Extension card · Classical
PROMETHEE III (Brans, Vincke & Mareschal, 1986)
This is the form within the PROMETHEE family that presents every alternative's net flow not as a single number but as a confidence interval. Two alternatives are ranked strictly only if their intervals do not overlap; if the intervals overlap, the two are treated as indifferent.
Base method
PROMETHEE →
Philosophy, mechanics, strengths and weaknesses are on the base method card; this card describes only the difference.
Data type (family)
Classical →
What this data type is, when to use it, how to write it in a cell: the family's full account is here.
What Changes from the Base Method?
Two things change: how the net flow is computed, and the ranking itself.
Mean-flow computation. In base PROMETHEE, the incoming and outgoing flow for every alternative is found by averaging the comparisons against the other m-1 rivals, and the net flow is the difference between the two. PROMETHEE III averages the signed differences (π(a,a′) − π(a′,a)) for every alternative over ALL m alternatives (including the comparison with itself, which contributes a term of zero); the divisor is m, not m-1. This small difference affects the result once combined with the standard-deviation computation below.
Building the interval. Base PROMETHEE uses this mean flow directly for ranking. PROMETHEE III additionally computes how spread out these same m terms are (the standard deviation, σ) and builds, for every alternative, an interval of the form [mean flow − α·σ, mean flow + α·σ]. α is a coefficient the decision maker fixes in advance (typically between 0.10 and 0.30 in the literature); in DecisionMind this value is taken from the user, it has no default, and it must be stated explicitly with every run.
Ranking rule. Two alternatives are ranked only if one's interval lower bound is strictly greater than the other's upper bound. If the intervals overlap at any point, the two alternatives are treated as indifferent; even if base PROMETHEE's mean flows differ, an overlapping interval means this difference is accepted as "within the noise." For the table view, DecisionMind also renders this interval relation as a rank ordered by the size of the mean flow, but the underlying real output is the interval relation; even if two neighbouring alternatives appear consecutively in the table, there is no strict dominance between them if their intervals overlap.
DecisionMind takes the choice of preference function, the threshold values and the weights from the user in this extension; these work as in base PROMETHEE. What changes is the interval construction after the fifth step and the decision rule in the sixth step.
How to Read the Output
The order shown in the table is a view built on the midpoint of every alternative's interval; it is read with the same logic as base PROMETHEE's net-flow order, but it is not sufficient on its own. The real result is which pairs' intervals overlap and which do not. An alternative whose interval does not overlap with any other alternative's genuinely stands apart; an alternative whose interval overlaps with its neighbours' is treated as indifferent to them despite its position in the table, and this must be stated separately in the report. As α is increased, the intervals widen and more pairs become indifferent; as α is decreased, more pairs are strictly separated. The choice of α is therefore itself an analytical decision, not merely a computational detail.
Thus instead of writing:
"According to PROMETHEE III, A5 is first, A2 second, A4 third"
the report should read:
"A5's interval is strictly higher than every other alternative's interval, and its first place is robust. A2's and A4's intervals, however, overlap (with α = 0.16); there is no strict dominance between the two, and their appearing second and third in the table comes only from the midpoint comparison"
When to Prefer This over the Base Method
Use this when net flows come out close to one another and the question of whether this closeness is "a genuine difference or computational noise" matters. PROMETHEE III restrains the decision maker from speaking too precisely (the net flow always gives a complete ranking) and shows which differences are meaningful relative to the width of the interval. If a complete ranking is required and overlapping intervals are not acceptable, base PROMETHEE (net flow) should be used; PROMETHEE I's partial relation offers a similar honesty but rests directly on the φ⁺/φ⁻ comparison rather than an interval. Crisp PROMETHEE's exit conditions (the computational burden once the alternative set exceeds fifty) apply here too.
Mistakes Specific to This Extension
Forgetting that the divisor is m, not m-1, and carrying base PROMETHEE's flow formula over here. PROMETHEE III's mean signed flow is divided by m (the comparison with itself is included as a zero term); this differs from base PROMETHEE's φ⁺/φ⁻ computation, and mixing the two makes the numbers fail to reconcile.
