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

Managing a construction project is challenging because of cost, time, safety, and quality considerations. In the most projects, the cost of construction is one of the most critical aspect because material cost alone accounts for significant ratio of the total project. Therefore, the cost of construc...

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Bibliographic Details
Published in:KSCE journal of civil engineering 2021, 25(7), , pp.2350-2359
Main Authors: Son, Pham Vu Hong, Duy, Nguyen Huynh Chi, Dat, Pham Ton
Format: Article
Language:English
Subjects:
Algorithms
Civil Engineering
Construction
Construction costs
Construction Management
Construction materials
Construction sites
Cost analysis
Economic analysis
Economic models
Engineering
Genetic algorithms
Geotechnical Engineering & Applied Earth Sciences
Industrial Pollution Prevention
Inventory management
Logistics
Material requirements planning
Optimization
Order quantity
Particle swarm optimization
Supply chains
토목공학
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Summary:Managing a construction project is challenging because of cost, time, safety, and quality considerations. In the most projects, the cost of construction is one of the most critical aspect because material cost alone accounts for significant ratio of the total project. Therefore, the cost of construction materials should be controlled. In this study, we proposed the use of material requirements planning (MRP) to control the cost of construction materials. After determining the demand for the materials required for construction, we estimated both the quantity of materials required and time taken to deliver the materials to the construction site. Although economic order quantity models have been applied to analyze construction material costs, they do not accurately reflect concerns related to material cost. Therefore, we used the material supply chain model (construction logistics planning) to analyze material costs. To optimize MRP according to the current progress of a project, a novel approach combining the dragonfly algorithm (DA) and particle swarm optimization algorithm (PSO) was proposed. To verify the advanced searchability of the DA–PSO algorithm, the algorithm was compared with the gray wolf and the genetic algorithms.
ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-021-1427-5