Optimization of Construction Material Cost through Logistics Planning Model of Dragonfly Algorithm — Particle Swarm Optimization

Son, Pham Vu Hong; Duy, Nguyen Huynh Chi; Dat, Pham Ton

doi:10.1007/s12205-021-1427-5

Cited by 30 publications

(9 citation statements)

References 24 publications

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“…Sun, et al analyzed the vehicle usage and distribution route planning model with time windows based on the door-todoor distribution model, and proposed methods to improve the quality of distribution services [5]. Son et al studied the delivery model of home delivery and believed that the home delivery model could be widely used in online retail [6]. Xie, et al studied B2C e-commerce enterprises and decided that enterprises should adopt a comprehensive analysis of the business scope, financial status, profit model, future planning, main customer types, and geographical division of B2C e-commerce enterprises.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Optimization of E-Commerce Logistics Distribution System Based on Multiobjective Function

Wen

2022

Journal of Control Science and Engineering

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To reduce the complexity and multiple constraints of logistics distribution routing problems, the author proposes an improved genetic algorithm, adaptive immune genetic algorithm (AIGA). The algorithm utilizes a new immune vaccine selection strategy and immune operation method, the optimization process is adaptively changed with the evolutionary algebra, combined with the parallel selection method to optimize the multiobjective logistics distribution path, and the specific steps to solve the multiobjective logistics distribution path problem are given. The experimental results show that the optimal path is calculated as 0—2—8—5—3—1—0; 0—4—7—6—0. AIGA not only efficiently converges to the optimal solution but also stabilizes to a fitness value of 66.5, reflecting better precision than IGA. The computational efficiency and convergence of the algorithm are significantly improved, which verifies the practicability and effectiveness of the algorithm. The adaptive immune genetic algorithm realizes the path optimization of complex logistics distribution, which can better change the global search performance of the original immune genetic algorithm, greatly improve the algorithm convergence speed, and achieve good results in practical applications.

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Section: Literature Reviewmentioning

confidence: 99%

Design and Optimization of E-Commerce Logistics Distribution System Based on Multiobjective Function

Wen

2022

Journal of Control Science and Engineering

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“…Evidently, the NFL has enabled researchers to improve and adapt current algorithms for solving different problems or propose new algorithms to provide competitive results against current algorithms. There are a significant number of developed hybrid metaheuristic algorithms, including the ant colony system-based decision support system (ACS-SGPU) 13 , dragonfly algorithm–particle swarm optimization model 14 , quantum-based sine cosine algorithm 15 , the improved sine–cosine algorithm based on orthogonal parallel information 16 , the hybrid sine cosine algorithm with multi-orthogonal search strategy 17 .…”

Section: Literature Reviewmentioning

confidence: 99%

Solving large-scale discrete time–cost trade-off problem using hybrid multi-verse optimizer model

Son

Dang

2023

Sci Rep

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The analysis of the relationship between time and cost is a crucial aspect of construction project management. Various optimization techniques have been developed to solve time–cost trade-off problems. A hybrid multi-verse optimizer model (hDMVO) is introduced in this study, which combines the multi-verse optimizer (MVO) and the sine cosine algorithm (SCA) to address the discrete time–cost trade-off problem (DTCTP). The algorithm's optimality is evaluated by using 23 well-known benchmark test functions. The results demonstrate that hDMVO is competitive with MVO, SCA, the dragonfly algorithm and ant lion optimization. The performance of hDMVO is evaluated using four benchmark test problems of DTCTP, including two medium-scale instances (63 activities) and two large-scale instances (630 activities). The results indicate that hDMVO can provide superior solutions in the time–cost optimization of large-scale and complex projects compared to previous algorithms.

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“…Son et al ( 2022 ) optimized the schedule using the matrix method and the dolphin algorithm. Son et al ( 2021 ) presented the optimization algorithm of the dragonfly particle swarm for the cost optimization of construction material. Son and Anh ( 2021 ) created a game model for the contractor selection process’ compensation idea, and Son ( 2021 ) used a dynamic hybrid bacterium to optimize the layout of the construction site.…”

Section: Literature Reviewmentioning

confidence: 99%

Building projects with time–cost–quality–environment trade-off optimization using adaptive selection slime mold algorithm

Son

Khoi

2023

Asian J Civ Eng

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Every project manager deals with various challenges, and almost all tasks have backup plans to ensure efficient success. Therefore, it is essential to manage resources, notably in terms of time, cost, quality, and environmental impact, and this needs to be thoroughly shown. As a result, the adaptive selection slime mold algorithm (ASSMA) is proposed for repetitive projects due to multiple concurrent instances. It is made by merging the tournament selection (TS) method and the slime mold algorithm (SMA) model. The new model’s capabilities are demonstrated using a case study of a rural water pipeline project, and the outcomes of the ASSMA are contrasted with those of the data envelopment analysis (DEA) approach utilized by the previous researcher. Consequently, the ASSMA technique is an effective optimization matching method that can help project managers select the best strategy for a given activity. This study is anticipated to expand significantly and outperform other models by utilizing quality performance metrics.

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Optimization of Construction Material Cost through Logistics Planning Model of Dragonfly Algorithm — Particle Swarm Optimization

Cited by 30 publications

References 24 publications

Design and Optimization of E-Commerce Logistics Distribution System Based on Multiobjective Function

Design and Optimization of E-Commerce Logistics Distribution System Based on Multiobjective Function

Solving large-scale discrete time–cost trade-off problem using hybrid multi-verse optimizer model

Building projects with time–cost–quality–environment trade-off optimization using adaptive selection slime mold algorithm

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