Optimizing Shipbuilding Production Project Scheduling Under Resource Constraints Using Genetic Algorithms and Fuzzy Sets

Year 2023, Volume: 12 Issue: 3, 380 - 401, 28.09.2023

Ercan Akan , Güler Alkan

https://doi.org/10.33714/masteb.1324266

Abstract

This study explores the application of Genetic Algorithms (GA) in optimizing shipbuilding production processes in the presence of uncertain environments. The research addresses two key aspects: firstly, the integration of GA RCPSP (Resource-Constrained Project Scheduling Problem) with techniques for managing uncertainty in shipbuilding production; and secondly, the analysis of Pareto optimal solutions generated by GA to achieve optimal scheduling in the shipbuilding context. The proposed framework aims to minimize project completion time and maximize resource utilization by incorporating probabilistic models, scenario analysis to handle uncertainties. Furthermore, the study focuses on evaluating the trade-offs between project completion time, resource allocation, and cost through the analysis of Pareto optimal solutions, using visualization techniques and sensitivity analyses to support decision-making processes. The findings contribute to enhancing shipbuilding production by providing a comprehensive approach for effectively managing uncertainty, improving resource allocation, and reducing project duration through the integration of GA RCPSP and uncertainty management techniques.

Keywords

Shipbuilding production, Project management (PM), Resource constraint project scheduling problem (RCPSP), Genetic algorithms (GA), Fuzzy sets (FS)

Thanks

This article was prepared based on the doctoral thesis entitled “Model of Ship Production Management in Shipyard: A Case Study in Marmara Region” completed by the first author in the “Maritime Transportation Management Engineering” PhD Program at Institute of Graduate Studies in Science, İstanbul University.

References

• Abbasi, B., Shadrokh, S., & Arkat, J. (2006). Bi-objective resource-constrained project scheduling with robustness and makespan criteria. Applied mathematics and Computation, 180(1), 146-152. https://doi.org/10.1016/j.amc.2005.11.160
• Adhau, S., Mittal, M. L., & Mittal, A. (2012). A multi-agent system for distributed multi-project scheduling: An auction-based negotiation approach. Engineering Applications of Artificial Intelligence, 25(8), 1738-1751. https://doi.org/10.1016/j.engappai.2011.12.003
• Adhau, S., Mittal, M. L., & Mittal, A. (2013). A multi-agent system for decentralized multi-project scheduling with resource transfers. International Journal of Production Economics, 146(2), 646-661. https://doi.org/10.1016/j.ijpe.2013.08.013
• Afshar-Nadjafi, B., Rahimi, A., & Karimi, H. (2013). A genetic algorithm for mode identity and the resource constrained project scheduling problem. Scientia Iranica, 20(3), 824-831. https://doi.org/10.1016/j.scient.2012.11.011
• Akan, E., & Bayar, S. (2022). Interval type-2 fuzzy program evaluation and review technique for project management in shipbuilding, Ships and Offshore Structures, 17(8), 1872-1890, https://doi.org/10.1080/17445302.2021.1950350
• Akan, E. (2023). A holistic analysis of maritime logistics process in fuzzy environment in terms of business process management. Business Process Management Journal, 29(4), 1116-1158. https://doi.org/10.1108/BPMJ-08-2022-0368
• Akan, E., (2017). Tersanelerde Gemi Üretim Yönetimi Modeli: Marmara Bölgesinde Bir Uygulama. [PhD Thesis. İstanbul University].
