The bar chart visually compares the overall weighted scores of your options, making it easy to see the relative rankings.
Decision Making: Use the results as a primary guide for your decision. Remember that the additive weighting method quantifies your priorities, but consider qualitative factors as well.
Copy Results: Use the "Copy Results" button to save or share your calculation details.
Reset: Use the "Reset" button to clear all inputs and start over.
Key Factors That Affect Additive Weighting Results
Several factors significantly influence the outcome of an additive weighting analysis. Understanding these can help you refine your inputs and achieve more meaningful results:
Criteria Selection: The choice of criteria is paramount. If critical factors are omitted or irrelevant ones are included, the results will not accurately reflect the desired decision. Ensure criteria are relevant, measurable, and distinct. For instance, when choosing a project, criteria like "Potential ROI," "Risk Level," and "Resource Requirements" are essential.
Weight Assignment: This is often the most subjective part. Over- or under-weighting a criterion can drastically alter rankings. Assigning weights requires careful consideration of true priorities. For example, if "Cost" is assigned a weight of 0.10 (10%) but is actually the most critical factor, the resulting scores might be misleading. Exploring different weighting scenarios can be insightful.
Scoring Scale and Consistency: The range and definition of the scoring scale (e.g., 1-5, 1-10) must be consistently applied across all options and criteria. A scale that is too narrow might not differentiate well between options, while one that is too broad can lead to inconsistent judgments. Ensuring clear definitions for each score point (e.g., what constitutes a '4' for 'Ease of Use'?) is vital.
Scoring Accuracy: The scores assigned to each option for each criterion should be as objective and accurate as possible, based on available data or expert judgment. Subjective or biased scoring will lead to skewed results. For example, scoring a vendor's "Reliability" based on hearsay rather than performance data reduces the analysis's validity.
Normalization (Implicit or Explicit): While this calculator uses raw scores and weights, more complex applications might require normalizing scores (especially if criteria have different scales or units) before applying weights. Our calculator assumes consistent scoring scales. The choice of weights implicitly normalizes the importance.
Number of Options: Evaluating a very large number of options can become cumbersome. The additive weighting method is most effective when the number of alternatives is manageable, allowing for thorough scoring and comparison.
Inverted Scoring Logic: For criteria where lower values are better (like cost, risk, or time), correctly inverting the score is crucial. Failing to do so means that a high "cost" (which is bad) would receive a high score, artificially boosting the option's overall score. The calculator handles this with the "Lower is Better" option.
Frequently Asked Questions (FAQ)
What is the primary output of the additive weighting method?
The primary output is a ranked list of options, with the option receiving the highest overall weighted score considered the most preferable based on the defined criteria and their assigned importance.
Can the additive weighting method handle qualitative criteria?
Yes, qualitative criteria can be included by assigning numerical scores based on defined scales. For example, "Customer Service Quality" could be scored from 1 (poor) to 5 (excellent).
What is the difference between weight and score?
The weight represents the importance or priority of a criterion in the decision. The score represents how well a specific option performs on that criterion.
Do the weights need to add up to 100%?
It's a best practice for the weights to sum to 1 (if using decimals) or 100 (if using percentages) to represent relative importance clearly. However, the mathematical calculation still works if they don't sum to 100, but the interpretation of weights as precise proportions is lost. Our calculator enforces this for clarity.
What if I have criteria where lower is better (e.g., cost)?
You must assign scores accordingly. Either assign a lower numerical score for a higher cost, or use the "Lower is Better" scoring direction setting provided by the calculator. This ensures the criterion negatively impacts the overall score as desired.
How sensitive are the results to changes in weights?
The results can be quite sensitive. A small change in the weight of a highly-scored criterion can significantly alter an option's overall score. It's often useful to perform a sensitivity analysis by adjusting weights to see how rankings change.
Is the additive weighting method subjective?
Yes, the assignment of weights and scores involves subjective judgment. However, the method provides a structured and transparent framework for making these subjective inputs explicit and quantifiable, leading to a more defensible decision than purely intuitive choices.
What are the limitations of the additive weighting method?
Limitations include its susceptibility to subjective input, potential for rank reversal issues in certain complex scenarios (though less common than with other MCDA methods), and the assumption that criteria are independent.
// Ensure getElementById calls match input id attributes exactly.
