Table 6 Fuzzy Q values for criteria.

From: Artificial intelligence-generated sustainable gift box design evaluation via reinforcement learning-driven hybrid molecular fuzzy modelling

Ex 1 (initial Q matrix)

CRT1

CRT2

CRT3

CRT4

CRT1

(0.00, 0.00, 0.00)

(0.60, 0.30, 0.10)

(0.40, 0.50, 0.10)

(0.80, 0.15, 0.05)

CRT2

(0.80, 0.15, 0.05)

(0.00, 0.00, 0.00)

(0.95, 0.05, 0.00)

(0.60, 0.30, 0.10)

CRT3

(0.95, 0.05, 0.00)

(0.80, 0.15, 0.05)

(0.00, 0.00, 0.00)

(0.80, 0.15, 0.05)

CRT4

(0.60, 0.30, 0.10)

(0.40, 0.50, 0.10)

(0.60, 0.30, 0.10)

(0.00, 0.00, 0.00)

Ex 2 (Balanced Q matrix)

CRT1

CRT2

CRT3

CRT4

CRT1

(0.00, 0.00, 0.00)

(0.67, 0.25, 0.08)

(0.47, 0.43, 0.10)

(0.66, 0.28, 0.07)

CRT2

(0.85, 0.11, 0.03)

(0.00, 0.00, 0.00)

(0.90, 0.09, 0.02)

(0.67, 0.25, 0.08)

CRT3

(0.95, 0.05, 0.00)

(0.85, 0.11, 0.03)

(0.00, 0.00, 0.00)

(0.80, 0.15, 0.05)

CRT4

(0.60, 0.30, 0.10)

(0.47, 0.43, 0.10)

(0.53, 0.37, 0.10)

(0.00, 0.00, 0.00)

Ex 3 (Balanced Q matrix)

CRT1

CRT2

CRT3

CRT4

CRT1

(0.00, 0.00, 0.00)

(0.63, 0.29, 0.09)

(0.48, 0.42, 0.10)

(0.69, 0.24, 0.06)

CRT2

(0.84, 0.12, 0.04)

(0.00, 0.00, 0.00)

(0.86, 0.11, 0.03)

(0.65, 0.26, 0.09)

CRT3

(0.93, 0.06, 0.01)

(0.84, 0.12, 0.04)

(0.00, 0.00, 0.00)

(0.77, 0.17, 0.06)

CRT4

(0.60, 0.30, 0.10)

(0.45, 0.45, 0.10)

(0.52, 0.38, 0.10)

(0.00, 0.00, 0.00)

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