Abstract
Time and cost are two of the most important issues for construction planning. Nowadays, the relationship between time and cost becomes more crucial due to the competitive conditions. The contradiction of these two project factors which are affected by the various project constraints needs to be balanced. In this study, time–cost trade-off (TCT) problem is considered as a multi-objective problem. To solve TCT, a novel hybrid algorithm (NHA) is suggested. This method, which is developed by hybridization of particle swarm optimization (PSO) and genetic algorithm, is compared on the application with standard PSO. NHA, which is expected to be more efficient in terms of avoiding local best points and searching the solution space, also presents shorter and more economical alternatives of the project.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- c :
-
Acceleration coefficient
- gbest(t):
-
Best position in the swarm at the time of “t”
- c ij :
-
Cost of the activity ith of the mode jth
- vi(t):
-
The velocity of the particle “i” at the time of “t”
- lbest(t):
-
Best local position at the time of “t”
- mn :
-
Mode alternatives
- rj(t):
-
Stochastic random number
- t t :
-
Total duration of the project
- T ij :
-
Duration of jth mode of ith activity
- T n :
-
Starting time of nth activity
- T max :
-
Maximum completion time
- w :
-
Inertia coefficient
- xi(t):
-
The position of particle “i” at the time of “t”
- x ij :
-
Decision variable of the jth mode for ith activity
- y g :
-
Global best position at the time of “t + 1”
- y i :
-
Local best position at the time of “t + 1”
- GA:
-
Genetic algorithm
- NHA:
-
Novel hybrid algorithm
- PSO:
-
Particle swarm optimization
References
Agdas D, Warne DJ, Osio-Norgaard J, Masters FJ (2018) Utility of genetic algorithms for solving large-scale construction time–cost trade-off problems. J Comput Civ Eng 32(1):1–10. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000718
Albayrak G (2017) A comparative selection of hybrid-metaheuristic methods on time-cost trade-off in resource constrained construction projects. Ph.D. Dissertation, Eskisehir Osmangazi University, Graduate School of Natural and Applied Sciences
Albayrak G, Özdemir İ (2017) A state of art review on metaheuristic methods in time–cost trade-off problems. Int J Struct Civ Eng Res 6(1):30–34. https://doi.org/10.18178/ijscer.6.1.30-34
Albayrak G, Özdemir I (2018) Multimodal optimization for time–cost trade-off in construction projects using a novel hybrid method based on FA and PSO. Rev Constr 17:304–318. https://doi.org/10.7764/RDLC.17.2.304
Anagnostopoulos KP, Kotsikas L (2010) Experimental evaluation of simulated annealing algorithms for the time–cost trade-off problem. Appl Math Comput 217:260–270. https://doi.org/10.1016/j.amc.2010.05.056
Castro-Lacouture D, Suer GA, Gonzalez-Joaqui J, Yates JK (2009) Construction project scheduling with time, cost, and material restrictions using fuzzy mathematical models and critical path method. J Constr Eng Manag 135(10):1096–1104. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:10(1096)
Chen SP, Tsai MJ (2011) Time–cost trade-off analysis of project networks in fuzzy environments. Eur J Oper Res 212(2):386–397. https://doi.org/10.1016/j.ejor.2011.02.002
De P, Dunne EJ, Ghosh JB, Wells CE (1997) Complexity of the discrete time–cost tradeoff problem for project networks. J Oper Res 45:302–306. https://doi.org/10.1287/opre.45.2.302
Feng CW, Liu L, Burns SA (2000) Stochastic construction time–cost trade-off analysis. J Comput Civ Eng 14(2):117–126
Gupta IK, Shakil S, Shakil S (2019) A hybrid GA-PSO algorithm to solve traveling salesman problem. In: Verma N, Ghosh A (eds) Computational intelligence: theories, applications and future directions, advances in intelligent systems and computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_35
Gutjahr WJ, Strauss C, Wagner E (2000) A stochastic branch-and-bound approach to activity crashing in project management. J Comput 12(2):125–135. https://doi.org/10.1287/ijoc.12.2.125.11894
Hazır O, Erel E, Günalay Y (2011) Robust optimization models for the discrete time/cost trade-off problem. Int J Prod Econ 130(1):87–95. https://doi.org/10.1016/j.ijpe.2010.11.018
He Z, He H, Liu R, Wang N (2017) Variable neighbourhood search and tabu search for a discrete time/cost trade-off problem to minimize the maximal cash flow gap. Comput Oper Res 78:564–577. https://doi.org/10.1016/j.cor.2016.07.013
Hegazy T (1999) Optimization of construction time–cost trade-off analysis using genetic algorithms. Can J Civ Eng 26(6):685–697. https://doi.org/10.1139/l99-031
