A Comparative Study on Bio-Inspired Algorithms in Layout Optimization of Construction Site Facilities

Authors

  • Doddy Prayogo Department of Civil Engineering, Faculty of Civil Engineering and Planning, Petra Christian University, Jalan Siwalankerto 121-131, Surabaya 60236. INDONESIA
  • Jessica Chandra Sutanto Department of Civil Engineering, Faculty of Civil Engineering and Planning, Petra Christian University, Jalan Siwalankerto 121-131, Surabaya 60236. INDONESIA
  • Hieronimus Enrico Suryo Department of Civil Engineering, Faculty of Civil Engineering and Planning, Petra Christian University, Jalan Siwalankerto 121-131, Surabaya 60236. INDONESIA
  • Samuel Eric Department of Civil Engineering, Faculty of Civil Engineering and Planning, Petra Christian University, Jalan Siwalankerto 121-131, Surabaya 60236. INDONESIA

:

https://doi.org/10.9744/ced.20.2.102-110

Keywords:

Site layout, optimization, bio-inspired algorithms, particle swarm optimization, artificial bee colony, symbiotic organisms search

Abstract

A good arrangement of site layout on a construction project is a fundamental component of the project’s efficiency. Optimization on site layout is necessary in order to reduce the transportation cost of resources or personnel between facilities. Recently, the use of bio-inspired algorithms has received considerable critical attention in solving the engineering optimization problem. These methods have consistently provided better performance than traditional mathematical-based methods to a variety of engineering problems. This study compares the performance of particle swarm optimization (PSO), artificial bee colony (ABC), and symbiotic organisms search (SOS) algorithms in optimizing site layout planning problems. Three real-world case studies of layout optimization problems have been used in this study. The results show that SOS has a better performance in comparison to the other algorithms. Thus, this study provides useful insights to construction practitioners in the industry who are involved in dealing with optimization problems

References

Tommelein, I., Levitt, R., and Hayes-Roth, B., Sight Plan Model for Site Layout, Journal of Construction Engineering and Management, 118(4), 1992, pp. 749-766.

Tate, D.M. and Smith, A.E., Unequal-Area Facility Layout by Genetic Search, IIE Transactions, 27(4), 1995, pp. 465-472.

Yeh, I.-C., Construction-Site Layout using Annealed Neural Network, Journal of Computing in Civil Engineering, 9(3), 1995, pp. 201-208.

Li, H. and Love, P.E.D., Genetic Search for Solving Construction Site-Level Unequal-Area Facility Layout Problems, Automation in Construction, 9(2), 2000, pp. 217-226.

Adrian, A.M., Utamima, A., and Wang, K.-J., A Comparative Study of GA, PSO and ACO for Solving Construction Site Layout Optimization, KSCE Journal of Civil Engineering, 19(3), 2015, pp. 520-527.

Yahya, M. and Saka, M.P., Construction Site Layout Planning using Multi-Objective Artificial Bee Colony Algorithm with Levy Flights, Automation in Construction, 38, 2014, pp. 14-29.

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.

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

Karaboga, D. and Basturk, B., A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm, Journal of Global Optimization, 39(3), 2007, pp. 459-471.

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

Cheng, M.-Y., Prayogo, D., and Tran, D.-H., Optimizing Multiple-Resources Leveling in Multiple Projects using Discrete Symbiotic Organisms Search, Journal of Computing in Civil Engineering, 30(3), 2016, pp. 04015036.

Tran, D.-H., Cheng, M.-Y., and Prayogo, D., A Novel Multiple Objective Symbiotic Organisms Search (MOSOS) for Time–Cost–Labor Utilization Tradeoff Problem, Knowledge-based Systems, 94, 2016, pp. 132-145.

Prayogo, D., Gosno, R.A., Evander, R., and Limanto, S., Implementasi Metode Metaheuristik Symbiotic Organisms Search dalam Penentuan Tata Letak Fasilitas Proyek Konstruksi Berdasarkan Jarak Tempuh Pekerja, Jurnal Teknik Industri, 19(2), 2018, pp. 103-114.

Prayogo, D., Metaheuristic-based Machine Learning System for Prediction of Compressive Strength Based on Concrete Mixture Properties and Early-Age Strength Test Results, Civil Engineering Dimension, 20(1), 2018, pp. 21-29.

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, 2018.

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.

Li, H. and Love, P.E.D., Site-Level Facilities Layout using Genetic Algorithms, Journal of Computing in Civil Engineering, 12(4), 1998, pp. 227-231.

Downloads

Published

2018-10-08

How to Cite

Prayogo, D., Sutanto, J. C., Suryo, H. E., & Eric, S. (2018). A Comparative Study on Bio-Inspired Algorithms in Layout Optimization of Construction Site Facilities. Civil Engineering Dimension, 20(2), 102-110. https://doi.org/10.9744/ced.20.2.102-110

Issue

Vol. 20 No. 2 (2018): SEPTEMBER 2018

Section

Articles

License

Authors who publish with this journal agree to the following terms:
  1. Authors retain the copyright and publishing right, and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) followingthe publication of the article, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).