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Multiobjective fuzzy-GSK algorithm in uncertain project management environments Cover

Multiobjective fuzzy-GSK algorithm in uncertain project management environments

Open Access
|Jun 2025

Figures & Tables

Figure 1:

FIS for linguistic antecedent and consequent variables. FIS, fuzzy inference system.
FIS for linguistic antecedent and consequent variables. FIS, fuzzy inference system.

Figure 2:

Membership function for ME. ME, managerial expertise.
Membership function for ME. ME, managerial expertise.

Figure 3:

Membership function for LE. LE, labor expertise.
Membership function for LE. LE, labor expertise.

Figure 4:

Membership function for WC. WC, weather conditions.
Membership function for WC. WC, weather conditions.

Figure 5:

Membership function for activity time.
Membership function for activity time.

Figure 6:

Membership function for activity cost.
Membership function for activity cost.

Figure 7:

Set of fuzzy rules.
Set of fuzzy rules.

Figure 8:

Vector xij during junior gaining and sharing of knowledge phase.
Vector xij during junior gaining and sharing of knowledge phase.

Figure 9:

Vector xij during senior gaining and sharing of knowledge phase.
Vector xij during senior gaining and sharing of knowledge phase.

Figure 10:

Working methodology of fuzzy-GSK. fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.
Working methodology of fuzzy-GSK. fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.

Figure 11:

ZDT1 using fuzzy-GSK. fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.
ZDT1 using fuzzy-GSK. fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.

Figure 12:

ZDT2 using fuzzy-GSK. fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.
ZDT2 using fuzzy-GSK. fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.

Figure 13:

ZDT3 using fuzzy-GSK. fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.
ZDT3 using fuzzy-GSK. fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.

Figure 14:

ZDT6 using fuzzy-GSK. fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.
ZDT6 using fuzzy-GSK. fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.

Figure 15:

Linear TCT relationship of the project activity. TCT, time–cost trade-off.
Linear TCT relationship of the project activity. TCT, time–cost trade-off.

Figure 16:

Network of seven activity test problem.
Network of seven activity test problem.

Figure 17:

Network of 13 activity test problem.
Network of 13 activity test problem.

Figure 18:

Network of 18 activity test problem.
Network of 18 activity test problem.

Figure 19:

Comparative analysis using fuzzy-GSK, fuzzy-NSGA-II and fuzzy-iMOGA (for seven activities). Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm; Fuzzy-iMOGA, fuzzy-improved multi-objective genetic algorithm; Fuzzy-NSGA-II, fuzzy-non-dominated sorting genetic algorithm-II.
Comparative analysis using fuzzy-GSK, fuzzy-NSGA-II and fuzzy-iMOGA (for seven activities). Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm; Fuzzy-iMOGA, fuzzy-improved multi-objective genetic algorithm; Fuzzy-NSGA-II, fuzzy-non-dominated sorting genetic algorithm-II.

Figure 20:

Comparative analysis using fuzzy-GSK, fuzzy-NSGA-II, and fuzzy-iMOGA (for 13 activities). Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm; Fuzzy-iMOGA, fuzzy-improved multi-objective genetic algorithm; Fuzzy-NSGA-II, fuzzy-non-dominated sorting genetic algorithm-II.
Comparative analysis using fuzzy-GSK, fuzzy-NSGA-II, and fuzzy-iMOGA (for 13 activities). Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm; Fuzzy-iMOGA, fuzzy-improved multi-objective genetic algorithm; Fuzzy-NSGA-II, fuzzy-non-dominated sorting genetic algorithm-II.

Figure 21:

Comparative analysis using fuzzy-GSK, fuzzy-NSGA-II, and fuzzy-iMOGA (for 18 activities). Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm; fuzzy-iMOGA, fuzzy-improved multi-objective genetic algorithm; fuzzy-NSGA-II, fuzzy-non-dominated sorting genetic algorithm-II.
Comparative analysis using fuzzy-GSK, fuzzy-NSGA-II, and fuzzy-iMOGA (for 18 activities). Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm; fuzzy-iMOGA, fuzzy-improved multi-objective genetic algorithm; fuzzy-NSGA-II, fuzzy-non-dominated sorting genetic algorithm-II.

Figure 22:

Trade-off profiles using fuzzy-GSK for seven activities. Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.
Trade-off profiles using fuzzy-GSK for seven activities. Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.

Figure 23:

Trade-off profiles using fuzzy-GSK for 13 activities. Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.
Trade-off profiles using fuzzy-GSK for 13 activities. Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.

Figure 24:

Trade-off profiles using fuzzy-GSK for 18 activities. Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.
Trade-off profiles using fuzzy-GSK for 18 activities. Fuzzy-GSK, fuzzy-gaining sharing knowledge-based algorithm.

