Study on Multi-Objective Optimization of Construction Project Based on Improved Genetic Algorithm and Particle Swarm Optimization
Abstract
:1. Introduction
- (i)
- Mathematical models of the construction project period, cost, and quality were established, respectively, on the basis of which six optimization objectives were determined, forming six multi-objective optimization models of period–cost–quality.
- (ii)
- An improved GA with a time-varying mutation rate is proposed for the construction project for optimization, based on which PSO is used for further optimization to avoid falling into the local optimum and to improve the stability of performance.
- (iii)
- The performance of the proposed GA-PSO method has been thoroughly investigated using four different real construction projects with uncertainty analysis, and the results have been compared with other existing methods.
2. Methods for Multi-Objective Optimization of Construction Project
2.1. Flowchart
Algorithm 1: GA-PSO method |
1. Parameter setting. 2. Binary coding of variables. 3. Initialization of GA. 4. for i = 1:MaxIteration Decoding of binary variables. Multi-objective fitness calculation. Selection with roulette wheel selection method to obtain new individuals. Random crossover of individuals. Mutation of individuals with time-varying mutation rate. end 5. Obtain top individuals by GA. 6. Initialization of PSO by Binary decoding of variables. 7. for j = 1:MaxIteration Updating particles in terms of velocity and position. Limiting positions for particles. Multi-objective fitness calculation. Updating particles in terms of local and global information. end 8. Obtain the best particle. 9. Decoding of the best particle. |
2.2. Models
2.2.1. Construction Period Modeling
2.2.2. Cost Modeling
2.2.3. Quality Modeling
2.2.4. Multi-Objective Optimization Modeling
2.3. Optimization Methods
3. Case Study
3.1. Case 1
3.1.1. Construction Parameters
3.1.2. Monte Carlo Simulation
3.2. Case 2
3.2.1. Construction Parameters
3.2.2. Monte Carlo Simulation
3.3. Case 3
3.3.1. Construction Parameters
3.3.2. Monte Carlo Simulation
3.4. Case 4
3.4.1. Construction Parameters
3.4.2. Monte Carlo Simulation
3.5. Scheme Settings
4. Results and Discussion
4.1. Case 1
4.1.1. Scheme 1
4.1.2. Scheme 2
4.1.3. Scheme 3
4.1.4. Scheme 4
4.1.5. Scheme 5
4.1.6. Scheme 6
4.2. Case 2
4.2.1. Scheme 1
4.2.2. Scheme 2
4.2.3. Scheme 3
4.2.4. Scheme 4
4.2.5. Scheme 5
4.2.6. Scheme 6
4.3. Case 3
4.3.1. Scheme 1
4.3.2. Scheme 2
4.3.3. Scheme 3
4.3.4. Scheme 4
4.3.5. Scheme 5
4.3.6. Scheme 6
4.4. Case 4
4.4.1. Scheme 1
4.4.2. Scheme 2
4.4.3. Scheme 3
4.4.4. Scheme 4
4.4.5. Scheme 5
4.4.6. Scheme 6
4.5. Comprehensive Analysis
5. Conclusions
- (1)
- Compared with existing PSO and GA methods, the proposed GA-PSO improves the optimization performance by shortening the construction period by 0.44–9.02 d, reducing the cost by ¥1400–166,000, and enhancing the quality by 0.0009–0.0244 when constraining the two objectives while optimizing the third objective, while improving the performance by 4.3–8.5% when utilizing comprehensive multi-objective optimization targets.
- (2)
- The proposed GA-PSO can reduce the volatility of optimized plans by 19.5–66.5% compared to PSO and GA.
