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

• Published in: KSCE journal of civil engineering Vol. 25; no. 7; pp. 2350 - 2359
• Main Authors: Son, Pham Vu Hong, Duy, Nguyen Huynh Chi, Dat, Pham Ton
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Civil Engineers 01.07.2021; Springer Nature B.V; 대한토목학회
• 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; Construction logistics planning; Optimizing material; Optimize delivery schedules; PSO algorithm; Inventory control; Time to import construction materials; Dragonfly algorithm
• ISSN: 1226-7988; 1976-3808
• DOI: 10.1007/s12205-021-1427-5

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.