Extension card · Z-Number
Z-number VIKOR (Shen et al., 2018)
This is the form of VIKOR for situations where every criterion value is given together with how far that value can be trusted. The output is again group utility, individual regret, and a compromise index combining the two.
Base method
VIKOR →
Philosophy, mechanics, strengths and weaknesses are on the base method card; this card describes only the difference.
Data type (family)
Z-Number →
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?
Three things change; the decision logic does not.
Cells. In crisp VIKOR every cell is a single number. Here every cell is a Z-number: Z = (A, B). A is a triangular fuzzy number stating the criterion value. B is a separate triangular fuzzy number stating how far that value is trusted. Weights and the compromise coefficient v stay crisp numbers.
Scale equalisation. DecisionMind reduces the Z-number to a single crisp number before linear normalisation. This reduction follows the method of Kang and colleagues (2012). A's membership curve is scaled by a coefficient drawn from B's centroid. The centroid of this scaled curve is then taken. Classical VIKOR's linear normalisation is applied to the crisp value obtained this way: every column's best and worst value is found, and the value scaled between 0 and 1 according to its distance between the two.
This reduction has a verified consequence. In the Kang transformation, B's contribution only scales the height of A's membership curve. It does not shift the centroid's horizontal position. The crisp value obtained is therefore equal only to A's centroid; B's numerical value does not affect the result. This has been verified by running DecisionMind's Z-VIKOR engine: in the illustrative example below, changing B drastically leaves the S, R and Q values exactly the same.
Group utility, individual regret and the compromise index. Nothing changes at these steps. On the crisp numbers obtained, classical VIKOR's S, R, Q calculation and its two compromise conditions (acceptable advantage, acceptable stability) all run exactly as before.
DecisionMind holds the Kang transformation and the centroid defuzzification, followed by classical VIKOR's linear normalisation and its two conditions, fixed in this extension.
How to Read the Output
The output is the S, R, Q triple and a compromise proposal, as in crisp VIKOR, and reads the same way. Not a single winner but a conditional compromise solution, or a compromise set, is given.
The difference is here. The purpose of the Z-number data structure is to carry trust in the source into the decision. But in the current DecisionMind engine, this trust is eliminated at the defuzzification step. The S, R and Q values depend only on the centroid of the criterion value (A).
Thus instead of writing:
"Because a Z-number was used, the compromise proposal also reflects reliability"
the report should read:
"The compromise proposal has been calculated from the centroid of the criterion values; the degree of reliability is not reflected in the result after defuzzification in the current engine"
When to Prefer This over the Base Method
If your data is genuinely Z-number in structure, and a compromise across stakeholders is being sought, this extension is the formally correct choice. But the reliability difference should not be expected to change the compromise proposal; the current engine does not provide this. This limit should be stated before the analysis begins.
The exit point is the same as for crisp VIKOR: if no compromise whatsoever is acceptable on one criterion, this extension too limits regret but does not eliminate it.
Mistakes Specific to This Extension
Assuming reliability (B) will change the compromise proposal. In the current engine, B drops out of the calculation entirely after defuzzification. This is not a software fault; it is a mathematical consequence of the Kang transformation, and it should be stated in the report.
Skipping the two conditions and declaring the alternative with the smallest Q the winner. This mistake also exists in base VIKOR; it applies to the Z-number extension too.
Writing the same B into every cell and using a Z-number. If B carries no discriminating information, the effort of collecting the data is wasted.
The governing principle is this:
Z-number VIKOR exists to record the criterion value and the trust placed in that value separately. But in the current engine reliability makes no contribution to the result after defuzzification; the compromise proposal rests only on the centroid of the value.
Cases
The first case is DecisionMind's validation example. A small table of three alternatives on three criteria has been built synthetically. The second case is an illustrative construction.
1. Illustrative example: Scoring three alternatives on three criteria as Z-numbers (DecisionMind validation example)
Three alternatives are scored as Z-numbers on three criteria. Every cell gives the A (value) triangle; reliability B is the same across all three criteria and all three alternatives: (0.70; 0.80; 0.90). The compromise coefficient is taken as v = 0.50.
| Alternative | C1 (A) | C2 (A) | C3 (cost, A) |
|---|---|---|---|
| A1 | 0.65; 0.70; 0.75 | 0.45; 0.50; 0.55 | 0.55; 0.60; 0.65 |
| A2 | 0.75; 0.80; 0.85 | 0.55; 0.60; 0.65 | 0.35; 0.40; 0.45 |
| A3 | 0.55; 0.60; 0.65 | 0.65; 0.70; 0.75 | 0.45; 0.50; 0.55 |
| Direction | higher is better | higher is better | lower is better |
| Weight | 0.40 | 0.35 | 0.25 |
The method reduces every cell's Z-number to a crisp number by the Kang transformation. It then applies classical VIKOR's linear normalisation, the group-utility (S) and individual-regret (R) calculation, the compromise index (Q), and the two conditions.