- (3)
- In the comprehensive analysis, the proposed multi-objective hybrid optimization algorithm can improve the optimized construction plan by 4.3–8.5% and, meanwhile, decrease the volatility of the optimized plan by 37.5–64.4%, which can provide a reference for the optimization of construction organization of the construction project.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Process | Content of Construction Process |
---|---|
1 | Reinforcement of the formation at the end of the well |
2 | Construction of shield-supporting facilities |
3 | Pile driving |
4 | Preparation for the start of the shield structure on the left line |
5 | Left line shield 100 rings test boring |
6 | Left line of the south bank of the Minjiang River, normal digging |
7 | Left line under the Minjiang River, normal digging |
8 | Left line of the north bank of the Minjiang River, normal digging |
9 | Left line, digging to receive |
10 | Left shield disintegrated and lifted out |
11 | Construction of No. 1 contact tunnel |
12 | Construction of No. 2 contact tunnel |
Process | Tmin (d) | Tmax (d) | (104 ¥) | (104 ¥) | (104 ¥) | (104 ¥) | |||
---|---|---|---|---|---|---|---|---|---|
1 | 38 | 42 | 793 | 821 | 190 | 210 | 0.86 | 0.89 | 0.070 |
2 | 50 | 50 | 375 | 375 | 250 | 250 | 0.90 | 0.90 | 0.053 |
3 | 54 | 61 | 96 | 122 | 54 | 61 | 0.83 | 0.85 | 0.053 |
4 | 53 | 56 | 62 | 73 | 53 | 56 | 0.82 | 0.85 | 0.072 |
5 | 25 | 28 | 375 | 403 | 125 | 140 | 0.80 | 0.84 | 0.091 |
6 | 62 | 64 | 2364 | 2379 | 310 | 320 | 0.78 | 0.82 | 0.110 |
7 | 70 | 74 | 1591 | 1622 | 350 | 370 | 0.78 | 0.84 | 0.172 |
8 | 19 | 20 | 1018 | 1028 | 95 | 100 | 0.78 | 0.80 | 0.110 |
9 | 18 | 20 | 414 | 428 | 90 | 100 | 0.79 | 0.81 | 0.085 |
10 | 22 | 22 | 14 | 14 | 11 | 11 | 0.80 | 0.80 | 0.056 |
11 | 184 | 186 | 55 | 62 | 36 | 37 | 0.75 | 0.78 | 0.064 |
12 | 180 | 185 | 69 | 75 | 36 | 37 | 0.75 | 0.78 | 0.064 |
Process | Content of Construction Process |
---|---|
1 | Reinforcement of the formation at the end of the well |
2 | Construction of shield-supporting facilities |
3 | Preparation for the start of the shield structure on the left line |
4 | Left line shield 100 m trial boring |
5 | Left line Wulong Jiangxi bank section, normal digging |
6 | Left line crossing the Wulong River, normal digging |
7 | Left line digging to wind shaft receiving |
8 | Left line digging through the middle air shaft |
9 | Left line east bank section of the Wulong River, normal digging |
10 | Left line digging to receive |
11 | Left shield disintegrated and lifted out |
Process | Tmin (d) | Tmax (d) | (104 ¥) | (104 ¥) | (104 ¥) | (104 ¥) | |||
---|---|---|---|---|---|---|---|---|---|
1 | 65 | 75 | 943 | 1023 | 227.5 | 262.5 | 0.72 | 0.85 | 0.0467 |
2 | 50 | 52 | 1388 | 1408 | 175 | 182 | 0.83 | 0.89 | 0.0205 |
3 | 60 | 62 | 107 | 108 | 210 | 217 | 0.82 | 0.89 | 0.0832 |
4 | 33 | 35 | 389 | 396 | 115.5 | 122.5 | 0.77 | 0.83 | 0.1224 |
5 | 88 | 90 | 2105 | 2137 | 308 | 315 | 0.75 | 0.82 | 0.1418 |
6 | 317 | 320 | 4495 | 4789 | 1109.5 | 1120 | 0.72 | 0.81 | 0.2244 |
7 | 5 | 7 | 98 | 102 | 17.5 | 24.5 | 0.77 | 0.83 | 0.0508 |
8 | 40 | 42 | 114 | 115 | 140 | 147 | 0.74 | 0.8 | 0.0508 |
9 | 92 | 105 | 2147 | 2202 | 322 | 367.5 | 0.79 | 0.82 | 0.1326 |