• Akhbari, M. (2022). Integration of multi-mode resource-constrained project scheduling under bonus-penalty policies with material ordering under quantity discount scheme for minimizing project cost. Scientia Iranica, 29(1), 427-446. https://doi.org/10.24200/sci.2020.54286.3680
• Alcaraz, J., & Maroto, C. (2001). A robust genetic algorithm for resource allocation in project scheduling. Annals of operations Research, 102, 83-109. https://doi.org/10.1023/A:1010949931021
• Aramesh, S., Aickelin, U., & Akbarzadeh Khorshidi, H. (2022). A hybrid projection method for resource-constrained project scheduling problem under uncertainty. Neural Computing and Applications, 34(17), 14557-14576. https://doi.org/10.1007/s00521-022-07321-2
• Asadujjaman, M., Rahman, H. F., Chakrabortty, R. K., & Ryan, M. J. (2021). An immune genetic algorithm for solving NPV-based resource constrained project scheduling problem. IEEE Access, 9, 26177-26195. https://doi.org/10.1109/ACCESS.2021.3057366
• Aziz, R. F. (2013). Optimizing strategy software for repetitive construction projects within multi-mode resources. Alexandria Engineering Journal, 52(3), 373-385. https://doi.org/10.1016/j.aej.2013.04.002
• Back, M. G., Lee, D. K., Shin, J. G., & Woo, J. H. (2016). A study for production simulation model generation system based on data model at a shipyard. International Journal of Naval Architecture and Ocean Engineering, 8, 496e510. https://doi.org/10.1016/j.ijnaoe.2016.05.005
• Bhaskar, T., Pal, M. N., & Pal, A. K. (2011). A heuristic method for RCPSP with fuzzy activity times. European Journal of Operational Research, 208(1), 57-66. https://doi.org/10.1016/j.ejor.2010.07.021
• Bianco, L., & Caramia, M. (2012). An exact algorithm to minimize the makespan in project scheduling with scarce resources and generalized precedence relations. European Journal of Operational Research, 219(1), 73-85. https://doi.org/10.1016/j.ejor.2011.12.019.
• Birjandi, A., & Mousavi, S. M. (2019). Fuzzy resource-constrained project scheduling with multiple routes: A heuristic solution. Automation in Construction, 100, 84-102. https://doi.org/10.1016/j.autcon.2018.11.029
• Birjandi, A., Mousavi, S. M., Hajirezaie, M., & Vahdani, B. (2019). A new weighted mixed integer nonlinear model and FPND solution algorithm for RCPSP with multi-route work packages under fuzzy uncertainty. Journal of Intelligent & Fuzzy Systems, 37(1), 737-751. https://doi.org/10.3233/JIFS-181293
• Blazewicz, J., Lenstra, J. K., & Rinnooy Kan, A. H. G. (1983). Scheduling subject to resource constraints: Classification and complexity. Discrete Applied Mathematics, 5, 11-22. https://doi.org/10.1016/0166-218X(83)90012-4
• Boctor, F. F. (1996). A new and efficient heuristic for scheduling projects with resource restrictions and multiple execution modes. European Journal of Operational Research, 90(2), 349-361. https://doi.org/10.1016/0377-2217(95)00359-2
• Brucker, P., & Knust, S. (2000). A linear programming and constraint propagation-based lower bound for the RCPSP. European Journal of Operational Research, 127(2), 355-362. https://doi.org/10.1016/S0377-2217(99)00489-0
• Çebi, F., & Otay, İ. (2015). A fuzzy multi-objective model for solving project network problem with bonus and incremental penalty cost. Computers & Industrial Engineering, 82, 143-150. https://doi.org/10.1016/j.cie.2015.01.007
• Cha, J. H., & Roh, M. I. (2010). Combined discrete event and discrete time simulation framework and its application to the block erection process in shipbuilding. Advances in Engineering Software, 41, 656-665.
