Rough back to the data type card12 methods
Rough
Methods that work with Rough data
Every method that works with this data type has a page of its own. Those with an academy card are explained here through their philosophy, how to read their output and worked cases; the rest open on their formula page in the library.
13 academy cards · 0 only in the library · 12 methods in the catalogue
Methods with an academy card
13 cards- RankingRough-DRSARough-DRSA does not rank alternatives; it sorts them into "certainly good," "certainly bad" and "uncertain" classes using dominance rules learned from past examples, and it shows contradictory examples openly rather than hiding them.Open the card →
- TOPSIS extensionsRough TOPSISThis is the form of TOPSIS that works with rough numbers for situations where criterion scores come from several experts' group assessment and the disagreement itself needs to be preserved. It carries the uncertainty as a lower and upper bound all the way to the final step, and still ranks the result with a closeness score.Open the card →
- VIKOR extensionsRough VIKORThis is the form of VIKOR that works with rough numbers for situations where criterion scores come from several experts' group assessment and the disagreement itself needs to be preserved. It carries group utility and individual regret as rough intervals, and still delivers the result as a compromise proposal.Open the card →
- SAW extensionsRough SAWThis is the form of SAW in which every cell is given not as a single number but as a rough number interval, that is, a lower and an upper approximation. It computes the weighted sum over these intervals and reduces the result to a single score with the interval's midpoint.Open the card →
- EDAS extensionsRough EDASThis is the form of EDAS that works with interval-valued rough numbers for situations where criterion scores must carry both an expert's own hesitation and the disagreement between experts at once. It carries the deviation from the average as two nested intervals, and still gives the result as a single appraisal score.Open the card →
- COPRAS extensionsRough COPRASThis is the form of COPRAS that works with rough numbers for situations where criterion scores come from a group assessment by several experts and the disagreement between them needs to be preserved. It carries the benefit and cost sums as rough intervals, and still gives the result as a degree of utility, a percentage relative to the best.Open the card →
- MARCOS extensionsRough MARCOSThis is the form of MARCOS that works with rough numbers for situations where criterion scores come from a group assessment by several experts and the disagreement between them needs to be preserved. It carries the ideal and anti-ideal references as rough intervals, and still gives the result as a single final degree of utility.Open the card →
- CODAS extensionsInterval rough CODASInterval rough CODAS is the form of CODAS used when several experts score the same criterion as an interval and the disagreement between them needs to be preserved. It directly supports a group decision and still ranks the result with a single assessment score.Open the card →
- TODIM extensionsRough TODIMRough TODIM is the form of TODIM for situations where every cell in the decision matrix is given as a lower and upper bound derived from disagreement within a group. It carries out the pairwise gain-loss comparison over these intervals.Open the card →
- WASPAS extensionsRough set WASPASThis is the form of WASPAS in which every cell is given as a rough-number interval rather than a single number. It computes the additive and multiplicative components over these intervals, then combines the two with a λ interval derived from the data itself, reducing them to a single score.Open the card →
- ARAS extensionsRough ARASThis is the form of ARAS that works with rough numbers for situations where criterion scores come from a group assessment by several experts and the disagreement between them needs to be preserved. It carries uncertainty as a lower and upper bound all the way to the final step, and still ranks alternatives by a single degree of utility.Open the card →
- MABAC extensionsRough MABACThis is the form of MABAC for situations where expert scores are not crisp numbers but intuitionistic fuzzy judgements. These judgements are opened out into a lower and an upper approximation from the disagreement within a group of experts. The border-region logic stays exactly the same; the cells change.Open the card →
- MOORA extensionsRough MOORAThis is the form of MOORA for situations where every cell in the decision matrix is given as a lower and upper bound derived from disagreement within a group of experts. It runs the ratio system over these intervals and still comes down to a single net score.Open the card →
Other methods in the library
These methods do not yet have an academy card. Their formulae, steps and source citation live in the library; each link opens the method page directly.
No method without a card remains for this type.