Abstract
In the construction industry, determining project schedules has become one of the most critical subjects among project managers. These schedules oftentimes result in significant resource fluctuations that are costly and impractical for the construction company. Thus, construction managers are required to adjust the resource profile through a resource leveling process. In this paper, a novel optimization model is presented for resource leveling, called the “modified symbiotic organisms search” (MSOS). MSOS is developed based on the standard symbiotic organisms search, but with an improvement in the parasitism phase to better tackle complex optimization problems. A case study is employed to investigate the performance of the proposed optimization model in coping with the resource leveling problem. The experimental results show that the proposed model can find a better quality solution in comparison with existing optimization models.
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Arditi, D., & Pattanakitchamroon, T. (2006). Selecting a delay analysis method in resolving construction claims. International Journal of Project Management, 24(2), 145–155. https://doi.org/10.1016/j.ijproman.2005.08.005.
Assaf, S. A., & Al-Hejji, S. (2006). Causes of delay in large construction projects. International Journal of Project Management, 24(4), 349–357. https://doi.org/10.1016/j.ijproman.2005.11.010.
Cheng, M.-Y., Chiu, C.-K., Chiu, Y.-F., Wu, Y.-W., Syu, Z.-L., Prayogo, D., et al. (2014). SOS optimization model for bridge life cycle risk evaluation and maintenance strategies. Journal of the Chinese Institute of Civil and Hydraulic Engineering, 26(4), 293–308.
Cheng, M.-Y., & Prayogo, D. (2014). Symbiotic organisms search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98–112. https://doi.org/10.1016/j.compstruc.2014.03.007.
Cheng, M.-Y., & Prayogo, D. (2017). A novel fuzzy adaptive teaching–learning-based optimization (FATLBO) for solving structural optimization problems. Engineering with Computers, 33(1), 55–69. https://doi.org/10.1007/s00366-016-0456-z.
Cheng, M.-Y., Prayogo, D., & Tran, D.-H. (2016a). Optimizing multiple-resources leveling in multiple projects using discrete symbiotic organisms search. Journal of Computing in Civil Engineering, 30(3), 04015036. https://doi.org/10.1061/(asce)cp.1943-5487.0000512.
Cheng, M.-Y., Prayogo, D., Wu, Y.-W., & Lukito, M. M. (2016b). A hybrid harmony search algorithm for discrete sizing optimization of truss structure. Automation in Construction, 69, 21–33. https://doi.org/10.1016/j.autcon.2016.05.023.
Cheng, M.-Y., Tran, D.-H., & Hoang, N.-D. (2017). Fuzzy clustering chaotic-based differential evolution for resource leveling in construction projects. Journal of Civil Engineering and Management, 23(1), 113–124. https://doi.org/10.3846/13923730.2014.982699.
Cheng, M.-Y., Wibowo, D. K., Prayogo, D., & Roy, A. F. V. (2015). Predicting productivity loss caused by change orders using the evolutionary fuzzy support vector machine inference model. Journal of Civil Engineering and Management, 21(7), 881–892. https://doi.org/10.3846/13923730.2014.893922.
Christodoulou, S., Ellinas, G., & Michaelidou-Kamenou, A. (2009). Minimum moment method for resource leveling using entropy maximization. Journal of Construction Engineering and Management, 136(5), 518–527. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000149.
Das, S., Maity, S., Qu, B.-Y., & Suganthan, P. N. (2011). Real-parameter evolutionary multimodal optimization—A survey of the state-of-the-art. Swarm and Evolutionary Computation, 1(2), 71–88. https://doi.org/10.1016/j.swevo.2011.05.005.
Easa, S. (1989). Resource leveling in construction by optimization. Journal of Construction Engineering and Management, 115(2), 302–316. https://doi.org/10.1061/(ASCE)0733-9364(1989)115:2(302).
El-Rayes, K., & Jun, D. (2009). Optimizing resource leveling in construction projects. Journal of Construction Engineering and Management, 135(11), 1172–1180. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000097.
Geng, J.-Q., Weng, L.-P., & Liu, S.-H. (2011). An improved ant colony optimization algorithm for nonlinear resource-leveling problems. Computers and Mathematics with Applications, 61(8), 2300–2305. https://doi.org/10.1016/j.camwa.2010.09.058.
Georgy, M. E. (2008). Evolutionary resource scheduler for linear projects. Automation in Construction, 17(5), 573–583. https://doi.org/10.1016/j.autcon.2007.10.005.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley Longman Publishing Co., Inc.
Harris, R. (1990). Packing method for resource leveling (pack). Journal of Construction Engineering and Management, 116(2), 331–350. https://doi.org/10.1061/(ASCE)0733-9364(1990)116:2(331).
Hegazy, T. (1999). Optimization of resource allocation and leveling using genetic algorithms. Journal of Construction Engineering and Management, 125(3), 167–175. https://doi.org/10.1061/(ASCE)0733-9364(1999)125:3(167).
Jong, K. A. D. (1975). An analysis of the behavior of a class of genetic adaptive systems. Michigan: University of Michigan.