• Chaleshtarti, A. S., Shadrokh, S., Khakifirooz, M., Fathi, M., & Pardalos, P. M. (2020). A hybrid genetic and Lagrangian relaxation algorithm for resource-constrained project scheduling under nonrenewable resources. Applied Soft Computing, 94, 106482. https://doi.org/10.1016/j.asoc.2020.106482
• Chand, S., Huynh, Q., Singh, H., Ray, T., & Wagner, M. (2018). On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems. Information Sciences, 432, 146-163. https://doi.org/10.1016/j.ins.2017.12.013
• Chand, S., Singh, H., & Ray, T. (2019). Evolving heuristics for the resource constrained project scheduling problem with dynamic resource disruptions. Swarm and Evolutionary Computation, 44, 897-912. https://doi.org/10.1016/j.swevo.2018.09.007
• Chang, C. K., Jiang, H. Y., Di, Y., Zhu, D., & Ge, Y. (2008). Time-line based model for software project scheduling with genetic algorithms. Information and Software Technology, 50(11), 1142-1154. https://doi.org/10.1016/j.infsof.2008.03.002
• Changchun, L., Xi, X., Canrong, Z., Qiang, W., & Li, Z. (2018). A column generation based distributed scheduling algorithm for multi-mode resource constrained project scheduling problem. Computers & Industrial Engineering. 125, 258-278. https://doi.org/10.1016/j.cie.2018.08.036
• Cheng, M. Y., Tran, D. H., & Wu, Y. W. (2014). Using a fuzzy clustering chaotic-based differential evolution with serial method to solve resource-constrained project scheduling problems. Automation in Construction, 37, 88-97. https://doi.org/10.1016/j.autcon.2013.10.002
• Cho, K. K., & Chung, D. S. (1996). An automatic process-planning system for block assembly in shipbuilding. ClRP Annals, 45(1), 41-46. https://doi.org/10.1016/S0007-8506(07)63013-3
• Cho, Y. I., Nam, S. H., Cho, K. Y., Yoon, H. C., & Woo, J. H. (2022). Minimize makespan of permutation flowshop using pointer network. Journal of Computational Design and Engineering, 9(1), 51-67. https://doi.org/10.1093/jcde/qwab068
• Coelho, J., & Tavares. L. (2003). Comparative analysis of meta–heuristics for the resource constrained project scheduling problem. Technical report, Department of Civil Engineering, Instituto Superior Tecnico, Portugal.
• Coelho, J., & Vanhoucke, M. (2023). New resource-constrained project scheduling instances for testing (meta-) heuristic scheduling algorithms. Computers & Operations Research, 153, 106165. https://doi.org/10.1016/j.cor.2023.106165
• Coley, D. A. (1999). An introduction to Genetic Algorithms for Scientists and Engineers. World Scientific.
• Devikamalam, J., & Jane Helena, H. (2013). Resource scheduling of construction projects using genetic algorithm, International Journal of Advanced Engineering Technology, 4(3), 113-119.
• Dong, F., Deglise-Hawkinson, J. R., Van Oyen, M. P., & Singer, D. J. (2016). Dynamic control of a closed two-stage queueing network for outfitting process in shipbuilding. Computers & Operations Research, 72, 1-11. https://doi.org/10.1016/j.cor.2015.05.002
• Dridi, O., Krichen, S., & Guitouni, A. (2014). A multiobjective hybrid ant colony optimization approach applied to the assignment and scheduling problem. International Transactions in Operational Research, 21(6), 935-953. https://doi.org/10.1111/itor.12071
• Ecorys. (2009). Study on competitiveness of the European shipbuilding industry: Within the framework contract of sectoral competitiveness studies – ENTR/06/054. Final report.
• Etgar, R., Gelbard, R., & Cohen, Y. (2018). Feature assignment in multi-release work plan: accelerating optimization using gene clustering. Computers & Industrial Engineering, 118, 123-137. https://doi.org/10.1016/j.cie.2018.02.036
• Formentini, M., & Romano, P. (2011). Using value analysis to support knowledge transfer in the multi-project setting. International. Journal of Production Economics, 131, 545–560. https://doi.org/10.1016/j.ijpe.2011.01.023
• Franco, E. D., Zurita, F. L. Z., & Delgadillo, G. M. (2007). A genetic algorithm for the resource constrained project scheduling problem (RCPSP). Revista Investigación & Desarrollo, 7(1), 39-50.
• García-Nieves, J. D., Ponz-Tienda, J. L., Ospina-Alvarado, A., & Bonilla-Palacios, M. (2019). Multipurpose linear programming optimization model for repetitive activities scheduling in construction projects. Automation in Construction, 105, 102799. https://doi.org/10.1016/j.autcon.2019.03.020
• García‐Nieves, J. D., Ponz‐Tienda, J. L., Salcedo‐Bernal, A., & Pellicer, E. (2018). The multimode resource‐constrained project scheduling problem for repetitive activities in construction projects. Computer‐Aided Civil and Infrastructure Engineering, 33(8), 655-671. https://doi.org/10.1111/mice.12356
• Ge, Y., & Wang, A. (2021). Spatial scheduling for irregularly shaped blocks in shipbuilding. Computers & Industrial Engineering, 152, 106985. https://doi.org/10.1016/j.cie.2020.106985
• Goldberg, D. A. (1989). Genetic Algorithms in search optimization and machine learning. Addison-Wesley Publishing.
