TY  - JOUR
T1  - INFLUENCE OF JUMPING RATE ON OPPOSITION-BASED JAYA ALGORITHM FOR DISCRETE TIME COST TRADE-OFF OPTIMIZATION PROBLEMS
TT  - Sıçrama Oranının Ayrık Zaman Maliyeti Ödünleşim Optimizasyonu Problemleri için Karşıtlık Tabanlı JAYA Algoritması Üzerindeki Etkisi
AU  - Eirgash, Mohammad Azim
PY  - 2025
DA  - April
Y2  - 2025
DO  - 10.17482/uumfd.1561366
JF  - Uludağ Üniversitesi Mühendislik Fakültesi Dergisi
JO  - UUJFE
PB  - Bursa Uludağ University
WT  - DergiPark
SN  - 2148-4155
SP  - 35
EP  - 50
VL  - 30
IS  - 1
LA  - en
AB  - This paper aims to develop a new multi-objective optimization algorithm for handling construction time-cost trade-off problems (TCTPs). An intelligent strategy called opposition-based learning (OBL) is incorporated into the Jaya algorithm, resulting in the opposition-based Jaya Algorithm (OBJA). The proposed model introduces an innovative approach to opposition-based optimization by employing an iterative-based varying oppositional jumping rates. This adaptive strategy significantly contributes to increased population diversity and effective avoidance of local optima throughout both the initialization and generational phases of the optimization process. By systematically varying the opposition jumping rate, its impact on the algorithm's convergence speed, solution quality, and computational efficiency are evaluated. The experimental results demonstrate that an iterative-based varying opposition jumping rate significantly enhances OBJA's efficiency to explore and exploit the search space, leading to superior tradeoff solutions. Hence, computational experiments on 9 and 19 activity problems reveal that an iterativebased varying opposition jumping rate result in high quality solution with reduced number of function evaluations. Furthermore, the OBJA model proved to be more successful than the non-dominated sorting GA (NSGA-II), multi-objective particle swarm optimzaiton (MOPSO), and plain Jaya algorithm for handling these complex TCTPs in construction project management.
KW  - Time-cost trade-off problem
KW  - Jaya algorithm
KW  - Opposition-based learning
KW  - Iterative-based varying opposition jumping rate.
N2  - Bu makale, inşaat sektörünün zaman-maliyet ödünleşim problemlerini (ZMÖP) çözmek için yeni bir çok amaçlı optimizasyon modeli geliştirmeyi amaçlamaktadır. Jaya algoritmasına karşıt tabanlı öğrenme (OBL) adı verilen akıllı bir strateji eklenmiş ve sonuç olarak karşıt tabanlı Jaya Algoritması (OBJA) önerilmiştir. OBL, popülasyonun daha iyi başlatılması ve popülasyonun yerel optimuma düşmemesi için nesil sıçrama oranı uygulanmaktadır. Önerilen model, iteratif tabanlı değişken karşıtlık sıçrama oranlarını kullanarak karşıt tabanlı optimizasyona yenilikçi bir yaklaşım sunmaktadır. Bu uyarlamalı strateji, optimizasyon sürecinin hem başlatma hem de nesil aşamalarında popülasyon çeşitliliğini artırmaya ve yerel optimal noktalardan etkili bir şekilde kaçınmaya önemli ölçüde katkıda bulunmaktadır. Karşıt sıçrama oranı sistematik olarak değiştirilerek algoritmanın yakınsama hızı, çözüm kalitesi ve hesaplama verimliliği üzerindeki etkisi değerlendirilmiştir. Deneysel sonuçlar, iteratif tabanlı değişken karşıt sıçrama oranının, OBJA'nın arama alanını arama ve araştırma yeteneğini önemli ölçüde artırarak üstün dengeleme çözümlerine yol açtığını göstermektedir. Bu nedenle, 9 ve 19 aktivite problemine yönelik hesaplamalı deneyler, iteratif tabanlı değişken karşıt sıçrama oranının, daha az fonksiyon değerlendirmesi ile yüksek kaliteli çözümler elde edilmesine neden olduğunu ortaya koymaktadır. Ayrıca, OBJA algoritması, bu karmaşık zaman-maliyet ödünleşim optimizasyon problemlerini yapı proje yönetiminde ele alırken NSGAII, MOPSO ve basit Jaya algoritmasından daha başarılı olduğunu kanıtlamıştır.