Karaa, F., & Nasr, A. (1986). Resource management in construction. Journal of Construction Engineering and Management, 112(3), 346–357. https://doi.org/10.1061/(ASCE)0733-9364(1986)112:3(346).
Kaveh, A. (2017). Applications of metaheuristic optimization algorithms in civil engineering. Cham: Springer.
Kaveh, A., & Ilchi Ghazaan, M. (2018). A new hybrid meta-heuristic algorithm for optimal design of large-scale dome structures. Engineering Optimization, 50(2), 235–252. https://doi.org/10.1080/0305215X.2017.1313250.
Kaveh, A., Khanzadi, M., & Alipour, M. (2016). Fuzzy resource constraint project scheduling problem using CBO and CSS algorithms. International Journal of Civil Engineering, 14(5), 325–337. https://doi.org/10.1007/s40999-016-0031-4.
Kaveh, A., & Nasrollahi, A. (2013). Engineering design optimization using a hybrid PSO and HS algorithm. Asian Journal of Civil Engineering (Bhrc), 14(2), 201–223.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Paper presented at the Proceedings of the IEEE international conference on neural networks, Perth, Australia.
Khanzadi, M., Kaveh, A., Alipour, M., & Aghmiuni, K. H. (2016). Application of CBO and CSS for resource allocation and resource leveling problem. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 40(1), 1–10. https://doi.org/10.1007/s40996-016-0004-5.
Leu, S.-S., Yang, C.-H., & Huang, J.-C. (2000). Resource leveling in construction by genetic algorithm-based optimization and its decision support system application. Automation in Construction, 10(1), 27–41. https://doi.org/10.1016/S0926-5805(99)00011-4.
Mahfoud, S. W. (1995). Niching methods for genetic algorithms. Ph.D. Dissertation, University of Illinois at Urbana-Champaign Champaign, IL, USA.
Martinez, J., & Loannou, P. (1993) Resource leveling based on the modified minimum moment heuristic. Computing in Civil and Building Engineering, 287–294.
Prayogo, D., Cheng, M.-Y., & Prayogo, H. (2017). A novel implementation of nature-inspired optimization for civil engineering: A comparative study of symbiotic organisms search. Civil Engineering Dimension, 19(1), 36–43.
Prayogo, D., Cheng, M.-Y., Wu, Y.-W., Herdany, A. A., & Prayogo, H. (2018). Differential Big Bang-Big Crunch algorithm for construction-engineering design optimization. Automation in Construction, 85, 290–304. https://doi.org/10.1016/j.autcon.2017.10.019.
Qin, A. K., Huang, V. L., & Suganthan, P. N. (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation, 13(2), 398–417.
Sears, S. K., Sears, G. A., & Clough, R. H. (2008). Construction project management: A practical guide to field construction management (5th ed.). New Jersey: Wiley.
Son, J., & Skibniewski, M. J. (1999). Multiheuristic approach for resource leveling problem in construction engineering: Hybrid approach. Journal of Construction Engineering and Management, 125(1), 23–31. https://doi.org/10.1061/(ASCE)0733-9364(1999)125:1(23).
Storn, R. M., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.
Tran, D.-H., Cheng, M.-Y., & Prayogo, D. (2016). A novel multiple objective symbiotic organisms search (MOSOS) for time–cost–labor utilization tradeoff problem. Knowledge-Based Systems, 94, 132–145. https://doi.org/10.1016/j.knosys.2015.11.016.
Tran, H.-H., & Hoang, N.-D. (2014). A novel resource-leveling approach for construction project based on differential evolution. Journal of Construction Engineering, 2014, 7. https://doi.org/10.1155/2014/648938.
Wu, J. I. E., & An, Q. (2012). New approaches for resource allocation via DEA models. International Journal of Information Technology & Decision Making, 11(01), 103–117. https://doi.org/10.1142/S0219622012500058.
Yu, V. F., Redi, A. A. N. P., Yang, C.-L., Ruskartina, E., & Santosa, B. (2017). Symbiotic organisms search and two solution representations for solving the capacitated vehicle routing problem. Applied Soft Computing, 52, 657–672. https://doi.org/10.1016/j.asoc.2016.10.006.
Zhang, M., Luo, W., & Wang, X. (2008). Differential evolution with dynamic stochastic selection for constrained optimization. Information Sciences, 178(15), 3043–3074. https://doi.org/10.1016/j.ins.2008.02.014.
Acknowledgements
The authors gratefully acknowledge that the present research is supported by The Ministry of Research, Technology and Higher Education of the Republic of Indonesia under the “Penelitian Dasar Unggulan Perguruan Tinggi 2018” (PDUPT) Research Grant Scheme (No: 002/SP2H/LT/K7/KM/2017).
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Prayogo, D., Cheng, MY., Wong, F.T. et al. Optimization model for construction project resource leveling using a novel modified symbiotic organisms search. Asian J Civ Eng 19, 625–638 (2018). https://doi.org/10.1007/s42107-018-0048-x
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DOI: https://doi.org/10.1007/s42107-018-0048-x