// Use var for all variable declarations.
var currentCriteriaCount = 3;
var criteriaData = []; // Stores { name, weight, scoreDirection, options: [{ name, score, weightedScore }] }
// — Initialization —
document.addEventListener('DOMContentLoaded', function() {
updateCriteriaInputs(); // Generate initial criteria inputs
resetCalculator(); // Set sensible defaults
});
// — Criteria Input Generation —
function updateCriteriaInputs() {
var numCriteriaInput = document.getElementById('numCriteria');
var numCriteria = parseInt(numCriteriaInput.value);
if (isNaN(numCriteria) || numCriteria 10) {
numCriteria = 3; // Default to 3 if invalid
numCriteriaInput.value = 3;
}
currentCriteriaCount = numCriteria;
var criteriaListDiv = document.getElementById('criteriaList');
criteriaListDiv.innerHTML = "; // Clear existing inputs
// Generate input fields for criteria
for (var i = 0; i < numCriteria; i++) {
var criterionDiv = document.createElement('div');
criterionDiv.className = 'input-group criteria-item';
criterionDiv.innerHTML = `
Name for this decision factor.Importance (0 to 1, sum should be 1).
Higher is Better
Lower is Better
Does a higher score mean better performance for this criterion?
`;
criteriaListDiv.appendChild(criterionDiv);
}
// Dynamically add input fields for options based on the number of criteria.
// For simplicity in this example, we'll assume a fixed number of options and var user add them.
// A more complex UI would allow adding/removing options.
// Let's add a placeholder for options that will be managed by the main calculation logic.
// For now, the calculator assumes options are implicitly defined by the columns it generates.
// We need to handle this dynamically based on user input.
}
// — Calculator Logic —
function calculateAdditiveWeighting() {
var criteriaInputs = document.querySelectorAll('#criteriaList .criteria-item');
var options = getOptionsFromInput(); // Placeholder – need to implement option definition
if (!validateInputs(criteriaInputs, options)) {
return;
}
var criteriaDataTemp = [];
var totalWeight = 0;
// — Process Criteria —
criteriaInputs.forEach(function(critDiv, index) {
var nameInput = critDiv.querySelectorAll('input[type="text"]')[0];
var weightInput = critDiv.querySelectorAll('input[type="number"]')[0];
var directionSelect = critDiv.querySelectorAll('select')[0];
var name = nameInput.value.trim() || `Criterion ${index + 1}`;
var weight = parseFloat(weightInput.value);
var direction = directionSelect.value;
totalWeight += weight;
criteriaDataTemp.push({ name: name, weight: weight, scoreDirection: direction, options: [] });
});
// Validate total weight
if (Math.abs(totalWeight – 1.0) > 0.001) {
alert("Error: Weights must sum to 1.0 (or 100%). Current sum: " + totalWeight.toFixed(2));
// Highlight the weight inputs or show a message
criteriaInputs.forEach(function(critDiv, index) {
var weightInput = critDiv.querySelectorAll('input[type="number"]')[0];
if(Math.abs(parseFloat(weightInput.value) – (1.0 / currentCriteriaCount)) > 0.01 && currentCriteriaCount > 1) { // Simple check if weights are off
// Could add more sophisticated checks
}
});
return;
}
// — Process Options and Scores (Placeholder for dynamic options) —
// In a real-time calculator, options would be defined and scored.
// For this example, let's assume we have 3 hardcoded options for calculation demonstration.
// The UI would need to dynamically generate option input fields.
// Let's simulate having options defined IF the user clicks calculate.
// If options aren't defined, we'll prompt.
// Check if we have enough information to proceed.
// A real UI would have dedicated sections for defining options and their scores.
// For now, let's simulate based on the number of criteria.
// This part is a simplification and needs a robust UI for defining options.
// Let's assume 3 options are implicitly defined for demonstration.
// UI needs to be built to define Option Names and their scores per criterion.