CR  - Abualigah., L, Diabat., A, Mirjalili., S, Elaziz., M.A, Gandomi., A.H. (2021). The arithmetic optimization algorithm. Computer Methods in Applied mechanics and Engineering, 376, 113609. https://doi.org/10.1016/j.cma.2020.113609
CR  - Agarwal., A.K., Chauhan., S.S., Sharma., K. (2024), “Development of time–cost trade-off optimization model for construction projects with MOPSO technique”, Asian Journal Civil Engineering. https://doi.org/10.1007/s42107-024-01063-3
CR  - Albayrak, G. (2020). “Novel hybrid method in time–cost trade-off or resource-constrained construction projects”. Iranian Journal of Science and  Technology, Transactions of Civil Engineering, 44 (4), 1295-1307. https://doi.org/10.1007/s40996-020-00437-2
CR  - Aminbakhsh, S., and Sönmez, R. (2016). “Applied discrete particle swarm optimization method for the large-scale discrete time–cost trade-off problem.” Expert System with Applications, Vol. 51, 177-185. https://doi.org/10.1016/j.eswa.2015.12.041
CR  - Banihashemi., S.A. and Khalilzadeh, M. (2020), "Time-cost-quality-environmental impact trade-off resource-constrained project scheduling problem with DEA approach", Engineering, Construction and Architectural Management, 28 (7),1979-2004. https://doi.org/10.1108/ECAM-05-0350
CR  - Banihashemi., S.A.; Khalilzadeh, M.; Zavadskas, E.K.; Antucheviciene, J. (2021) Investigating the Environmental Impacts of Construction Projects in Time-Cost Trade-Off Project Scheduling Problems with CoCoSo Multi-Criteria Decision-Making Method. Sustainability, 13, 10922.  https://doi.org/10.3390/su131910922
CR  - Bettemir., Ö.H., Birgönül, T. (2017). Network analysis algorithm for the solution of discrete time-cost trade-off problem. KSCE Journal of Civil Engineering, 21, 1047–1058. https://doi.org/10.1007/s12205-016-1615-x
CR  - Bettemir, Ö.H. and Birgonul, M.T. (2023), “Solution of discrete time–cost trade-off problem with adaptive search domain”, Engineering, Construction and Architectural Management, 0969-9988. https://www.emerald.com/insight/0969-9988.htm
CR  - Bettemir.,Ö.H., Yücel. T (2023), “Simplified Solution of Time-Cost Trade-off Problem for Building Constructions by Linear Scheduling”, Jordon Journal of Civil Engineering, 17(2), 293–309. https://doi.org/10.14525/jjce.v17i2.10
CR  - 1Bettemir., Ö.H., & Yücel, T. (2021). zaman maliyet ödünleşim probleminin en az insan müdahalesi ile oluşturulup çözülmesi. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 26(2), 461-480. https://doi.org/10.17482/uumfd.869234
CR  - Bhoi, A. K., Kumar, P., & Rout, B. K. (2019). An efficient optimization approach for manufacturing system using Jaya algorithm. Materials Today: Proceedings, 18, 3209-3216.
CR  - Deb., K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). “A fast and elitist multio-bjective genetic algorithm: NSGA-II.” IEEE Transaction and Evolution Computing, 6(2), 182–197. DOI: 10.1109/4235.996017
CR  - Eirgash., M.A, Dede., T. (2018). A multi-objective improved teaching learning-based optimization algorithm for time-cost trade-off problems, Journal of Construction Engineering and Management Innovation 1(3):118-128. 10.31462/jcemi.2018.03118128
CR  - Eirgash, M. A., Toğan, V., Trivedi, MK.; Sharma, K., (2022). Modified Oppositional Teaching-Learning-Based Optimization Model for Solving Construction Time-Cost-Quality Trade-Off Problems, 7th International Project and Construction Management Conference, 100-112, Yildiz Technical University, Istanbul.
