Asian Journal of Civil Engineering

, Volume 19, Issue 5, pp 625–638 | Cite as

Optimization model for construction project resource leveling using a novel modified symbiotic organisms search

  • Doddy Prayogo
  • Min-Yuan Cheng
  • Foek Tjong Wong
  • Daniel Tjandra
  • Duc-Hoc Tran
Original Paper


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.


Construction management Resource leveling Optimization Metaheuristic Symbiotic organisms search 



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).


  1. Arditi, D., & Pattanakitchamroon, T. (2006). Selecting a delay analysis method in resolving construction claims. International Journal of Project Management, 24(2), 145–155. Scholar
  2. Assaf, S. A., & Al-Hejji, S. (2006). Causes of delay in large construction projects. International Journal of Project Management, 24(4), 349–357. Scholar
  3. 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.Google Scholar
  4. Cheng, M.-Y., & Prayogo, D. (2014). Symbiotic organisms search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98–112. Scholar
  5. 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. Scholar
  6. 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. Scholar
  7. 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. Scholar
  8. 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. Scholar
  9. 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. Scholar
  10. 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. Scholar
  11. 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. Scholar
  12. Easa, S. (1989). Resource leveling in construction by optimization. Journal of Construction Engineering and Management, 115(2), 302–316. Scholar
  13. El-Rayes, K., & Jun, D. (2009). Optimizing resource leveling in construction projects. Journal of Construction Engineering and Management, 135(11), 1172–1180. Scholar
  14. 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. Scholar
  15. Georgy, M. E. (2008). Evolutionary resource scheduler for linear projects. Automation in Construction, 17(5), 573–583. Scholar
  16. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley Longman Publishing Co., Inc.zbMATHGoogle Scholar
  17. Harris, R. (1990). Packing method for resource leveling (pack). Journal of Construction Engineering and Management, 116(2), 331–350. Scholar
  18. Hegazy, T. (1999). Optimization of resource allocation and leveling using genetic algorithms. Journal of Construction Engineering and Management, 125(3), 167–175. Scholar
  19. Jong, K. A. D. (1975). An analysis of the behavior of a class of genetic adaptive systems. Michigan: University of Michigan.Google Scholar
  20. Karaa, F., & Nasr, A. (1986). Resource management in construction. Journal of Construction Engineering and Management, 112(3), 346–357. Scholar
  21. Kaveh, A. (2017). Applications of metaheuristic optimization algorithms in civil engineering. Cham: Springer.CrossRefzbMATHGoogle Scholar
  22. 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. Scholar
  23. 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. Scholar
  24. 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.Google Scholar
  25. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Paper presented at the Proceedings of the IEEE international conference on neural networks, Perth, Australia.Google Scholar
  26. 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. Scholar
  27. 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. Scholar
  28. Mahfoud, S. W. (1995). Niching methods for genetic algorithms. Ph.D. Dissertation, University of Illinois at Urbana-Champaign Champaign, IL, USA.Google Scholar
  29. Martinez, J., & Loannou, P. (1993) Resource leveling based on the modified minimum moment heuristic. Computing in Civil and Building Engineering, 287–294.Google Scholar
  30. 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.Google Scholar
  31. 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. Scholar
  32. 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.CrossRefGoogle Scholar
  33. 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.Google Scholar
  34. 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. Scholar
  35. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  36. 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. Scholar
  37. 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. Scholar
  38. 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. Scholar
  39. 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. Scholar
  40. Zhang, M., Luo, W., & Wang, X. (2008). Differential evolution with dynamic stochastic selection for constrained optimization. Information Sciences, 178(15), 3043–3074. Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringPetra Christian UniversitySurabayaIndonesia
  2. 2.Department of Civil and Construction EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan, ROC
  3. 3.Department of Construction Engineering and ManagementHo Chi Minh City University of TechnologyHo Chi Minh CityVietnam

Personalised recommendations