Optimization of resource leveling problem under multiple objective criteria using a symbiotic organisms search


  • Doddy Prayogo Petra Christian University
  • Christianto Tirta Kusuma




resource leveling, optimization, metaheuristic algorithms, particle swarm optimization, symbiotic organisms search


Bad scheduling and resource management can cause delays or cost overruns. Optimization in solving resource leveling is necessary to avoid those problems. Several objective criteria are used to solve resource leveling. Each of them has the same objective, which is to reduce the fluctuation of resource demand of the project. This study compares the performance of particle swarm optimization (PSO) and symbiotic organisms search (SOS) in solving resource leveling problems using separate objective functions in order to find which one produces a better solution. The results show that SOS produced a better solution than PSO, and one objective function is better in solving resource leveling than the others.


Damci, A. and Polat, G., Impacts of Different Objective Functions on Resource Leveling in Construction Projects: A Case Study, Journal of Civil Engineering and Management, 20(4), 2014, pp. 537-547.

Leu, S.-S., Yang, C.-H., and Huang, J.-C., Resource Leveling in Construction by Genetic Algorithm-Based Optimization and Its Decision Support System Application, Automation in Construction, 10(1), 2000, pp. 27-41.

Son, J. and Skibniewski, M.J., Multiheuristic Approach for Resource Leveling Problem in Construction Engineering: Hybrid Approach, Journal of Construction Engineering and Management, 125(1), 1999, pp. 23-31.

Burgess, A.R. and Killebrew, J.B., Variation in Activity Level on a Cyclical Arrow Diagram, Journal of Industrial Engineering, 13(2), 1962, pp. 76-83.

Wagner, H.M., Giglio, R.J., and Glaser, R.G., Preventive Maintenance Scheduling by Mathematical Programming, Management Science, 10(2), 1964, pp. 316-334.

Hossein Hashemi Doulabi, S., Seifi, A., and Shariat Seyed, Y., Efficient Hybrid Genetic Algorithm for Resource Leveling Via Activity Splitting, Journal of Construction Engineering and Management, 137(2), 2011, pp. 137-146.

Easa Said, M., Resource Leveling in Construction by Optimization, Journal of Construction Engineering and Management, 115(2), 1989, pp. 302-316.

Hiyassat Mohammed, A.S., Applying Modified Minimum Moment Method to Multiple Resource Leveling, Journal of Construction Engineering and Management, 127(3), 2001, pp. 192-198.

Levy, F.K., Thompson, G.L., and Wiest, J.D., Multiship, Multishop, Workload-Smoothing Progfum, Naval Research Logistics Quarterly, 9(1), 1962, pp. 37-44.

Prayogo, D., Cheng, M.-Y., Wong, F.T., Tjandra, D., and Tran, D.-H., Optimization Model for Construction Project Resource Leveling Using a Novel Modified Symbiotic Organisms Search, Asian Journal of Civil Engineering, 19(5), 2018, pp. 625-638.

Geng, J.-Q., Weng, L.-P., and Liu, S.-H., An Improved Ant Colony Optimization Algorithm for Nonlinear Resource-Leveling Problems, Computers & Mathematics with Applications, 61(8), 2011, pp. 2300-2305.

Zhang, H. and Yang, Z., Accelerated Particle Swarm Optimization to Solve Large-Scale Network Plan Optimization of Resource-Leveling with a Fixed Duration Mathematical Problems in Engineering, 2018, 2018, pp. 11.

Cheng, M.-Y. and Prayogo, D., Symbiotic Organisms Search: A New Metaheuristic Optimization Algorithm, Computers & Structures, 139, 2014, pp. 98-112.

Prayogo, D., Cheng, M.-Y., and Prayogo, H., A Novel Implementation of Nature-Inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search, Civil Engineering Dimension, 19(1), 2017, pp. 36-43.

Prayogo, D. and Susanto, Y.T.T., Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine, Advances in Civil Engineering, 2018(6490169), 2018.

Prayogo, D., Sutanto, J.C., Suryo, H.E., and Eric, S., A Comparative Study on Bio-Inspired Algorithms in Layout Optimization of Construction Site Facilities, Civil Engineering Dimension, 20(2), 2018, pp. 102-110.

Ezugwu, A.E. and Prayogo, D., Symbiotic Organisms Search Algorithm: Theory, Recent Advances and Applications, Expert Systems with Applications, 119, 2019, pp. 184-209.

Tejani, G.G., Pholdee, N., Bureerat, S., Prayogo, D., and Gandomi, A.H., Structural Optimization Using Multi-Objective Modified Adaptive Symbiotic Organisms Search, Expert Systems with Applications, 125, 2019, pp. 425-441.

Hegazy, T., Optimization of Resource Allocation and Leveling Using Genetic Algorithms, Journal of Construction Engineering and Management, 125(3), 1999, pp. 167-175.

Kennedy, J. and Eberhart, R., Particle Swarm Optimization, Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.

Popescu, C., How to Use C.P.M in Practice, Part 2. Resources, Department of Civil Engineering, University of Texas at Austin, 1976.

Sears, K.S., Sears, G.A., and Clough, R.H., Construction Project Management, a Practical Guide to Field Construction Management, Publisher John Wiley & Sons, 2008, Inc.




How to Cite

Prayogo, D., & Kusuma, C. T. (2019). Optimization of resource leveling problem under multiple objective criteria using a symbiotic organisms search. Civil Engineering Dimension, 21(1), 43-51. https://doi.org/10.9744/ced.21.1.43-49