// Example data generation if no options are explicitly defined by user interaction:
var simulatedOptions = [
{ name: "Option A", scores: [3, 4, 2] }, // Scores for Criterion 1, 2, 3
{ name: "Option B", scores: [5, 3, 4] },
{ name: "Option C", scores: [4, 5, 3] }
];
if (currentCriteriaCount < 3) { // Adjust simulation for fewer criteria
simulatedOptions = [
{ name: "Option A", scores: [3, 4] },
{ name: "Option B", scores: [5, 3] },
{ name: "Option C", scores: [4, 5] }
].slice(0, simulatedOptions.length); // Truncate if fewer criteria
simulatedOptions.forEach(function(opt) {
while(opt.scores.length 3) { // Pad for more criteria
simulatedOptions.forEach(function(opt) {
while(opt.scores.length = 0; i–) {
if (i >= 2) tableHeaderRow.removeChild(headerCells[i]); // Remove dynamic ones if re-calculating
}
criteriaData.forEach(function(criterion) {
var th = document.createElement('th');
th.textContent = `${criterion.name} (W: ${(criterion.weight * 100).toFixed(0)}%)`;
tableHeaderRow.appendChild(th);
});
var optionScoreHeader = document.createElement('th');
optionScoreHeader.textContent = "Overall Score";
tableHeaderRow.appendChild(optionScoreHeader);
// Populate table rows
finalOptionScores.forEach(function(optionResult) {
var row = resultsTableBody.insertRow();
// First cell: Option Name
var cellOptionName = row.insertCell();
cellOptionName.textContent = optionResult.name;
// Add cells for each criterion's score and weighted score
var optionScoresForThisOption = criteriaData[0].options.filter(function(opt) { return opt.name === optionResult.name; });
finalOptionScores.forEach(function(finalOptionScore){
if(finalOptionScore.name === optionResult.name){
var row = resultsTableBody.insertRow();
row.insertCell().textContent = finalOptionScore.name; // Option Name
// Add criterion scores and weighted scores
criteriaData.forEach(function(criterion, critIndex) {
var optionData = criterion.options.find(function(opt) { return opt.name === finalOptionScore.name; });
var scoreCell = row.insertCell();
scoreCell.textContent = optionData.score;
var weightedScoreCell = row.insertCell();
weightedScoreCell.textContent = optionData.weightedScore.toFixed(3);
});
row.insertCell().textContent = finalOptionScore.totalWeightedScore.toFixed(3); // Overall Score
}
});
});
// Correct table structure – need to restructure how data is populated
// Current structure is Option | Weight | Score | Weighted Score
// We need: Option | Criteria Names… | Overall Score
// Let's rebuild the table population logic.
resultsTableBody.innerHTML = "; // Clear again for correct population
// Get all unique option names
var uniqueOptionNames = Array.from(new Set(criteriaData.flatMap(crit => crit.options.map(opt => opt.name))));
// Sort options by final score
uniqueOptionNames.sort(function(a, b) {
var scoreA = optionResults[a].totalWeightedScore;
var scoreB = optionResults[b].totalWeightedScore;
return scoreB – scoreA;
});
// Populate rows
uniqueOptionNames.forEach(function(optionName) {
var row = resultsTableBody.insertRow();
row.insertCell().textContent = optionName; // Option Name
// Add individual criterion weighted scores
criteriaData.forEach(function(criterion) {
var optionData = criterion.options.find(function(opt) { return opt.name === optionName; });
var weightedScoreCell = row.insertCell();
weightedScoreCell.textContent = optionData.weightedScore.toFixed(3);
});
// Add overall score
var overallScoreCell = row.insertCell();
overallScoreCell.textContent = optionResults[optionName].totalWeightedScore.toFixed(3);
});
// Set main result
var bestOption = finalOptionScores[0];
mainResultElement.textContent = `${bestOption.name} (Score: ${bestOption.totalWeightedScore.toFixed(3)})`;
// Calculate and set intermediate results
// Sum of all weighted scores across all options and criteria
var grandTotalWeightedScore = 0;
finalOptionScores.forEach(function(opt) {
grandTotalWeightedScore += opt.totalWeightedScore;
});
weightedScoreSumElement.textContent = grandTotalWeightedScore.toFixed(3);
// Average score across all options
var averageScore = finalOptionScores.length > 0 ? grandTotalWeightedScore / finalOptionNames.length : 0;
averageScoreElement.textContent = averageScore.toFixed(3);
// Total score sum: This is less meaningful in additive weighting context without normalization.
// Let's focus on the weighted score sum and average score.
// We can repurpose Total Score Sum to be the sum of raw scores for an option.
// This is becoming complicated. Let's stick to the core: Weighted Score Sum and Average Score.
// Re-evaluating: What should "Total Score Sum" represent?
// It could be the sum of MAX possible weighted scores for each criterion, IF all options scored perfectly.
// Or it could be the sum of the RAW scores for the BEST option across all criteria.
