2020
DOI: 10.17559/tv-20191212113720
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Multi-objective Optimization of Construction Project Based on Improved Ant Colony Algorithm

Abstract: It is the key and difficult problem for the current project management to consider the multi-objective optimization of the four elements, such as quality, duration, cost and safety. To improve the accuracy and efficiency of project management during the engineering construction, considering the advantages and disadvantages of the traditional quality-cost-time model, the four elements were regarded as a system, and a multi-objective optimization model was established. The improved ant colony algorithm was used … Show more

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Cited by 4 publications
(2 citation statements)
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“…Huang et al [ 13 ] proposed a real-time dynamic scheduling mechanism of manufacturing system based on event triggering from the aspects of scheduling method, multi-agent coordinated control, and scheduling control algorithm. Li et al [ 14 ] studied the multi-objective planning using ant colony algorithm, and verified through engineering practice that the optimized ant colony algorithm can avoid the low efficiency of optimal solution search and the shortage of initial pheromone.…”
Section: Related Workmentioning
confidence: 99%
“…Huang et al [ 13 ] proposed a real-time dynamic scheduling mechanism of manufacturing system based on event triggering from the aspects of scheduling method, multi-agent coordinated control, and scheduling control algorithm. Li et al [ 14 ] studied the multi-objective planning using ant colony algorithm, and verified through engineering practice that the optimized ant colony algorithm can avoid the low efficiency of optimal solution search and the shortage of initial pheromone.…”
Section: Related Workmentioning
confidence: 99%
“…Considering the laboratory and economic conditions, the independently designed and manufactured model device was adopted in the indoor model test [39,40]. The sizes of the model box were Length × Width × Height = 1.2 × 0.8 × 0.6 m. As shown in Fig.…”
Section: Verification and Analysis Of Indoor Model Testmentioning
confidence: 99%