Hendrickson C, Au T (1989) Project management for construction. Prentice Hall, Englewood Cliffs, NJ
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, 2nd edn. MIT Press, Cambridge
Kandil A, El-Rayes K (2005) Parallel computing framework for optimizing construction planning in large-scale projects. J Comput Civ Eng 19(3):304–312. https://doi.org/10.1061/(ASCE)0887-3801(2005)19:3(304)
Ke H, Ma J (2014) Modeling project time–cost trade-off in fuzzy random environment. Appl Soft Comput 19:80–85. https://doi.org/10.1016/j.asoc.2014.01.040
Ke H, Ma W, Ni Y (2009) Optimization models and a GA-based algorithm for stochastic time–cost trade-off problem. Appl Math Comput 215(1):308–313. https://doi.org/10.1016/j.amc.2009.05.004
Kelly JE Jr (1961) Critical path planning and scheduling: mathematical basis. Oper Res 9(3):167–179. https://doi.org/10.1287/opre.9.3.296
Kennedy J, Eberhart R (1995) Particle swarm optimization. Neural Netw Proc 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Koo C, Hong T, Kim S (2015) An integrated multi-objective optimization model for solving the construction time–cost trade-off problem. J Civ Eng Manag 21(3):323–333. https://doi.org/10.3846/13923730.2013.802733
Liu D, Li H, Wang H, Qi C, Rose T (2020) Discrete symbiotic organisms search method for solving large-scale time–cost trade-off problem in construction scheduling. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113230
Meier C, Yassine AA, Browning TR, Walter U (2016) Optimizing time-cost tradeoffs in product development projects with a multi-objective evolutionary algorithm. Research in Engineering Design 27(4):347–366. https://doi.org/10.1007/s00163-016-0222-7
Ng ST, Zhang Y (2008) Optimizing construction time and cost using ant colony optimization approach. J Constr Eng Manag 134(9):721–728. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:9(721)
Pagnoni A (1990) Project engineering: computer oriented planning and operational decision making. Springer, Berlin, p 1990
Pathak BK, Srivastava S (2015) Effects of project uncertainties on nonlinear time–cost tradeoff profile. Iran J Fuzzy Syst 12(4):79–100. https://doi.org/10.22111/IJFS.2015.2086
Sebt MH, Afshar MR, Alipouri Y (2017) Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource constrained project scheduling problem. Eng Optim 49(3):513–530. https://doi.org/10.1080/0305215X.2016.1197610
Son J, Hong T, Lee S (2013) A mixed (continuous + discrete) time–cost trade-off model considering four different relationships with lag time. KSCE J Civ Eng 17(2):281–291. https://doi.org/10.1007/s12205-013-1506-3
Tran DH, Cheng MY, Cao MT (2015) Hybrid multiple objective artificial bee colony with differential evolution for the time–cost-quality tradeoff problem. Knowl Based Syst 74:176–186. https://doi.org/10.1016/j.knosys.2014.11.018
Tran DH, Chou JS, Luong DL (2019) Multi-objective symbiotic organisms optimization for making time–cost tradeoffs in repetitive project scheduling problem. J Civ Eng Manag 25(4):322–339. https://doi.org/10.3846/jcem.2019.9681
Vanhoucke M, Debels D (2007) The discrete time/cost trade-off problem: extensions and heuristic procedures. J Sched 10:311–326. https://doi.org/10.1007/s10951-007-0031-y
Wei H, Su Z, Zhang Y (2020) Preprocessing the discrete time–cost tradeoff problem with generalized precedence relations. Math Probl Eng 63:1–19. https://doi.org/10.1155/2020/6312198
Xiong Y, Kuang Y (2008) Applying an ant colony optimization algorithm-based multiobjective approach for time–cost trade-off. J Constr Eng Manag 134(2):153–156. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:2(153)
Yazdanpanah A, Rezaei A, Mahdiyar H, Kalantariasl A (2019) Development of an efficient hybrid GA-PSO approach applicable for well. Adv Geo Energy Res 3(4):365–374. https://doi.org/10.26804/ager.2019.04.03
Zhang Y, Thomas Ng S (2012) An ant colony system based decision support system for construction time–cost optimization. J Civ Eng Manag 18(4):580–589. https://doi.org/10.3846/13923730.2012.704164
Zheng DXM, Ng ST, Kumaraswamy MM (2004) Applying a genetic algorithm-based multiobjective approach for time–cost optimization. J Constr Eng Manag 130(2):168. 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
Albayrak, G. Novel Hybrid Method in Time–Cost Trade-Off for Resource-Constrained Construction Projects. Iran J Sci Technol Trans Civ Eng 44, 1295–1307 (2020). https://doi.org/10.1007/s40996-020-00437-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40996-020-00437-2