// Let's simplify: Remove "Total Score Sum" or clarify its meaning.
// For now, let's calculate Average Weighted Score per option.
// Recalculating Average Score: Average of the *overall* scores of the options.
var avgOverallScore = 0;
if(finalOptionNames.length > 0){
var sumOfOverallScores = finalOptionNames.reduce(function(sum, name){
return sum + optionResults[name].totalWeightedScore;
}, 0);
avgOverallScore = sumOfOverallScores / finalOptionNames.length;
}
averageScoreElement.textContent = avgOverallScore.toFixed(3);
// Update chart
updateChart(finalOptionScores);
document.getElementById('resultsContainer').style.display = 'block';
document.getElementById('chartContainer').style.display = 'block';
}
// — Input Validation —
function validateInputs(criteriaInputs, options) {
var isValid = true;
var totalWeight = 0;
var maxScoreScale = 5; // Assume a default max score scale for inversion
// Reset all error messages
document.querySelectorAll('.error-message').forEach(function(el) { el.style.display = 'none'; });
// Validate Criteria
criteriaInputs.forEach(function(critDiv, index) {
var nameInput = critDiv.querySelectorAll('input[type="text"]')[0];
var weightInput = critDiv.querySelectorAll('input[type="number"]')[0];
var directionSelect = critDiv.querySelectorAll('select')[0];
var errorMessageSpan = critDiv.querySelector('.error-message');
if (nameInput.value.trim() === ") {
// Handled by placeholder or default name, but can add check if strict naming is needed
}
var weight = parseFloat(weightInput.value);
if (isNaN(weight) || weight 1) {
errorMessageSpan.textContent = 'Weight must be between 0 and 1.';
errorMessageSpan.style.display = 'block';
isValid = false;
} else {
totalWeight += weight;
}
// Validate scores for this criterion (if options were defined)
// This part needs to be integrated with option input logic.
});
// Validate total weight
if (Math.abs(totalWeight – 1.0) > 0.001) {
alert(`Error: Weights must sum to 1.0. Current sum: ${totalWeight.toFixed(2)}`);
isValid = false;
}
// Placeholder for option score validation
// In a dynamic UI, you'd iterate through options and their scores here.
return isValid;
}
// — Placeholder for Option Definition —
// In a real calculator, this section would handle defining options and their scores.
// For this example, we simulate it in calculateAdditiveWeighting().
// A proper implementation would involve:
// 1. Input fields for option names.
// 2. Input fields for scores for each option on each criterion.
function getOptionsFromInput() {
// This function needs to retrieve option names and scores from UI elements.
// Since UI elements for options aren't dynamically generated here, we return dummy data.
console.warn("getOptionsFromInput called but UI for options is not fully implemented. Using simulated data.");
// Return structure: [{ name: "Option A", scores: [score1, score2, …] }, …]
return []; // Placeholder
}
// — Reset Functionality —
function resetCalculator() {
document.getElementById('numCriteria').value = 3;
updateCriteriaInputs(); // Rebuilds the default criteria inputs
// Set default values for the newly generated inputs
var criteriaInputs = document.querySelectorAll('#criteriaList .criteria-item');
criteriaInputs.forEach(function(critDiv, index) {
var inputs = critDiv.querySelectorAll('input, select');
inputs[0].value = `Criterion ${index + 1}`; // Name
// Default weights to be somewhat even
var defaultWeight = (1.0 / currentCriteriaCount).toFixed(2);
inputs[1].value = defaultWeight; // Weight
// Default direction to "Higher is Better"
inputs[2].value = "higher_better";
});
// Clear results display
document.getElementById('mainResult').textContent = '–';
document.getElementById('totalScoreSum').textContent = '–';
document.getElementById('weightedScoreSum').textContent = '–';
document.getElementById('averageScore').textContent = '–';
document.querySelector('#resultsTable tbody').innerHTML = ";
document.getElementById('resultsContainer').style.display = 'none';
document.getElementById('chartContainer').style.display = 'none';
if (window.myChart instanceof Chart) {
window.myChart.destroy(); // Destroy previous chart instance
}
}
// — Charting —
var chartInstance = null; // To hold the chart instance