• Goncalves, J. F., Mendes, J. J. M., & Resende, M. G. C. (2008). A genetic algorithm for the resource constrained multi-project scheduling problem. European Journal of Operational Research, 189, 1171–1190. https://doi.org/10.1016/j.ejor.2006.06.074
• Goo, B., Chung, H., & Han, S. (2019). Layered discrete event system specification for a ship production scheduling model. Simulation Modelling Practice and Theory, 96, 101934. https://doi.org/10.1016/j.simpat.2019.101934
• Guo, W., Vanhoucke, M., Coelho, J., & Luo, J. (2021). Automatic detection of the best performing priority rule for the resource-constrained project scheduling problem. Expert Systems with Applications, 167, 114116. https://doi.org/10.1016/j.eswa.2020.114116
• Hadžić, N. (2019). Analytical solution of the serial Bernoulli production line steady-state performance and its application in the shipbuilding process. International Journal of Production Research, 57(4), 1052-1065. https://doi.org/10.1080/00207543.2018.1500042
• Han, D., Yang, B., Li, J., Sun, M., Zhou, Q., & Wang, J. (2017). A three-layer parallel computing system for shipbuilding project scheduling optimization. Advances in Mechanical Engineering, 9(10), 1687814017723297. https://doi.org/10.1177/1687814017723297
• Hapke, M., & Slowinski, R. (1996). Fuzzy priority heuristics for project scheduling. Fuzzy sets and systems, 83(3), 291-299. https://doi.org/10.1016/0165-0114(95)00338-X
• Hartmann, S. (1998). A competitive genetic algorithm for resource‐constrained project scheduling. Naval Research Logistics (NRL), 45(7), 733-750. https://doi.org/10.1002/(SICI)1520-6750(199810)45:7%3C733::AID-NAV5%3E3.0.CO;2-C
• Hartmann, S. (2001). Project scheduling with multiple modes: a genetic algorithm. Annals of Operations Research, 102(1-4), 111-135. https://doi.org/10.1023/A:1010902015091
• Hartmann, S. (2002). A self‐adapting genetic algorithm for project scheduling under resource constraints. Naval Research Logistics (NRL), 49(5) 433-448. https://doi.org/10.1002/nav.10029
• Hartmann, S., & Kolisch, R. (2000). Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 127(2), 394-407. https://doi.org/10.1016/S0377-2217(99)00485-3
• Haupt, R. L., & Haupt, S. E. (2004). Pratical genetic algorithms. 2nd ed. Wiley-Interscience Publication.
• Hiekata, K., Yamato, H., & Tsujimoto, S. (2010). Ontology based knowledge extraction for shipyard fabrication workshop reports. Expert Systems with Applications, 37, 7380-7386. https://doi.org/10.1016/j.eswa.2010.04.031
• Hindi, K. S., Yang, H., & Fleszar, K. (2002). An evolutionary algorithm for resource-constrained project scheduling. IEEE Transactions on Evolutionary Computation, 6, 512-518. https://doi.org/10.1109/TEVC.2002.804914
• Holland, J. H. (1975). Adaptation in natural and artificial systems. The MIT Press.