Trying several values of α as if it were a sensitivity-scanning parameter and picking the one that "looks best." α is an analytical decision that must be fixed once before the study begins and explained in the report; typical values in the literature lie between 0.10 and 0.30. Searching for an α that steers the result in a desired direction undermines the method's honesty from the outset.
Mistaking the consecutive order in the table for strict dominance. As explained above, the table is built on the midpoint; the method makes no dominance claim between neighbouring alternatives whose intervals overlap.
Mistaking the interval width (σ) for a sign of poor data quality. σ shows how INCONSISTENT that alternative's signed differences against every other alternative are (far ahead of some rivals, far behind others); it has nothing to do with measurement error.
The governing principle is this:
PROMETHEE III's contribution is to show whether a small difference in the net flow is meaningful relative to the width of the interval; a ranking produced without α being chosen and explained in the report does not carry this contribution.
Cases
The first case is a literature case: the car-manufacturing case study from Alinezhad and Khalili's (2019) book chapter. The second case is an illustrative fiction.
1. Manufacturing: Choosing a car-production-line site among six factories (Alinezhad & Khalili, 2019, pp. 33-38)
A production-planning exercise is to choose a site among six candidate factories (A1-A6). Six criteria are equally weighted (1/6 each): C1 and C3 along with C4, C5 are cost/risk-type criteria ("cost"); C2 and C6 are "benefit" criteria. A different type of preference function is used for each criterion (threshold-type, linear, Gaussian, and so on); α = 0.160.
| Factory | C1 | C2 | C3 | C4 | C5 | C6 |
|---|---|---|---|---|---|---|
| A1 | 80 | 90 | 600 | 54 | 8 | 5 |
| A2 | 65 | 58 | 200 | 97 | 1 | 1 |
| A3 | 83 | 60 | 400 | 72 | 4 | 7 |
| A4 | 40 | 80 | 1000 | 75 | 7 | 10 |
| A5 | 52 | 72 | 600 | 20 | 3 | 8 |
| A6 | 94 | 96 | 700 | 36 | 5 | 6 |
| Direction | cost | benefit | cost | cost | cost | benefit |
The method computes the pairwise preferences on every criterion with the relevant preference function, finds the mean flows and the standard deviations, and builds an interval for every factory with α = 0.160.
| Factory | Mean flow (φ̄) | Interval [X, Y] | Rank (by midpoint) |
|---|---|---|---|
| A5 | 0.272 | [0.247 ; 0.297] | 1 |
| A2 | 0.014 | [-0.012 ; 0.040] | 2 |
| A4 | -0.031 | [-0.056 ; -0.006] | 3 |
| A6 | -0.046 | [-0.074 ; -0.018] | 4 |
| A3 | -0.088 | [-0.124 ; -0.053] | 5 |
| A1 | -0.121 | [-0.141 ; -0.102] | 6 |
The result reads as follows. A5's interval ([0.247; 0.297]) is strictly higher than every other factory's interval; A5 leaves everyone behind on an interval basis too, and this first place is robust. But A2's, A4's, A6's and A3's intervals overlap with one another (A2A4, A4A3, A4A6, A3A6, A3~A1): the 2-3-4-5 order in the table is by midpoint, and there is no strict dominance among these four factories at α = 0.160. Only A1, with the lowest interval, is strictly behind A5, A2 and A4; it overlaps with A3 and A6.
The planning team's hesitation: if α is lowered to 0.05 (computed by independently running the same algorithm in Python), the intervals narrow and the number of overlapping pairs falls from 5 to 1, meaning the order among A2-A4-A6-A3 also begins to become strict. If α is raised to 0.30, the number of overlapping pairs rises to 8, and only A5's first place and A1's clear last place remain robust. This shows that the choice of α directly determines how many factories are counted as "strictly different."
In the report: "At α = 0.160, A5 is strictly separated from every factory and its first place is robust. Factories A2, A4, A6 and A3 have overlapping intervals; the order among them comes only from the midpoint comparison, and how strict this order becomes under a narrower α should be tested separately."