CR  - Eirgash,. M.A, and Toğan,V. (2024). A dual opposition learning-based multi-objective Aquila Optimizer for trading-off time-cost-quality-CO2 emissions of generalized construction projects, Engineering Computations.,729648, https://doi.org/10.1108/EC-01-2024-0043
CR  - Eirgash., M.A, V. Toğan., T. Dede., H.B. Başağa. (2023). Modified Dynamic Opposite Learning assisted TLBO for solving Time-Cost optimization in Generalized Construction Projects, Structures, 53(1), 608-621. doi.org/10.1016/j.istruc.2023.04.091
CR  - Eirgash., M.A., Toğan,V. (2023). A Novel Oppositional Teaching Learning Strategy Based on the Golden Ratio to Solve the Time-Cost-Environmental Impact Trade-Off Optimization Problems, Expert System with Applications, 119995. doi.org/10.1016/j.istruc.2023.04.091
CR  - El-Rayes K, and Kandil., A. (2005). Time–cost–quality trade-off analysis for highway construction. Journal of Construction Engineering and Management. 131(4):477–48610.1061/(ASCE)0733-9364(2005)131:4(477)
CR  - Feng, C.W., Liu, L., Burns, S.A. (1997). using genetic algorithms to solve construction time-cost trade-off problems. Journal of Computing in Civil Engineering 11(3), 184 –189. https://doi.org/10.1061/(ASCE)0887-3801(1997)11:3(184)
CR  - Ghoddousi, P., Eshtehardian, E., Jooybanpour, S. and Javanmardi, A. (2013), “Multi-mode resource-constrained discrete time–cost-resource optimization in project scheduling using non-dominated sorting genetic algorithm”, Automation in Construction, 30, 216-227. https://doi.org/10.1016/j.autcon.2012.11.014
CR  - Huynh., V.H, Nguyen., T.H, Pham., H.C, Huynh,T.M.D, Ngu-yen.T.C, Tran. D.H. (2020), Multiple Objective Social Group Optimization for Time–Cost–Quality– Carbon Dioxide in Generalized Construction Projects, International Journal of Civil Engineering https://doi.org/10.1007/s40999-020-00581-w
CR  - Kaveh, A., & Dadras, A. (2017). A novel meta-heuristic optimization algorithm: Thermal exchange optimization. Advances in Engineering Software, 110, 69-84. https://doi.org/10.1016/j.advengsoft.2017.03.014
CR  - Kumar., K.M., Agrawal, D., Vishwakarma, V.K. Eirgash., M.A (2024). Development of time-cost trade-off optimization model for Indian highway construction projects using non-dominated sorting genetic algorithm-II methodology. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-024-01157-y
CR  - Mahdavi., S, Rahnamayan., S, Deb., K. (2018). “Opposition based learning: A literature review” Swarm and Evolutionary Computation., 39:1–23. https://doi.org/10.1016/j.swevo.2017.09.010
CR  - Mahdavi-Roshan., P, Mousavi, S.M. (2022). "A new interval-valued fuzzy multi-objective approach for project time–cost–quality trade-off problem with activity crashing and overlapping under uncertainty", Kybernetes, doi.org/10.1108/K-11-2021-1217
CR  - Ozcan-Deniz., G, Zhu., Y, Ceron., V. (2012). Time, cost, and environmental impact analysis on construction operation optimization using genetic algorithms. Journal of Management and Engineering, 28 (3), 265–272.5https://doi.org/10.1061/(ASCE)ME.1943-5479.00000
CR  - Panwar. A, Jha. N.K, (2021). Integrating Quality and Safety in Construction Scheduling Time-Cost Trade-Off Model, Journal of Construction and Engineering Management, 147 (2). doi.org/10.1061/(ASCE)CO.1943-7862.0001979
CR  - Pham, V.H.S., Nguyen Dang, N.T. & Nguyen, V.N. (2024). “Achieving improved performance in construction projects: advanced time and cost optimization framework”. Evolutionary Intelligence, . https://doi.org/10.1007/s12065-024-00918-7
CR  - Rahnamayan., S., Tizhoosh., H.R., & Salama., M.M.A. (2007). “Quasi-oppositional differential evolution. In Proceedings of IEEE Congress on Evolutionary Computation.” Singapore, 25–28, (22229) 2236. doi.org/10.1109/CEC.2007.4424748.
