[1] Liu L, Burns SA, Feng C-W. Construction Time-Cost Trade-Off Analysis Using LP/IP Hybrid Method. J Constr Eng Manag 1995;121:446–54. doi:10.1061/(ASCE)0733-9364(1995)121:4(446).
[2] Sakellaropoulos S, Chassiakos AP. Project time–cost analysis under generalised precedence relations. Adv Eng Softw 2004;35:715–24. doi:10.1016/j.advengsoft.2004.03.017.
[3] Klanšek U, Pšunder M. MINLP optimization model for the nonlinear discrete time–cost trade-off problem. Adv Eng Softw 2012;48:6–16. doi:10.1016/j.advengsoft.2012.01.006.
[4] De P, James Dunne E, Ghosh JB, Wells CE. The discrete time-cost tradeoff problem revisited. Eur J Oper Res 1995;81:225–38. doi:10.1016/0377-2217(94)00187-H.
[5] Akkan C, Drexl A, Kimms A. Network decomposition-based benchmark results for the discrete time–cost tradeoff problem. Eur J Oper Res 2005;165:339–58. doi:10.1016/j.ejor.2004.04.006.
[6] Hazır Ö, Haouari M, Erel E. Discrete time/cost trade-off problem: A decomposition-based solution algorithm for the budget version. Comput Oper Res 2010;37:649–55. doi:10.1016/j.cor.2009.06.009.
[7] Feng C-W, Liu L, Burns SA. Using Genetic Algorithms to Solve Construction Time-Cost Trade-Off Problems. J Comput Civ Eng 1997;11:184–9. doi:10.1061/(ASCE)0887-3801(1997)11:3(184).
[8] Hegazy T. Optimization of construction time-cost trade-off analysis using genetic algorithms. Can J Civ Eng 1999;26:685–97. doi:10.1139/l99-031.
[9] Li H, Love P. Using Improved Genetic Algorithms to Facilitate Time-Cost Optimization. J Constr Eng Manag 1997;123:233–7. doi:10.1061/(ASCE)0733-9364(1997)123:3(233).
[10] Li H, Cao J-N, Love PED. Using Machine Learning and GA to Solve Time-Cost Trade-Off Problems. J Constr Eng Manag 1999;125:347–53. doi:10.1061/(ASCE)0733-9364(1999)125:5(347).
[11] Zheng DXM, Ng ST, Kumaraswamy MM. Applying a Genetic Algorithm-Based Multiobjective Approach for Time-Cost Optimization. J Constr Eng Manag 2004;130:168–76. doi:10.1061/(ASCE)0733-9364(2004)130:2(168).
[12] Zheng DXM, Ng ST, Kumaraswamy MM. Applying Pareto Ranking and Niche Formation to Genetic Algorithm-Based Multiobjective Time–Cost Optimization. J Constr Eng Manag 2005;131:81–91. doi:10.1061/(ASCE)0733-9364(2005)131:1(81).
[13] Yang I-T. Using Elitist Particle Swarm Optimization to Facilitate Bicriterion Time-Cost Trade-Off Analysis. J Constr Eng Manag 2007;133:498–505. doi:10.1061/(ASCE)0733-9364(2007)133:7(498).
[14] Zhang H, Li H. Multi‐objective particle swarm optimization for construction time‐cost tradeoff problems. Constr Manag Econ 2010;28:75–88. doi:10.1080/01446190903406170.
[15] Ng ST, Zhang Y. Optimizing Construction Time and Cost Using Ant Colony Optimization Approach. J Constr Eng Manag 2008;134:721–8. doi:10.1061/(ASCE)0733-9364(2008)134:9(721).
[16] Xiong Y, Kuang Y. Applying an Ant Colony Optimization Algorithm-Based Multiobjective Approach for Time–Cost Trade-Off. J Constr Eng Manag 2008;134:153–6. doi:10.1061/(ASCE)0733-9364(2008)134:2(153).
[17] Afshar A, Ziaraty AK, Kaveh A, Sharifi F. Nondominated Archiving Multicolony Ant Algorithm in Time–Cost Trade-Off Optimization. J Constr Eng Manag 2009;135:668–74. doi:10.1061/(ASCE)0733-9364(2009)135:7(668).
[18] Sonmez R, Bettemir ÖH. A hybrid genetic algorithm for the discrete time–cost trade-off problem. Expert Syst Appl 2012;39:11428–34. doi:10.1016/j.eswa.2012.04.019.
[19] Elbeltagi E, Hegazy T, Grierson D. Comparison among five evolutionary-based optimization algorithms. Adv Eng Informatics 2005;19:43–53. doi:10.1016/j.aei.2005.01.004.
[20] xlOptimizer software, www.xloptimizer.com, 2015.
[21] Eiben AE, Smith JE. Introduction to evolutionary computing. vol. 53. Springer; 2003.
[22] Koumousis VK, Katsaras CP. A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans Evol Comput 2006;10:19–28. doi:10.1109/TEVC.2005.860765.
[23] Krishnakumar K. Micro-genetic algorithms for stationary and non-stationary function optimization. Intell. Control Adapt. Syst., vol. 1196, International Society for Optics and Photonics; 1990, p. 289–96.
[24] Kennedy J, Eberhart R. Particle swarm optimization. Proc. IEEE Int. Conf. neural networks (Perth, Aust., 1995, p. 1942–8.
[25] Price K, Storn RM, Lampinen JA. Differential evolution: a practical approach to global optimization. Springer Science & Business Media; 2006.
[26] Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 2008;8:687–97. doi:10.1016/j.asoc.2007.05.007.