• Homberger, J. (2007). A multi‐agent system for the decentralized resource‐constrained multi‐project scheduling problem. International Transactions in Operational Research, 14(6), 565-589. https://doi.org/10.1111/j.1475-3995.2007.00614.x
• Hu, S., Zhang, Z., Wang, S., Kao, Y., & Ito, T. (2019). A project scheduling problem with spatial resource constraints and a corresponding guided local search algorithm. Journal of the Operational Research Society, 70(8), 1349–1361. https://doi.org/10.1080/01605682.2018.1489340
• Hua, Z., Liu, Z., Yang, L., & Yang, L. (2022). Improved genetic algorithm based on time windows decomposition for solving resource-constrained project scheduling problem. Automation in Construction, 142, 104503. https://doi.org/10.1016/j.autcon.2022.104503
• Huang, W., Oh, S. K., & Pedrycz, W. (2013). A fuzzy time-dependent project scheduling problem. Information Sciences, 246, 100-114. https://doi.org/10.1016/j.ins.2013.05.026
• Hwang, I. H., Kim, Y., Lee, D. G., & Shin, J. G. (2014). Automation of block assignment planning using a diagram-based scenario modeling method. International Journal of Naval Architect Ocean Engineering, 6, 162-174. https://doi.org/10.2478/IJNAOE-2013-0170
• Issa, S. B., Patterson, R. A., & Tu, Y. (2023). Solving resource-constrained project scheduling problems under different activity assumptions. Computers & Industrial Engineering, 180, 109170. https://doi.org/10.1016/j.cie.2023.109170
• Jeong, Y. K., Ju, S., Shen, H., Lee, D. K., Shin, J. G., & Ryu, C. (2018). An analysis of shipyard spatial arrangement planning problems and a spatial arrangement algorithm considering free space and unplaced block. The International Journal of Advanced Manufacturing Technology, 95, 4307-4325. https://doi.org/10.1007/s00170-017-1525-1
• Jiang, L., & Strandenes, S. P. (2012). Assessing the cost competitiveness of China’s shipbuilding industry. Maritime Economics & Logistics, 14, 480-497. https://doi.org/10.1057/mel.2012.17
• Joo, C. M., & Kim, B. S. (2014). Block transportation scheduling under delivery restriction in shipyard using meta-heuristic algorithms. Expert Systems with Applications, 41, 2851-2858. https://doi.org/10.1016/j.eswa.2013.10.020
• Kahraman, C., & Kaya, I. (2010). A fuzzy multicriteria methodology for selection among energy alternatives. Expert Systems with Applications, 37(9), 6270–6281. https://doi.org/10.1016/j.eswa.2010.02.095
• Khanzadi, M., Soufipour, R., & Rostami, M. (2011). A new improved genetic algorithm approach and a competitive heuristic method for large-scale multiple resource-constrained project-scheduling problems. International Journal of Industrial Engineering Computations, 2(4), 737-748. https://doi.org/10.5267/j.ijiec.2011.06.009
• Kim, H., Kang, J., & Park, S. (2002). Scheduling of shipyard block assembly process using constraint satisfaction problem. Asia Pacific Management Review, 7(1), 119-138.
• Kim, H., Lee, S. S., Park, J. H., & Lee, J. G. (2005). A model for a simulation-based shipbuilding system in a shipyard manufacturing process. International Journal of Computer Integrated Manufacturing, 18(6), 427-441. https://doi.org/10.1080/09511920500064789
• Kim, J. L. (2013). Genetic algorithm stopping criteria for optimization of construction resource scheduling problems. Construction Management and Economics, 31(1), 3-19. https://doi.org/10.1080/01446193.2012.697181
• Kim, K. W., Gen, M., & Yamazaki, G. (2003). Hybrid genetic algorithm with fuzzy logic for resource-constrained project scheduling. Applied Soft Computing, 2(3), 174-188. https://doi.org/10.1016/S1568-4946(02)00065-0
• Knyazeva, M., Bozhenyuk, A., & Rozenberg, I. (2015). Resource-constrained project scheduling approach under fuzzy conditions. Procedia Computer Science, 77, 56-64. https://doi.org/10.1016/j.procs.2015.12.359
• Kolisch, R., & Spracher A. (1996). PSPLIB - A project scheduling problem library. European Journal of Operational Research, 96, 205-216. https://doi.org/10.1016/S0377-2217(96)00170-1
• Kolisch, R. (1995). Project scheduling under resource constraints. Springer.
