Evolutionary Algorithm Performance Evaluation in Project Time-Cost Optimization

Document Type: Regular Article

Authors

1 Associate Professor, Department of Civil Engineering, University of Patras, Patras, Greece

2 Civil Engineer, Graduate, Department of Civil Engineering, University of Patras, Patras, Greece

10.22115/scce.2019.155434.1091

Abstract

The time-cost trade-off problem pertains to the assessment of the best method of activity construction so that a project is completed within a given deadline and at least cost. Although several evolutionary-type of algorithms have been reported over the last two decades to solve this NP-hard combinatorial problem, there are not many comparative studies independently evaluating several methods. Such studies can provide support to project managers regarding the selection of the appropriate method. The objective of this work is to comparatively evaluate the performance potential of a number of evolutionary algorithms, each one with its own variations, for the time-cost trade-off problem. The evaluation is based on two measures of effectiveness, the solution quality (accuracy) and the processing time to obtain the solution. The solution is sought via a general purpose commercial optimization software without much interference in algorithm parameter setting and fine-tuning in an attempt to follow the anticipated project manager approach. The investigation has been based on case studies from the literature with varying project size and characteristics. Results indicate that certain structures of genetic algorithms, particle swarm optimization, and differential evolution method present the best performance.

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[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.