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Table 5 Compromised solutions with respect to improvement desired in various objective at confidence level \(\alpha =0.1\) with different shape parameter

From: Genetic algorithm based hybrid approach to solve fuzzy multi-objective assignment problem using exponential membership function

Case

Obj. function

Bounds

\(\lambda\)

Objective values

Solution variables

(\(Z_{1}, Z_{2}, Z_{3}\))

\(x_{ij}\)

Shape parameter: (−5, −1, −2)

Aspiration level: (0.8, 0.85, 0.7)

1

Cost

\(15.8 \le z_{11} \le 32,\)

0.8025

(26.9, 35, 48.5),

\(x_{11}, x_{23}, x_{36}, x_{44}, x_{55}\),\(x_{62}\)

\(23 \le z_{12} \le 41,\)

(31.3, 43, 58.3),

\(32 \le z_{13}\le 56.3\)

(12.1, 22, 32.8)

2

Quality

\(3.9\le z_{31}\le 7,\)

0.8611

(32, 41, 56.3),

\(x_{13}, x_{14}, x_{31}, x_{46}, x_{55}\),\(x_{62}\)

\(12 \le z_{32} \le 16,\)

(33.1, 43, 57.4),

\(22.8 \le z_{33} \le 26.8\)

(3.9, 12, 22)

Aspiration level: (0.9, 0.7, 0.8)

1

Cost

\(15.8 \le z_{11}\le 28.9,\)

0.7104

(22.8, 30, 40.8),

\(x_{11}, x_{23}, x_{35}, x_{44}, x_{46}\),\(x_{62}\)

\(23 \le z_{12} \le 41,\)

(43.4, 56, 72.2),

\(37 \le z_{13} \le 51.4\)

(10.1, 20, 30.8)

Aspiration level: (0.7, 0.8, 0.9)

1

Cost

\(15.8 \le z_{11} \le 32.9\)

0.8143

(25.9, 34, 47.5),

\(x_{11}, x_{13}, x_{44}, x_{46}, x_{55}\),\(x_{62}\)

\(23 \le z_{12}\le 41,\)

(35.3, 47, 62.3),

\(32 \le z_{13} \le 56.3\)

(7, 26, 26.8)

Shape parameter: (−2, −5, −1)

Aspiration level: (0.8, 0.85, 0.7)

1

Time

\(20 \le z_{21} \le 47.3,\)

0.8159

(24.9, 33, 46.5),

\(x_{13}, x_{14}, x_{46}, x_{51}, x_{55}, x_{62}\)

\(29 \le z_{22} \le 59,\)

(33.3, 45, 59.4),

\(40.7 \le z_{23} \le 75.2\)

(11, 20, 30.8)

Shape parameter: (−1, −2, −5)

Aspiration level: (0.8, 0.7, 0.75)

1

Quality

\(3.9 \le z_{31}\le 13.1\),

0.7784

(23.7, 30, 41.7),

\(x_{12}, x_{15}, x_{23}, x_{34}, x_{46}, x_{61}\)

\(12 \le z_{32} \le 22\),

(46.3, 58, 75.1),

\(22.8\le z_{33} \le 32.8\)

(8.1, 18, 28.8)