Abstract
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.
Similar content being viewed by others
Change history
18 November 2021
An Erratum to this paper has been published: https://doi.org/10.1007/s12205-021-6427-y
References
Ala-Risku T, Kärkkäinen M (2006) Material delivery problems in construction projects: A possible solution. International Journal of Production Economics 104:19–29, DOI: https://doi.org/10.1016/j.ijpe.2004.12.027
Behera P, Mohanty RP, Prakash A (2015) Understanding construction supply chain management. Production Planning & Control 26:1332–1350, DOI: https://doi.org/10.1080/09537287.2015.1045953
Bose S, Goswami A, Chaudhuri KS (1995) An EOQ model for deteriorating items with linear time-dependent demand rate and shortages under inflation and time discounting. Journal of the Operational Research Society 46:771–782, DOI: https://doi.org/10.1057/jors.1995.107
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. Proceedings of the sixth international symposium on micro machine and human science (MHS’95), October 4–6, Nagoya, Japan
Eberhart RC, Yuhui S (2001) Particle swarm optimization: Developments, applications and resources. Proceedings of the 2001 congress on evolutionary computation (IEEE Cat No01TH8546), May 27–30, Seoul, Korea, 81–86
Georgy M, Basily SY (2008) Using genetic algorithms in optimizing construction material delivery schedules. Construction Innovation 8:23–45, DOI: https://doi.org/10.1108/14714170810846503
Hatta NM, Zain AM, Sallehuddin R Shayfull Z, Yusoff Y (2019) Recent studies on optimisation method of grey wolf optimiser (GWO): A review (2014–2017). Artificial Intelligence Review 52:2651–2683, DOI: https://doi.org/10.1007/s10462-018-9634-2
Hsu P-Y, Angeloudis P, Aurisicchio M (2018) Optimal logistics planning for modular construction using two-stage stochastic programming. Automation in Construction 94:47–61, DOI: https://doi.org/10.1016/j.autcon.2018.05.029
Jaskowski P, Sobotka A, Czarnigowska A (2018) Decision model for planning material supply channels in construction. Automation in Construction 90:235–242, DOI: https://doi.org/10.1016/j.autcon.2018.02.026
Jia Q, Guo Y (2016) Hybridization of ABC and PSO algorithms for improved solutions of RCPSP. Journal of the Chinese Institute of Engineers 39:727–734, DOI: https://doi.org/10.1080/02533839.2016.1176866
Khanh HD, Kim SY (2016) A survey on production planning system in construction projects based on last planner system. KSCE Journal of Civil Engineering 20(1):1–11, DOI: https://doi.org/10.1007/s12205-015-1412-y
Li Y, Huang Z, Xie Y (2020) Path planning of mobile robot based on improved genetic algorithm. 2020 3rd international conference on electron device and mechanical engineering (ICEDME), May 1–3, Suzhou, China, 691–695
Mao H, Cheng P (2010) Design of material delivery system based on lean construction. International conference of logistics engineering and management (ICLEM), October 8–10, Chengdu, China, 1793–1799
Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems 149:153–165, DOI: https://doi.org/10.1016/j.chemolab.2015.08.020
Min W, Sui Pheng L (2005) Economic order quantity (EOQ) versus just-in-time (JIT) purchasing: An alternative analysis in the ready-mixed concrete industry. Construction Management and Economics 23(4):409–422, DOI: https://doi.org/10.1080/01446190500041339
Mirjalili S (2016) Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications 27:1053–1073, DOI: https://doi.org/10.1007/s00521-015-1920-1
Mitsel AA, Kritski OL, Stavchuk LG (2017) An inventory model with random demand. Journal of Physics: Conference Series 803:012099, DOI: https://doi.org/10.1088/1742-6596/803/1/012099
Nolz PC (2020) Optimizing construction schedules and material deliveries in city logistics: A case study from the building industry. Flexible Services and Manufacturing Journal, DOI: https://doi.org/10.1007/s10696-020-09391-7
Olivieri H, Seppänen O, Alves TdCL, Scala NM, Schiavone V, Liu M, Granja AD (2019) Survey comparing critical path method, last planner system, and location-based techniques. Journal of Construction Engineering and Management 145:04019077, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001644
Panova Y, Hilletofth P (2018) Managing supply chain risks and delays in construction project. Industrial Management & Data Systems 118:1413–1431, DOI: https://doi.org/10.1108/IMDS-09-2017-0422
Polat G, Arditi D (2005) The JIT materials management system in developing countries. Construction Management and Economics 23:697–712, DOI: https://doi.org/10.1080/01446190500041388
Roach B (2005) Origin of the economic order quantity formula; Transcription or transformation? Management Decision 43:1262–1268, DOI: https://doi.org/10.1108/00251740510626317
Said H, El-Rayes K (2011) Optimizing material procurement and storage on construction sites. Journal of Construction Engineering and Management 137:421–431, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000307
Thomas HR, Riley David R, Messner John I (2005) Fundamental principles of site material management. Journal of Construction Engineering and Management 131:808–815, DOI: https://doi.org/10.1061/(ASCE)0733-9364(2005)131:7(808)
Zheng Daisy XM, Ng ST, Kumaraswamy Mohan M (2004) Applying a genetic algorithm-based multiobjective approach for time-cost optimization. Journal of Construction Engineering and Management 130:168–176, DOI: https://doi.org/10.1061/(ASCE)0733-9364(2004)130:2(168)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Son, P.V.H., Duy, N.H.C. & Dat, P.T. Optimization of Construction Material Cost through Logistics Planning Model of Dragonfly Algorithm — Particle Swarm Optimization. KSCE J Civ Eng 25, 2350–2359 (2021). https://doi.org/10.1007/s12205-021-1427-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-021-1427-5