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Table 9 Results summery of sensitivity analysis w.r.t number of jobs at \(\alpha = 0.1\)

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

No. of jobs

Case

W

Membership values \(\left( \mu _{Z_{1j}}, \mu _{Z_{2j}}, \mu _{Z_{3j}} \right)\)

Objective values \(\left( Z_{1}, Z_{2}, Z_{3} \right)\)

Optimum allocations

\(x_{1j}\)

\(x_{2j}\)

\(x_{3j}\)

\(x_{4j}\)

\(x_{5j}\)

\(x_{6j}\)

Jobs-9

1

0.8235

(0.9574, 0.9567, 0.9287)

(0.8549, 0.8367, 0.8235)

(0.9184, 0.9124, 0.9152)

(49.8, 66, 90.3)

(47.9, 65, 85.7)

(14.4, 27, 42.3)

\({x_{11}}\)

\({x_{13}}\)

\({x_{14}}\)

 

\(x_{37}\)

\({x_{48}}\)

\({x_{49}}\)

\({x_{52}}\)

\({x_{55}}\)

\({x_{56}}\)

 

2

0.8716

(0.9148, 0.9170, 0.8824)

(0.8899, 0.8757, 0.8716)

(0.9270, 0.9124, 0.9066)

(55.9, 73, 96.4)

(43.8, 60, 78.9)

(13.5, 27, 43.2)

\({x_{11}}\)

\({x_{14}}\)

\({x_{19}}\)

 

\(x_{38}\)

 

\({x_{55}}\)

\({x_{56}}\)

\({x_{62}}\)

\({x_{63}}\)

\({x_{67}}\)

3

0.8436

(0.9626, 0.9644, 0.9431)

(0.8713, 0.8447, 0.8436)

(0.8882, 0.8936, 0.8957)

(48.7, 64, 87.4)

(46, 64, 82.9)

(17.3, 29, 44.3)

\({x_{11}}\)

\({x_{13}}\)

\({x_{14}}\)

 

\(x_{36}\)

\({x_{48}}\)

\({x_{49}}\)

\({x_{52}}\)

\({x_{55}}\)

\(x_{67}\)

4

0.8036

(0.8671, 0.8829, 0.8684)

(0.8233, 0.8243, 0.8036)

(0.9515, 0.9442, 0.9423)

(40.4, 53, 71)

(60.8, 77, 100.4)

(24.6, 39, 54.3)

\({x_{11}}\)

\({x_{14}}\)

\({x_{17}}\)

\(x_{23}\)

\({x_{32}}\)

\({x_{35}}\)

\({x_{38}}\)

\({x_{46}}\)

\({x_{49}}\)

  

5

0.7452

(0.7856, 0.7832, 0.7452)

(0.9174, 0.8998, 0.8813)

(0.9740, 0.9709, 0.9709)

(45.7, 61, 81.7)

(47, 65, 87.5)

(19.5, 33, 48.3)

\({x_{11}}\)

\({x_{13}}\)

 

\(x_{38}\)

\({x_{44}}\)

\({x_{46}}\)

\({x_{49}}\)

\({x_{52}}\)

\({x_{55}}\)

\(x_{67}\)

6

0.8434

(0.8763, 0.8807, 0.8434)

(0.9665, 0.9623, 0.9527)

(0.8902, 0.8552, 0.8466)

(44.6, 59, 80.6)

(59, 77, 101.3)

(12.6, 27, 43.2)

\(x_{13}\)

\({x_{21}}\)

\({x_{24}}\)

\(x_{37}\)

\({x_{46}}\)

\({x_{48}}\)

\({x_{49}}\)

\(x_{55}\)

\(x_{62}\)

Jobs-11

1

0.8528

(0.9490, 0.9456, 0.9283)

(0.8735, 0.8685, 0.8528)

(0.9241, 0.9203, 0.9201)

(63.3, 84, 111)

(57.2, 77, 102.2)

(17.7, 33, 51.9)

\({x_{11}}\)

\({x_{13}}\)

\({x_{14}}\)

 

\(x_{38}\)

\({x_{46}}\)

\({x_{411}}\)

\({x_{52}}\)

\({x_{55}}\)

\({x_{59}}\)

\({x_{67}}\)

\({x_{610}}\)

2

0.8516

(0.9503, 0.9567, 0.9406)

(0.8655, 0.8552, 0.8516)

(0.9241, 0.9203, 0.9201)

(63, 81, 108)

(58.3, 79, 102.4)

(17.7, 33, 51.9)

\({x_{13}}\)

\({x_{14}}\)

\({x_{111}}\)

\(x_{210}\)

\(x_{31}\)

\({x_{46}}\)

\({x_{48}}\)

\({x_{49}}\)

\({x_{52}}\)

\({x_{55}}\)

\(x_{67}\)

3

0.8706

(0.9296, 0.9456, 0.9359)

(0.8885, 0.8881, 0.8706)

(0.8995, 0.8908, 0.8883)

(66.9, 84, 109.2)

(55.1, 74, 99.2)

(20.8, 37, 55.9)

\({x_{13}}\)

\({x_{14}}\)

\({x_{111}}\)

\(x_{210}\)

\(x_{31}\)

\({x_{46}}\)

\({x_{47}}\)

\({x_{55}}\)

\({x_{58}}\)

\({x_{62}}\)

