DOI: https://doi.org/10.9744/jti.19.2.103-114

Implementasi Metode Metaheuristik Symbiotic Organisms Search Dalam Penentuan Tata Letak Fasilitas Proyek Konstruksi Berdasarkan Jarak Tempuh Pekerja

Doddy Prayogo, Richard Antoni Gosno, Richard Evander, Sentosa Limanto

Abstract


Penelitian ini menyelidiki performa dari metode metaheuristik baru bernama symbiotic organisms search (SOS) dalam menentukan tata letak fasilitas proyek konstruksi yang optimal berdasarkan jarak tempuh pekerja. Dua buah studi kasus tata letak fasilitas digunakan untuk menguji akurasi dan konsistensi dari SOS. Sebagai tambahan, tiga metode metaheuristik lainnya, yaitu particle swarm optimization, artificial bee colony, dan teaching–learning-based optimization, digunakan sebagai pembanding terhadap algoritma SOS. Hasil simulasi mengindikasikan bahwa algoritma SOS lebih unggul serta memiliki karakteristik untuk menghasilkan titik konvergen lebih cepat jika dibandingkan dengan metode metaheuristik lainnya dalam proses optimasi tata letak fasilitas proyek konstruksi.


Keywords


tata letak fasilitas; optimasi; metaheuristik; symbiotic organisms search

Full Text:

PDF

References


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.

Tommelein, I., Levitt, R., and Hayes-Roth, B., SightPlan model for site layout, Journal of Construction Engineering and Management, 118(4), 1992, pp. 749-766.

Simmons, D.M., Single row space allocation: An ordering algorithm, Operations Research, 17(5), 1969, pp. 812–826.

Picard, J.-C. and Queyranne, M., On the One-Dimensional Space Allocation Problem, Operations Research, 29(2), 1981, pp. 371-391.

Love, R.F. and Wong, J.Y., On solving a single row space allocation problem with integer programming, INFOR, 14, 1976, pp. 139-143.

Ravi Kumar, K., Hadjinicola, G.C., and Lin, T.-l., A heuristic procedure for the single-row facility layout problem, European Journal of Operational Research, 87(1), 1995, pp. 65-73.

Holland, J.H., Adaptation in natural and artificial systems, University of Michigan Press, 1975.

Kennedy, J. and Eberhart, R. Particle swarm optimization. in Proceedings of IEEE International Conference on Neural Networks, 1995.

Dorigo, M., Maniezzo, V., and Colorni, A., Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 1996, pp. 29-41.

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.

Zouein, P., Harmanani, H., and Hajar, A., Genetic Algorithm for Solving Site Layout Problem with Unequal-Size and Constrained Facilities, Journal of Computing in Civil Engineering, 16(2), 2002, pp. 143-151.

Lee, K.-Y., Roh, M.-I., and Jeong, H.-S., An improved genetic algorithm for multi-floor facility layout problems having inner structure walls and passages, Computers & Operations Research, 32(4), 2005, pp. 879-899.

Önüt, S., Tuzkaya, U.R., and Doğaç, B., A particle swarm optimization algorithm for the multiple-level warehouse layout design problem, Computers & Industrial Engineering, 54(4), 2008, pp. 783-799.

Zhang, H. and Wang, J.Y., Particle Swarm Optimization for Construction Site Unequal-Area Layout, Journal of Construction Engineering and Management, 134(9), 2008, pp. 739-748.

Lam, K.C., Ning, X., and Ng, T., The application of the ant colony optimization algorithm to the construction site layout planning problem, Construction Management and Economics, 25(4), 2007, pp. 359-374.

Liang, L.Y. and Chao, W.C., The strategies of tabu search technique for facility layout optimization, Automation in Construction, 17(6), 2008, pp. 657-669.

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.

Setiawan, I.L. and Palit, H.C., Perbandingan Kombinasi Genetic Algorithm–Simulated Annealing dengan Particle Swarm Optimization pada Permasalahan Tata Letak Fasilitas, Jurnal Teknik Industri, 12(2), 2010, pp. pp. 119-124.

Prayogo, D., An Innovative Parameter-Free Symbiotic Organisms Search (SOS) for Solving Construction-Engineering Problems, 2015, PhD thesis, Department of Construction Engineering, National Taiwan University of Science and Technology.

Cheng, M.-Y., Firdausi, P.M., and Prayogo, D., High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT), Engineering Applications of Artificial Intelligence, 29, 2014, pp. 104-113.

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.

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.

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.

Cheng, M.-Y., Chiu, C.-K., Chiu, Y.-F., Wu, Y.-W., Syu, Z.-L., Prayogo, D., and Lin, C.-H., SOS optimization model for bridge life cycle risk evaluation and maintenance strategies, Journal of the Chinese Institute of Civil and Hydraulic Engineering, 26(4), 2014, pp. 293-308.

Cheng, M.-Y. and Prayogo, D. Modeling the permanent deformation behavior of asphalt mixtures using a novel hybrid computational intelligence. in ISARC 2016 - 33rd International Symposium on Automation and Robotics in Construction, 2016, Auburn, USA, International Association for Automation and Robotics in Construction.

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.

Rao, R.V., Savsani, V.J., and Vakharia, D.P., Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems, Computer-Aided Design, 43(3), 2011, pp. 303-315.




DOI: https://doi.org/10.9744/jti.19.2.103-114



Instructions for Preparing Papers for JTI.docx
Panduan untuk Menulis di JTI.docx

The Journal is published by The Institute of Research & Community Outreach - Petra Christian University. It available online supported by Directorate General of Higher Education - Ministry of National Education - Republic of Indonesia.

©All right reserved 2016.Jurnal Teknik Industri, ISSN: 1411-2485, e-ISSN: 2087-7439

shopify traffic stats
View My Stats