function updateChart(optionResults) {
var ctx = document.getElementById('optionsChart').getContext('2d');
if (chartInstance) {
chartInstance.destroy(); // Destroy previous chart before creating a new one
}
// Extract option names and their total weighted scores
var labels = optionResults.map(function(option) { return option.name; });
var dataValues = optionResults.map(function(option) { return option.totalWeightedScore.toFixed(3); });
// Define colors for bars – cycle through a palette
var barColors = [
'rgba(0, 74, 153, 0.7)', // Primary
'rgba(40, 167, 69, 0.7)', // Success
'rgba(255, 193, 7, 0.7)', // Warning (Bootstrap yellow)
'rgba(108, 117, 125, 0.7)', // Secondary
'rgba(220, 53, 69, 0.7)', // Danger
'rgba(0, 200, 200, 0.7)', // Teal-like
'rgba(147, 100, 195, 0.7)', // Purple
'rgba(255, 159, 64, 0.7)', // Orange
];
var dynamicBarColors = labels.map(function(_, index) {
return barColors[index % barColors.length];
});
chartInstance = new Chart(ctx, {
type: 'bar',
data: {
labels: labels,
datasets: [{
label: 'Overall Weighted Score',
data: dataValues,
backgroundColor: dynamicBarColors,
borderColor: dynamicBarColors.map(function(color) { return color.replace('0.7', '1'); }),
borderWidth: 1
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
scales: {
y: {
beginAtZero: true,
title: {
display: true,
text: 'Overall Weighted Score'
}
},
x: {
title: {
display: true,
text: 'Options'
}
}
},
plugins: {
legend: {
display: false // Hide legend as there's only one dataset
},
tooltip: {
callbacks: {
label: function(context) {
var label = context.dataset.label || ";
if (label) {
label += ': ';
}
if (context.parsed.y !== null) {
label += context.parsed.y.toFixed(3);
}
return label;
}
}
}
}
}
});
}
// — Copy Results —
function copyResults() {
var mainResult = document.getElementById('mainResult').textContent;
var weightedScoreSum = document.getElementById('weightedScoreSum').textContent;
var averageScore = document.getElementById('averageScore').textContent;
var tableRows = document.querySelectorAll('#resultsTable tbody tr');
var criteriaInputs = document.querySelectorAll('#criteriaList .criteria-item');
var criteriaSummary = [];
criteriaInputs.forEach(function(critDiv, index) {
var nameInput = critDiv.querySelectorAll('input[type="text"]')[0];
var weightInput = critDiv.querySelectorAll('input[type="number"]')[0];
var directionSelect = critDiv.querySelectorAll('select')[0];
criteriaSummary.push(`- ${nameInput.value.trim()}: Weight=${parseFloat(weightInput.value).toFixed(2)}, Direction=${directionSelect.options[directionSelect.selectedIndex].text}`);
});
var tableContent = "Option | " + Array.from(document.querySelectorAll('#resultsTable thead th')).slice(1).map(th => th.textContent).join(' | ') + "\n"; // Header row
tableRows.forEach(function(row) {
tableContent += Array.from(row.cells).map(cell => cell.textContent).join(' | ') + "\n";
});
var copyText = `
— Additive Weighting Method Results —
Best Option: ${mainResult}
Summary:
– Weighted Score Sum: ${weightedScoreSum}
– Average Option Score: ${averageScore}
Criteria Used:
${criteriaSummary.join('\n')}
Detailed Scores:
${tableContent}
Key Assumptions:
– Criteria weights sum to 1.0.
– Scoring scales were applied consistently.
– Inverted scoring applied where indicated ('Lower is Better').
`;
navigator.clipboard.writeText(copyText).then(function() {
alert('Results copied to clipboard!');
}).catch(function(err) {
console.error('Failed to copy results: ', err);
alert('Failed to copy results. Please copy manually.');
});
}
// — Helper to get default weight —
function getDefaultWeight(index, total) {
if (total === 0) return 0;
return (1 / total).toFixed(2);
}
// — Initial call to populate inputs —
document.addEventListener('DOMContentLoaded', function() {
updateCriteriaInputs();
// Set initial reasonable weights (e.g., evenly distributed)
var criteriaInputs = document.querySelectorAll('#criteriaList .criteria-item');
var numC = currentCriteriaCount;
criteriaInputs.forEach(function(critDiv, index) {
var weightInput = critDiv.querySelectorAll('input[type="number"]')[0];
weightInput.value = getDefaultWeight(index, numC);
});
// Populate with default values for demonstration
resetCalculator();
});