CR  - Rao, R. V. (2016). ‘‘Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems,’’ International Journal of Industrial Engineering Computations., 7, 1, 19–34. doi: 10.5267/j.ijiec.2015.8.004
CR  - Rao. R, Savsani. V, Vakharia. D. (2011). “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems,” Computer-Aided Design, 43 (3), 303–315. doi.org/10.1016/j.cad.2010.12.015.
CR  - Sheikh., M.A, Kumar., C, (2020). Tıme, cost and quality trade-off analysıs in constructıon projects of international research, journal of engineering and technology (ırjet) 7, 9.
CR  - Sheikholeslami, R., Talatahari, S., & Gandomi, A. H. (2017). Structural optimization using the Jaya algorithm. Structural and Multidisciplinary Optimization, 55(2), 697-716.
CR  - Tiwari., A, Trivedi., M, & Sharma., K, (2020). NSGA III based Time-Cost-Environmental Impact Trade-Off Optimization model for Construction Projects, Second International Conference on Sustainable and Innovative Solutions for Current Challenges in Engineering & Technology. doi : 10.1007/978-981-16-1220-6_2
CR  - Toğan., V, & Eirgash, M.A, (2019). “Time-Cost Trade-off Optimization of Construction Projects Using Teaching Learning Based Optimization”, KSCE Journal of Civil Engineering, 23(1), 10-20. doi.org/10.1007/s12205-018-1670-6
CR  - Tran., D.-H. & Tarigan.,P.B. (2022), "Time Cost Quality Trade-Off in Repetitive Construction Project for Sustainable Construction Project",  Sustainability Management Strategies and Impact in Developing Countries , (26), 75-85. . https://doi.org/10.1108/S2040-726220220000026007
CR  - Tran., D-H, Luong-D.L, Duong. M.T, Le. T.N, & Pham. A,D, (2018), Opposition multiple objective symbiotic organisms search (OMOSOS) for time, cost, quality and work conti-nuity, Journal of Computational Design and Engineering, 5, 2, 160–172, https://doi.org/10.1016/j.jcde.2017.11.008
CR  - Vanhoucke, M., Debels, D. (2007). The discrete time/cost trade-off problem: extensions and heuristic procedures. Journal of Scheduling, 10(5), 311-326. https://doi.org/10.1007/s10951-007-0031-y
CR  - Zhao, L., Xu, Q. and Pan, J. (2013), “Influence of jumping rate on opposition-based differential evolution using the current optimum”, Information Technology Journal, 12,  959-966. DOI: 10.3923/itj.2013.959.966
CR  - Zheng, D. X. M., Ng, S. T. and Kumaraswamy, M. M.(2004). Applying a Genetic Algorithm-Based Multi-objective Approach for Time-Cost Optimization, Journal of Construction Engineering and Management,  ASCE, 130, 168-176. DOI: 10.1061/(ASCE)0733-9364(2004)130:2(168)
CR  - Zheng. H, (2016). The Bi-Level Optimization Research for Time-Cost-Quality-Environment Trade-off Scheduling Problem and Its Application to a Construction Project, Proceedings of the 10th International Conference on Management Science and Engineering Management, 745–753. http://dx.doi.org/10.1007/978-981-10-1837-4_62
UR  - https://doi.org/10.17482/uumfd.1561366
L1  - https://dergipark.org.tr/en/download/article-file/4263695
ER  -