Source: Alinezhad, A., & Khalili, J. (2019). PROMETHEE I-II-III Methods (Chapter 5, Case Study §5.3), New Methods and Applications in Multiple Attribute Decision Making (MADM), Springer ISOR Vol. 277, pp. 33-38. The manifest's own note states that the book's narrative text ("all are benefit criteria") conflicts with the table's criterion directions, and that the manifest adopts the direction assignment (the table above) that reproduces the book's Table 5.12 ranking. The mean-flow and interval values were independently reproduced by this card's author by running the DecisionMind kernel, and matched the book's ranking (A5≻A2≻A4≻A6≻A3≻A1) exactly.
2. Textiles: A ready-to-wear brand's choice of fabric supplier
A ready-to-wear brand is to choose among four fabric suppliers (T1-T4). Three criteria: fabric-quality score (a benefit criterion), delivery time (a cost criterion) and unit price (a cost criterion). The weights are 0.4 for quality, 0.35 for delivery time, 0.25 for price; the plain preference function is used on every criterion, with α = 0.16.
| Supplier | Quality score | Delivery time (days) | Unit price |
|---|---|---|---|
| T1 | 8 | 10 | 50 |
| T2 | 6 | 5 | 40 |
| T3 | 7 | 7 | 45 |
| T4 | 5 | 12 | 35 |
| Direction | benefit | cost | cost |
The method compares the four suppliers and builds the mean flows and intervals.
| Supplier | Mean flow | Interval [X, Y] |
|---|---|---|
| T2 | 0.225 | [0.196 ; 0.254] |
| T3 | 0.125 | [0.084 ; 0.166] |
| T1 | 0.025 | [-0.021 ; 0.071] |
| T4 | -0.375 | [-0.410 ; -0.340] |
At α = 0.16, the intervals of all four suppliers do not overlap with one another: T2 strictly leaves T3 behind, T3 leaves T1, and T1 leaves T4. A complete ranking results.
The brand's hesitation: when α is widened slightly to 0.20 (computed by independently running the same algorithm in Python), the intervals of T1 and T3 begin to overlap; the claim that T3 leaves T1 behind is no longer strict. At α = 0.25, the intervals of T2 and T3 also overlap. The brand should show how "noise-resistant" the delivery-time and price difference between T1 and T3 is by stating α explicitly in the report.
In the report: "At α = 0.16, T2 is the best supplier, and the order T2, T3, T1, T4 is fully separated. The difference between T1 and T3 becomes indifferent at α = 0.20; T3's dominance over T1 is therefore strict only under a narrow interval choice, and this choice must be justified in the report."
3. What Not to Do
In the first case, reporting the 2-3-4-5 order of factories A2, A4, A6 and A3 in the table as "a strict order of dominance" is wrong: these four factories' intervals overlap, and the method makes no claim of strict distinction between them. The second error is tuning α by trial and error to change the result and picking whichever value looks "cleanest"; α must be fixed before the analysis begins and explained in the report. The third error is taking the divisor in PROMETHEE III's φ̄ computation as m-1 and confusing it with base PROMETHEE's φ⁺-φ⁻ formula; the book states this explicitly as m in Equation 5.19.
Sources
For the formulas behind each step, the intermediate tables and citation formats, see the DecisionMind method page: decisionmind.app/library/promethee-iii
Alinezhad, A., & Khalili, J. (2019). PROMETHEE I-II-III Methods (Chapter 5). In New Methods and Applications in Multiple Attribute Decision Making (MADM) (International Series in Operations Research & Management Science, Vol. 277, pp. 29–39). Springer. DOI: 10.1007/978-3-030-15009-9_5
Brans, J. P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research, 24(2), 228–238. DOI: 10.1016/0377-2217(86)90044-5
Brans, J. P., & Vincke, Ph. (1985). A preference ranking organisation method (The PROMETHEE method for multiple criteria decision-making). Management Science, 31(6), 647–656. DOI: 10.1287/mnsc.31.6.647
Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 200(1), 198–215. DOI: 10.1016/j.ejor.2009.01.021