10 | 4 | 8 | 104 | 131 | 14 | 28 | 0.70 | 0.80 | 0.0758 |
11 | 45 | 50 | 192 | 232 | 157.5 | 175 | 0.78 | 0.82 | 0.0510 |
Process | Content of Construction Process |
---|---|
1 | Construction preparation |
2 | Removal of humus layer |
3 | Excavation |
4 | Landfill |
5 | Level the surface |
6 | Compact |
7 | Substrate construction for runways and blowdown protection |
8 | Taxiway Subgrade Construction |
9 | Paddock Road Subgrade Construction |
10 | Subgrade construction for aprons |
11 | Construction of runway and blowdown protection surfaces |
12 | Taxiway Surface Construction |
13 | Paddock Road Surfacing |
14 | Aircraft surface construction |
15 | Project closeout |
Process | Tmin (d) | Tmax (d) | (104 ¥) | (104 ¥) | (104 ¥) | (104 ¥) | ||
---|---|---|---|---|---|---|---|---|
1 | 6 | 8 | 1432 | 5314 | 1032 | 1567 | 0.56 | 0.87 |
2 | 5 | 10 | 4500 | 10,321 | 2032 | 3210 | 0.68 | 0.8 |
3 | 100 | 108 | 65,046 | 69,431 | 33,162 | 41,537 | 0.82 | 0.95 |
4 | 60 | 70 | 480 | 577 | 165 | 210 | 0.77 | 0.97 |
5 | 48 | 55 | 577 | 605 | 160 | 231 | 0.68 | 0.92 |
6 | 28 | 35 | 8135 | 8861 | 2163 | 3263 | 0.86 | 0.93 |
7 | 53 | 62 | 356,351 | 368,716 | 98,005 | 110,236 | 0.78 | 0.82 |
8 | 39 | 43 | 366,547 | 387,752 | 63,165 | 67,533 | 0.78 | 0.91 |
9 | 14 | 18 | 51,328 | 55,654 | 9856 | 11,246 | 0.83 | 0.89 |
10 | 33 | 36 | 364,110 | 386,321 | 103,256 | 136,142 | 0.85 | 0.96 |
11 | 136 | 145 | 183,865 | 208,650 | 62,081 | 120,115 | 0.85 | 0.91 |
12 | 74 | 80 | 231,596 | 243,706 | 52,056 | 68,449 | 0.68 | 0.91 |
13 | 44 | 49 | 35,625 | 37,846 | 11,365 | 14,025 | 0.75 | 0.92 |
14 | 114 | 126 | 175,343 | 186,455 | 81,135 | 98,741 | 0.86 | 0.91 |
15 | 14 | 15 | 4782 | 5028 | 1452 | 1765 | 0.87 | 0.92 |
Process | Content of Construction Process |
---|---|
1 | Foundation |
2 | 1st-floor wall |
3 | 1st-floor beams and slabs |
4 | 1st- and 2nd-floor stairs |
5 | 1st-floor cast-in-place |
6 | 2nd-floor wall |
7 | 2nd-floor beams and slabs |
8 | 2nd- and 3rd-floor stairs |
9 | 3rd-floor wall |
10 | 3rd-floor beams and slabs |
11 | roofing |
12 | 1st-floor windows, doors, and trim |
13 | Installation of equipment on the 1st floor |
14 | 2nd-floor windows, doors, and trim |
15 | Installation of equipment on the 2nd floor |
16 | 3rd-floor windows, doors, and trim |
17 | Installation of equipment on the 3rd floor |
18 | Exterior wall decoration |
19 | Steps and bulkheads, etc. |
Process | Tmin (d) | Tmax (d) | (104 ¥) | (104 ¥) | (104 ¥) | (104 ¥) | |||
---|---|---|---|---|---|---|---|---|---|
1 | 18 | 30 | 92,038 | 154,608 | 63,000 | 105,000 | 0.85 | 0.95 | 0.0875 |
2 | 15 | 18 | 85,563 | 119,788 | 52,500 | 63,000 | 0.90 | 0.91 | 0.0772 |
3 | 25 | 30 | 135,581 | 189,816 | 87,500 | 105,000 | 0.92 | 0.93 | 0.0637 |
4 | 12 | 13 | 9867 | 13,813 | 42,000 | 45,500 | 0.89 | 0.90 | 0.0557 |
5 | 7 | 7 | 9762 | 9762 | 24,500 | 24,500 | 0.87 | 0.87 | 0.0356 |
6 | 15 | 18 | 85,563 | 119,788 | 52,500 | 63,000 | 0.85 | 0.90 | 0.0678 |
7 | 25 | 30 | 135,581 | 189,816 | 87,500 | 105,000 | 0.80 | 0.89 | 0.0609 |
8 | 12 | 13 | 9867 | 13,813 | 42,000 | 45,500 | 0.87 | 0.88 | 0.0533 |
9 | 16 | 20 | 100,003 | 140,007 | 56,000 | 70,000 | 0.82 | 0.89 | 0.0661 |
10 | 25 | 30 | 135,581 | 189,816 | 87,500 | 105,000 | 0.85 | 0.87 | 0.0594 |