| Alternative | S | R | Q |
|---|---|---|---|
| A2 | 0.175 | 0.175 | 0.000 |
| A3 | 0.525 | 0.400 | 0.780 |
| A1 | 0.800 | 0.350 | 0.889 |
The result reads as follows. A2 is best both overall (S) and on the single worst criterion (R); neither the majority nor the minority side can object to it. The acceptable-advantage condition is met, because A2's Q gap to A3 (0.780) exceeds the threshold of 0.50 that applies for three alternatives. The acceptable-stability condition is met too, because A2 ranks first on both S and R. A2 is the single compromise solution.
The decision's hesitation is this. If the weights are changed to C1=0.20, C2=0.55, C3=0.25, A3 comes first (Q=0.040), and A2 stays second (Q=0.107). But the Q gap between these two (0.067) is below the threshold (0.50). The acceptable-advantage condition is not met, and VIKOR proposes A3 and A2 together as a compromise set rather than a single winner.
Reliability B has no effect at all on these results. Even if B is changed, S, R and Q come out exactly the same under both weighting scenarios. This has been verified by running the kernel.
In the report: "With weights C1=0.40, C2=0.35, C3=0.25, A2 is the single compromise solution (Q=0.000); both conditions are met. When the weight of C1 is lowered below that of C2, the acceptable-advantage condition is not met, and the compromise set consists of A3 and A2. The degree of reliability is not reflected in this result by the current engine."
Source: DecisionMind's Z-VIKOR validation example; a synthetic 3x3 fixed dataset, built following the Z-number framework of Zadeh (2011) and Kang et al. (2012). The S, R, Q values and the compromise scenario have been independently recomputed by this card's author using the same algorithm, and match the manifest's expected values exactly (tolerance 1e-9).
2. Publishing: A publisher's compromise choice among three translated-book proposals
A publisher will choose one of three translated-book proposals for its new season's list. The criteria are expected sales potential, translation-and-rights cost, and a literary-value score. All but cost are "higher is better," cost is "lower is better." Sales potential is entered as a Z-number because it rests on a forecast. A is the estimate given by the editor; B is whether that estimate rests on the author's previous-book sales data or only on the editor's personal opinion.
The method reduces the three proposals' Z-numbers to crisp numbers by the Kang transformation and applies classical VIKOR. Suppose the result gives, as the single compromise solution, the proposal with the highest sales-potential forecast, where that forecast rests only on the editor's personal opinion.
The publisher's hesitation is this. If this forecast has low reliability, it should be known that the current engine does not reflect this gap in the compromise proposal at all. The publisher should discuss this reliability gap separately at the editorial-board meeting.
In the report: "With the weights given, the proposal with the highest sales-potential forecast is the single compromise solution. This forecast rests only on the editor's personal opinion, and the current calculation does not reflect this gap; it should be considered separately at the editorial board."
3. What Not to Do
In the illustrative example, sharply lowering A2's reliability on the C1 criterion and expecting "the compromise solution should now change" is wrong: S, R and Q do not change at all, because the current engine does not read B. The second error is writing only "A3 is first" under the weight scenario C1=0.20/C2=0.55 and concealing the compromise set; when the acceptable-advantage condition is not met, A2 is also part of the proposal. The third error is reading a Q of 0.000 as "a flawless alternative"; Q only positions these three alternatives relative to one another.
Sources
For the formulas behind each step, the intermediate tables and citation formats, see the DecisionMind method page: decisionmind.app/library/z-vikor
Shen, K.-w., Wang, J.-q., & Wang, T.-l. (2018). Z-VIKOR Method Based on a New Comprehensive Weighted Distance Measure of Z-Number and Its Application. IEEE Transactions on Fuzzy Systems, 26(6), 3232–3245. DOI: 10.1109/TFUZZ.2018.2816581
Zadeh, L. A. (2011). A note on Z-numbers. Information Sciences, 181(14), 2923–2932. DOI: 10.1016/j.ins.2011.02.022
Kang, B., Wei, D., Li, Y., & Deng, Y. (2012). A method of converting Z-number to classical fuzzy number. Journal of Information & Computational Science, 9(3), 703–709. (no DOI)
Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. DOI: 10.1016/S0377-2217(03)00020-1
Opricovic, S., & Tzeng, G.-H. (2007). Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research, 178(2), 514–529. DOI: 10.1016/j.ejor.2006.01.020