Test problem alternatives

ActivitiesCTNTCCNC
114242,4001,500
215253,0001,000
315334,5003,200
4122045,00030,000
5223020,00010,000
6142440,00018,000
7091830,00020,000
81424220120

91525300100
101533450320
111220450300
1222302,0001,000
1314244,0001,800
1409183,0002,200
1516163,5003,500
1620303,0001,000
1714244,0001,800
1809183,0002,200

Statistical Analysis of fuzzy-GSK, fuzzy-NSGA-II, and fuzzy-iMOGA (for 18 activities)

AlgorithmProject timeProject cost


MinMaxMeanMedianModeStdMinMaxMeanMedianModeStd
Fuzzy-GSK104169125.7116.716920.079.804e+041.267e+051.07e+051.027e+059.084e+049,068
Fuzzy-NSGA-II104169123.8113.810419.239.81e+041.274e+051.077e+051.038e+059.81e+049,298
Fuzzy-iMOGA104136.6113.5111.11048.6911.029e+051.321e+051.16e+051.15e+051.029e+058,930

Statistical analysis of fuzzy-GSK, fuzzy-NSGA-II, and fuzzy-iMOGA (for seven activities)

AlgorithmProject timeProject cost


MinMaxMeanMedianModeStdMinMaxMeanMedianModeStd
Fuzzy-GSK601057775.36013.059.62e+041.417e+051.137e+051.105e+059.62e+041.449e+04
Fuzzy-NSGA-II6010577.474.6810513.359.62e+041.418e+051.134e+051.112e+059.62e+041.419e+04
Fuzzy-iMOGA60.695.5674.773.0460.610.419.733e+041.422e+051.156e+051.137e+059.733e+051.334e+04

Statistical results for 18 activities

Statistical valuesCompletion timeTotal costUnpredictable factors
Min97.19.185e+04ME = 0.9, LE = 0.8, WC = 0.1 Good conditions
Max1411.45e+05
Mean112.41.028e+04
Median108.39.611e+04
Mode97.19.185e+04
Std13.591.294e+04
Range43.865.32e+04

Min1049.88e+04ME = 0.5, LE = 0.5, WC = 0.5 Normal conditions
Max1611.296e+05
Mean122.51.079e+05
Median113.91.036e+05
Mode1049.88e+04
Std17.029372
Range56.983.077e+04

Min114.91.089e+05ME = 0.9, LE-0.5, WC = 0.1 Average Conditions
Max182.81.528e+05
Mean137.61.196e+05
Median129.21.138e+05
Mode114.91.089e+05
Std21.361.142e+04
Range67.834.393e+04

Min119.21.124e+05ME = 0.1, LE = 0.1, WC = 0.1 Worst conditions
Max193.71.475e+05
Mean143.81.23e+05
Median1341.177e+05
Mode193.71.124e+05
Std23.151.102e+04
Range74.513.509e+04

Statistical Analysis of fuzzy-GSK, fuzzy-NSGA-II, and fuzzy-iMOGA (for 13 activities)

AlgorithmProject timeProject cost


MinMaxMeanMedianModeStdMinMaxMeanMedianModeStd
Fuzzy-GSK108125115.5115.31084.9512.44e+042.684e+042.543e+042.536e+042.44e+04729.3
Fuzzy-NSGA-II108125115.7115.51085.0142.44e+042.684e+042.541e+042.534e+042.44e+04735.9
Fuzzy-iMOGA108121.1114.11141083.942.46e+042.689e+042.562e+042.556e+042.46e+04672.3

Statistical results for 13 activities

Statistical valuesCompletion timeTotal costUnpredictable factors
Min100.82.278e+04ME = 0.9, LE = 0.8, WC = 0.1 Good conditions
Max116.72.507e+04
Mean107.32.382e+04
Median106.82.38e+04
Mode100.82.278e+04
Std4.429676.5
Range15.852288

Min1082.44e+04ME = 0.5, LE = 0.5, WC = 0.5 Normal conditions
Max1252.708e+04
Mean1152.555e+04
Median114.22.544e+04
Mode1252.44e+04
Std5.068783.6
Range172677

Min119.42.684e+04ME = 0.9, LE = 0.5, WC = 0.1 Average conditions
Max138.22.967e+04
Mean127.22.809e+04
Median126.42.808e+04
Mode119.42.967e+04
Std5.522834.5
Range18.82822

Min123.82.779e+04ME = 0.1, LE = 0.1, WC = 0.1 Worst conditions
Max143.33.078e+04
Mean132.12.907e+04
Median131.92.899e+04
Mode123.82.799e+04
Std5.68878.2
Range19.482983

Statistical results for seven activities

Statistical ValuesCompletion timeTotal costUnpredictable Factors
Min56.027.815e+04ME = 0.9, LE = 0.8, WC = 0.1 Good conditions
Max98.021.148e+05
Mean70.489.11e+04
Median66.448.813e+04
Mode56.027.815e+04
Std12.691.174e+04
Range423.663e+04

Min608.372e+04ME = 0.5, LE = 0.5, WC = 0.5 Normal conditions
Max1051.23e+05
Mean76.19.717e+04
Median72.399.305e+04
Mode608.372e+04
Std13.861.275e+04
Range453.923e+04

Min66.329.253e+04ME = 0.9, LE = 0.5, WC = 0.1 Average conditions
Max116.11.338e+05
Mean84.471.062e+05
Median80.421.026e+05
Mode116.19.253e+04
Std14.771.259e+04
Range49.744.127e+04

Min68.799.594e+04ME = 0.1, LE = 0.1, WC = 0.1 Worst conditions
Max120.41.388e+05
Mean86.91.107e+05
Median83.331.063e+05
Mode68.799.594e+05
Std14.781.37e+04
Range51.584.285e+04
Language: English
Submitted on: Mar 26, 2025
Published on: Jun 7, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2025 Talal A. Alshammari, Ali Wagdy Mohamed, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.