11 | 8 | 15 | 45,793 | 88,641 | 28,000 | 52,500 | 0.86 | 0.90 | 0.0476 |
12 | 9 | 13 | 67,997 | 95,195 | 31,500 | 45,500 | 0.86 | 0.88 | 0.0316 |
13 | 2 | 4 | 15,062 | 21,086 | 7000 | 14,000 | 0.87 | 0.89 | 0.0508 |
14 | 9 | 13 | 65,332 | 91,464 | 31,500 | 45,500 | 0.87 | 0.88 | 0.0331 |
15 | 2 | 4 | 15,062 | 21,086 | 7000 | 14,000 | 0.87 | 0.88 | 0.0523 |
16 | 9 | 13 | 65,332 | 91,464 | 31,500 | 45,500 | 0.87 | 0.88 | 0.0338 |
17 | 2 | 4 | 15,062 | 21,086 | 7000 | 14,000 | 0.87 | 0.88 | 0.0509 |
18 | 12 | 16 | 56,246 | 79,342 | 42,000 | 56,000 | 0.84 | 0.86 | 0.0367 |
19 | 3 | 5 | 5505 | 7708 | 10,500 | 17,500 | 0.86 | 0.87 | 0.0360 |
Scheme | Optimization Target |
---|---|
Scheme 1 | Cost and quality are controlled within certain limits, and the construction period is minimized. |
Scheme 2 | Period and quality are controlled within certain limits, and the construction cost is minimized. |
Scheme 3 | Period and cost are controlled within certain limits, and the construction quality is maximized. |
Scheme 4 | Multi-objective optimization of construction period, cost, and quality with equal weights |
Scheme 5 | Multi-objective optimization of construction period, cost, and quality with a weight of (0.3, 0.4, 0.3) |
Scheme 6 | Multi-objective optimization of construction period, cost, and quality with a weight of (0.2, 0.2, 0.6) |
Case | Scheme | Performance | Volatility | ||
---|---|---|---|---|---|
PSO | GA | PSO | GA | ||
1 | 1 | 1.18 d | 2.26 d | 73.8% | 42.1% |
2 | ¥22,000 | ¥36,000 | −7.0% | −5.8% | |
3 | 0.0028 | 0.0009 | 45.4% | −66.7% | |
4 | 3.4% | 5.3% | 100% | 100% | |
5 | 4.1% | 5.7% | 100% | 100% | |
6 | 4.8% | 8.6% | 100% | 100% | |
2 | 1 | 1.71 d | 3.69 d | 68.2% | 39.8% |
2 | ¥20,000 | ¥166,000 | 78.1% | 71.8% | |
3 | 0.0030 | 0.0072 | 97.0% | 85.7% | |
4 | 1.8% | 5.5% | 85.8% | 66.3% | |
5 | 2.1% | 4.7% | 77.8% | 30.9% | |
6 | 2.4% | 7.8% | 95.6% | 88.0% | |
3 | 1 | 0.44 d | 1.64 d | 100% | 100% |
2 | ¥1400 | ¥5000 | 95.0% | 88.3% | |
3 | 0.0101 | 0.0244 | 55.9% | −39.1% | |
4 | 3.0% | 9.1% | 89.1% | 68.9% | |
5 | 2.5% | 7.9% | 83.6% | 39.5% | |
6 | 2.0% | 13.5% | 69.2% | 20.8% | |
4 | 1 | 4.95 d | 9.02 d | 79.8% | 43.2% |
2 | ¥33,400 | ¥28,200 | 72.2% | −48.9% | |
3 | 0.0031 | 0.0037 | −10.5% | −76.2% | |
4 | 5.9% | 9.9% | −45.6% | 57.7% | |
5 | 4.7% | 9.1% | −40.6% | −61.8% | |
6 | 14.9% | 14.6% | 82.9% | 56.4% |
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Hu, W.; Zhang, Y.; Liu, L.; Zhang, P.; Qin, J.; Nie, B. Study on Multi-Objective Optimization of Construction Project Based on Improved Genetic Algorithm and Particle Swarm Optimization. Processes 2024, 12, 1737. https://doi.org/10.3390/pr12081737
Hu W, Zhang Y, Liu L, Zhang P, Qin J, Nie B. Study on Multi-Objective Optimization of Construction Project Based on Improved Genetic Algorithm and Particle Swarm Optimization. Processes. 2024; 12(8):1737. https://doi.org/10.3390/pr12081737
Chicago/Turabian StyleHu, Weicheng, Yan Zhang, Linya Liu, Pengfei Zhang, Jialiang Qin, and Biao Nie. 2024. "Study on Multi-Objective Optimization of Construction Project Based on Improved Genetic Algorithm and Particle Swarm Optimization" Processes 12, no. 8: 1737. https://doi.org/10.3390/pr12081737