• Kwon, B., & Lee, G. M. (2015). Spatial scheduling for large assembly blocks in shipbuilding. Computers & Industrial Engineering, 89, 203–212. https://doi.org/10.1016/j.cie.2015.04.036
• Laszczyk, M., & Myszkowski, P. B. (2019). Improved selection in evolutionary multi–objective optimization of multi–skill resource–constrained project scheduling problem. Information Sciences, 481, 412-431. https://doi.org/10.1016/j.ins.2019.01.002
• Lee, J. K., Lee, K. J., Park, H. K., Hong, J. S., & Lee, J. S. (1997). Developing scheduling systems for Daewoo Shipbuilding: DAS project. European Journal of Operational Research, 97, 380-395. https://doi.org/10.1016/S0377-2217(96)00205-6
• Lee, Y. G., Ju, S., & Woo, J. H. (2020). Simulation-based planning system for shipbuilding. International Journal of Computer Integrated Manufacturing, 33(6), 626-641. https://doi.org/10.1080/0951192X.2020.1775304
• Li, H., Duan, J., & Zhang, Q. (2021a). Multi-objective integrated scheduling optimization of semi-combined marine crankshaft structure production workshop for green manufacturing. Transactions of the Institute of Measurement and Control, 43(3), 579-596. https://doi.org/10.1177/0142331220945917
• Li, F., Xu, Z., & Li, H. (2021b). A multi-agent based cooperative approach to decentralized multi-project scheduling and resource allocation. Computers & Industrial Engineering, 151, 106961. https://doi.org/10.1016/j.cie.2020.106961
• Li, J., Sun, M., Han, D., Wang, J., Mao, X., & Wu, X. (2019). A knowledge discovery and reuse method for time estimation in ship block manufacturing planning using DEA. Advanced Engineering Informatics, 39, 25-40. https://doi.org/10.1016/j.aei.2018.11.005
• Liu, J., Liu, Y., Shi, Y., & Li, J. (2020). Solving resource-constrained project scheduling problem via genetic algorithm. Journal of Computing in Civil Engineering, 34(2), 04019055. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000874
• Liu, Z., Chua, D. K. H., & Yeoh, K. W. (2011). Aggregate production planning for shipbuilding with variation-inventory trade-offs. International Journal of Production Research, 49(20), 6249-6272. https://doi.org/10.1080/00207543.2010.527388
• Ljubenkov, B., Dukic, G., & Kuzmanic, M. (2008). Simulation methods in shipbuilding process design. Strojniski Vestnik, 54(2), 131-139.
• Long, L. D., & Ohsato, A. (2008). Fuzzy critical chain method for project scheduling under resource constraints and uncertainty. International Journal of Project Management, 26(6), 688-698. https://doi.org/10.1016/j.ijproman.2007.09.012
• Magalhaes-Mendes, J. (2011). A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem. International Journal of Systems Applications, Engineering & Development, 3(5), 271-278.
• Mao, X., Li, J., Guo, H., & Wu, X. (2020). Research on collaborative planning and symmetric scheduling for parallel shipbuilding projects in the open distributed manufacturing environment. Symmetry, 12(1), 161. https://doi.org/10.3390/sym12010161
• Mei, Y., Ye, J., & Zeng, Z. (2016). Entropy-weighted ANP fuzzy comprehensive evaluation of interim product production schemes in one-of-a-kind production. Computers & Industrial Engineering, 100, 144-152. https://doi.org/10.1016/j.cie.2016.08.016
• Mitchell, M. (1999). An introduction to genetic algorithms. 5th ed. MIT Press.
• Montoya-Torres, J. R., Gutierrez-Franco, E., & Pirachicán-Mayorga, C. P. (2009). Project scheduling with limited resources using a genetic algorithm. International Journal of Project Management, 28, 619–628. https://doi.org/10.1016/j.ijproman.2009.10.003
• Muñoz, G., Espinoza, D., Goycoolea, M., Moreno, E., Queyranne, M., & Letelier, O. R. (2018). A study of the Bienstock–Zuckerberg algorithm: applications in mining and resource constrained project scheduling. Computational Optimization and Applications, 69, 501-534. https://doi.org/10.1007/s10589-017-9946-1
• Muritiba, A. E. F., Rodrigues, C. D., & da Costa, F. A. (2018). A path-relinking algorithm for the multi-mode resource-constrained project scheduling problem. Computers & Operations Research, 92, 145-154. https://doi.org/10.1016/j.cor.2018.01.001
• Myszkowski, P. B., & Laszczyk, M. (2022). Investigation of benchmark dataset for many-objective multi-skill resource constrained project scheduling problem. Applied Soft Computing, 127, 109253. https://doi.org/10.1016/j.asoc.2022.109253
• Okubo, Y., & Mitsuyuki, T. (2022). Ship production planning using shipbuilding system modeling and discrete time process simulation. Journal of Marine Science and Engineering, 10(2), 176. https://doi.org/10.3390/jmse10020176
• Özyiğit, İ., 2006, Gemi İnşaatında Planlama ve Üretim Kademeleri [MSc Thesis. Yıldız Teknik University].
