Table 2.

Hypothetical multi-criteria problem. Measured data and method-relevant calculations are given to set up transformation and additive aggregation.

	Criterion c1	Criterion c2	Criterion c3	Criterion c4
Alternative a1	0	2.25	75	Excellent
Alternative a2	0.74	0.9	15	Poor
Alternative a3	0.55	2.25	30	Good
Alternative a4	1	3	10	Fair
Global “worst” ( $z_j^{^}$)	0	0	0	None
Global “best” ( $z_j^{^^}$)	1	3	100	Excellent
Local “worst” ( $z_j^{\ast }$; z#)	0	0.9	10	Poor
Local “best” or “ideal” ( $z_j^{\ast \ast }$; z##)	1	3	75	Excellent
$|z_j^{\ast }-Z_j^{\ast \ast }|$	1	2.1	65
$\sum _{i=1}^m{z}_j(a_i)$	2.29	8.4	130

Notes: We assume c₄ categories correspond to numbers (None = 0, Poor = 25, Fair = 50, Good = 75, Excellent = 100); we assume local “worst” and “best” “worst” and “ideal” values, respectively, for compromise programming.