\({x_{69}}\)

4

0.8304

(0.8498, 0.8519, 0.8304)

(0.8409, 0.8402, 0.8461)

(0.9651, 0.9609, 0.9587)

(50.9, 68, 90.5)

(72.3, 93, 116.4)

(27.9, 45, 63.9)

\({x_{13}}\)

\({x_{111}}\)

\(x_{21}\)

\({x_{36}}\)

\({x_{37}}\)

\({x_{38}}\)

\({x_{44}}\)

\({x_{45}}\)

\({x_{49}}\)

 

\({x_{62}}\)

\({x_{610}}\)

5

0.8105

(0.9074, 0.9081, 0.8874)

(0.8210, 0.8221, 0.8105)

(0.9438, 0.9354, 0.9336)

(45.8, 62, 83.6)

(75.3, 96, 123)

(33, 51, 69)

\({x_{13}}\)

\({x_{111}}\)

\(x_{24}\)

\(x_{35}\)

\({x_{46}}\)

\({x_{49}}\)

\({x_{51}}\)

\({x_{52}}\)

\({x_{58}}\)

\({x_{67}}\)

\({x_{610}}\)

6

0.8093

(0.9096, 0.9167, 0.9066)

(0.9590, 0.9615, 0.9575)

(0.8577, 0.8236, 0.8093)

(50.8, 67, 88.6)

(76.2, 96, 123)

(19, 37, 56.8)

\({x_{14}}\)

\({x_{111}}\)

\({x_{21}}\)

\({x_{23}}\)

\({x_{35}}\)

\({x_{37}}\)

\({x_{46}}\)

\({x_{48}}\)

\({x_{49}}\)

 

\({x_{62}}\)

\({x_{610}}\)

Jobs-13

1

0.8118

(0.9098, 0.9265, 0.9127)

(0.8518, 0.8248, 0.8118)

(0.9182, 0.9110, 0.9014)

(83.4, 105, 137.4)

(71, 98, 128.6)

(24.1, 43, 66.4)

\({x_{13}}\)

\({x_{113}}\)

\({x_{210}}\)

\({x_{211}}\)

\({x_{31}}\)

\({x_{36}}\)

\({x_{44}}\)

\({x_{48}}\)

\({x_{49}}\)

\({x_{55}}\)

\({x_{512}}\)

\({x_{62}}\)

\({x_{67}}\)

2

0.8636

(0.8734, 0.8705, 0.8837)

(0.9086, 0.9056, 0.9031)

(0.8824, 0.9713, 0.8636)

(87.9, 114, 142.8)

(61.4, 83, 110)

(29.2, 49, 71.5)

\({x_{13}}\)

\({x_{14}}\)

\({x_{113}}\)

\({x_{21}}\)

\({x_{26}}\)

\({x_{29}}\)

\({x_{38}}\)

\({x_{312}}\)

\({x_{45}}\)

\({x_{47}}\)

\({x_{411}}\)

\(x_{52}\)

\(x_{610}\)

3

0.8170

(0.9598, 0.9672, 0.9550)

(0.8469, 0.8305, 0.8170)

(0.9109, 0.8985, 0.8872)

(73.2, 93, 125.4)

(71.8, 97, 127.6)

(25.2, 45, 68.4)

\({x_{11}}\)

\({x_{14}}\)

\({x_{111}}\)

\({x_{23}}\)

\({x_{210}}\)

\({x_{36}}\)

\({x_{37}}\)

\({x_{48}}\)

\({x_{412}}\)

\({x_{55}}\)

\({x_{513}}\)

\({x_{62}}\)

\({x_{69}}\)

4

0.8260

(0.8260, 0.8322, 0.8364)

(0.8727, 0.8786, 0.8586)

(0.9708, 0.9609, 0.9542)

(52.9, 70, 89.8)

(67.1, 86, 113.9)

(26.1, 45, 64.8)

\({x_{13}}\)

\({x_{111}}\)

\(x_{21}\)

\({x_{36}}\)

\({x_{37}}\)

\({x_{38}}\)

\({x_{44}}\)

\({x_{45}}\)

\({x_{49}}\)

 

\({x_{62}}\)

\({x_{610}}\)

5

0.8296

(0.8380, 0.8421, 0.8296)

(0.8778, 0.8786, 0.8630)

(0.9830, 0.9773, 0.9740)

(51.9, 69, 90.6)

(66.2, 86, 113)

(21, 39, 58.8)

\({x_{13}}\)

\({x_{111}}\)

\(x_{21}\)

\(x_{38}\)

\({x_{46}}\)

\({x_{47}}\)

\({x_{49}}\)

\(x_{55}\)

\({x_{62}}\)

\({x_{67}}\)

\({x_{610}}\)

6

0.8186

(0.8438, 0.8540, 0.8352)

(0.9749, 0.9726, 0.9684)

(08619, 0.8347, 0.8186)

(74.3, 95, 125.6)

(82.8, 108, 139.5)

(30.3, 51, 73.5)

\({x_{15}}\)

\({x_{111}}\)

\({x_{113}}\)

\({x_{23}}\)

\({x_{24}}\)

\(x_{37}\)

\({x_{46}}\)

\({x_{48}}\)

\({x_{51}}\)

\({x_{512}}\)

\({x_{62}}\)

\({x_{69}}\)

\({x_{610}}\)