public function run()
{
//Step 1: define the semantics
//$this->debug = true;
//Step 2: transform assessments into the HFLTS
parent::run();
if ($this->debug) {
system_message($this->N . " x " . $this->M . " x " . $this->P);
}
//Step 3: establish the alternatives, criteria and the weights of the criteria
self::crossAlternativesWithCriteria();
//Step 4: find out the positive ideal and the negative ideal solution
self::idealSolution();
//Step 5: derive the compromise solutions
self::linguisticCompromise();
$this->ranking();
return $this->ranking[0]['vikor']['label'];
}
public function run()
{
//step 1: transform the linguistic expressions into HFLTS.
//Since all criteria are of the maximizing type no normalization is needed
parent::run();
//$this->debug = true;
if ($this->debug) {
system_message($this->N . " x " . $this->M . " x " . $this->P);
}
//step 2: identify the concordance an discordance indices
$this->crossAlternativesWithCriteria();
//step 3: construct the concordance and discordance matrices
$this->concordanceBalance();
//step 4: get the net dominance and disadvantage indices (c_i and d_i)
$this->dominanceBalance();
//step 5: rank the alternatives in acoordance with c_i and d_i
$this->ranking();
return $this->ranking[0]['electre']['label'];
}
public function run()
{
parent::run();
//$this->debug = true;
if ($this->debug) {
system_message($this->N . " x " . $this->M . " x " . $this->P);
}
//Assuption: G is a normalized linguistic decision matrix, where criteria benefit is same and cost criteria es negated
$this->variance = $this->variance();
//step 1 find the most important factor and calculate the relative weights
$this->relativeWeights();
//step 2 calculate the dominance degree for each alternative concerning a criterion
//step 3 calculate the dominance degree for each alternative
$this->crossAlternativesWithCriteria();
//step 4 calculate the overall dominance degree for each alternative
$this->overallDominance();
//step 5 rank alternatives
$this->ranking();
return $this->ranking[0]['todim']['label'];
}
public function run()
{
parent::run();
$this->debug = true;
if ($this->debug) {
system_message($this->N . " x " . $this->M . " x " . $this->P);
echo '<br>superE <pre>';
print_r($this->superE);
echo '</pre>';
echo '<br>E <pre>';
print_r($this->E);
echo '</pre>';
echo '<br>W <pre>';
print_r($this->W);
echo '</pre>';
}
//Get the scenario .-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-
if (elgg_get_plugin_setting('weight_experts', 'hflts') == 1) {
if (elgg_get_plugin_setting('weight_assessments', 'hflts') == 1) {
system_message("Scenario 3: double subjectivity (E, superE) => 2-tuple extendend weighted mean in two phases");
$this->crossAlternativesWithCriteria();
} else {
system_message("Scenario 2: DM with expertise (E) ignore (superE, W=1) => HFWA operator + extended mean");
$this->crossWithHFWAoperator();
$this->aggregation2();
}
} else {
if (elgg_get_plugin_setting('weight_assessments', 'hflts') == 0) {
system_message("Scenario 1: ignore (E, superE) W=1 => minmax operator + extended mean");
} else {
system_message("Scenario 4: ignore (E, superE) aggregate W => minmax + extended mean");
}
$this->translation();
$this->aggregation2();
}
//..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-..-
$this->average();
$this->ranking();
return $this->ranking[0]['average']['label'];
}
public function run()
{
//step 1 collect data into a fuzzy decision matrix
parent::run();
//$this->debug = true;
if ($this->debug) {
system_message($this->N . " x " . $this->M . " x " . $this->P);
}
//Assuption: G is a normalized linguistic decision matrix, where criteria benefit is same and cost criteria es negated
//parent::topsisCase();//realEstateCase();vikorCase
//step 2 aggretate the opinion of decision makers into X (one decision matrix) by using max-min operators
$this->crossAlternativesWithCriteria();
//step 3 Let a collection of benefit criteria (i.e., the larger C_j, the greater preference)
//Let be a collection of cost criteria (i.e., the smaller C_j, the greater preference)
// compute the HFLTS positive-ideal solution (HFLTS-PIS) & negative-ideal solution (HFLTS-NIS)
$this->idealSolutions();
//Step 4. Construct positive ideal separation matrix (D+) and negative ideal separation matrix (D−)
//Step 5. Calculate the relative closeness (RC) of each alternative to the ideal solution
$this->separationMatrix();
//Step 6. Rank all the alternatives A i (i = 1, . , m) according to the closeness coefficient RC(A i ), the greater the value RC(A i ), the better the alternative A i.
$this->ranking();
return $this->ranking[0]['topsis']['label'];
}
public function run()
{
parent::run();
//method not implemented
return $this->ranking[0]['promethee']['label'];
}