• Palencia, A. E. R., & Delgadillo, G. E. M. (2012). A computer application for a bus body assembly line using Genetic Algorithms, International Journal Production Economics, 140(1), 431-438. https://doi.org/10.1016/j.ijpe.2012.06.026
• Park, C., & Seo, J. (2010). Comparing heuristic algorithms of the planar storage location assignment problem. Transportation Research Part E: Logistics and Transportation Review, 46, 171–185. https://doi.org/10.1016/j.tre.2009.07.004
• Park, C., Chung, K. H., Park, J. C., Cho, K. K., Baek, T. H., & Son, E. I. (2002). A spatial scheduling application at the block paint shop in shipbuilding: The HYPOS project. Production Planning & Control, 13(4), 342-354. https://doi.org/10.1080/095372802760108309
• Park, J., Lee, D., & Zhu, J. (2014). An integrated approach for ship block manufacturing process performance evaluation: Case from a Korean shipbuilding company. International. Journal of Production Economics, 156, 214–222. https://doi.org/10.1016/j.ijpe.2014.06.012
• Ponz-Tienda, J. L., Yepes, V., Pellicer, E., & Moreno-Flores, J. (2012). The resource leveling problem with multiple resources using an adaptive genetic algorithm. Automation in Construction, 29, 161–172. https://doi.org/10.1016/j.autcon.2012.10.003
• Rahman, H. F., Servranckx, T., Chakrabortty, R. K., Vanhoucke, M., & El Sawah, S. (2022). Manufacturing project scheduling considering human factors to minimize total cost and carbon footprints. Applied Soft Computing, 131, 109764. https://doi.org/10.1016/j.asoc.2022.109764
• Ranjbar, M. R., & Kianfar, F. (2007). Solving the discrete time/resource trade-off problem in project scheduling with genetic algorithms. Applied Mathematics and Computation, 191, 451–456. https://doi.org/10.1016/j.amc.2007.02.109
• Reyck, B., & Herroelen, W. (1998). A branch-and-bound procedure for the resource-constrained project scheduling problem with generalized precedence relations. European Journal of Operational Research, 111, 152-174.
• Rubeša, R., Hadjina, M., Matulja, T., & Bolf, D. (2023). The shipyard technological level evaluation methodology. Brodogradnja: Teorija i praksa brodogradnje i pomorske tehnike, 74(3), 91-106. https://doi.org/10.1016/S0377-2217(97)00305-6
• Saad, H. M., Chakrabortty, R. K., Elsayed, S., & Ryan, M. J. (2021). Quantum-inspired genetic algorithm for resource-constrained project-scheduling. IEEE Access, 9, 38488-38502. https://doi.org/10.1109/ACCESS.2021.3062790
• Satıç, U. (2014). Çok kaynak kısıtlı projelerin sezgisel yöntemlerle çizelgelenmesi. [MSc Thesis. Yıldız Teknik University].
• Sawant, V. C. (2016). Genetic algorithm for resource constrained project scheduling. International Journal of Science and Research, 5(6), 733-750 https://doi.org/10.1002/(SICI)1520-6750(199810)45:7%3C733::AID-NAV5%3E3.0.CO;2-C
• Schirmer, A. (1998). Case-based reasoning and improved adaptive search for project scheduling. Naval Research Logistics. 47, 201-222. https://doi.org/10.1002/(SICI)1520-6750(200004)47:3%3C201::AID-NAV2%3E3.0.CO;2-L
• Servranckx, T., & Vanhoucke, M. (2019). Strategies for project scheduling with alternative subgraphs under uncertainty: similar and dissimilar sets of schedules. European Journal of Operational Research, 279(1), 38-53. https://doi.org/10.1016/j.ejor.2019.05.023
• Shadrokh, S., & Kianfar, F. (2007). A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty. European Journal of Operational Research, 181(1), 86-101. https://doi.org/10.1016/j.ejor.2006.03.056
• Shariatmadari, M., & Nahavandi, N. (2020). A new resource buffer insertion approach for proactive resource investment problem. Computers & Industrial Engineering, 146, 106582. https://doi.org/10.1016/j.cie.2020.106582
• Snauwaert, J., & Vanhoucke, M. (2021). A new algorithm for resource-constrained project scheduling with breadth and depth of skills. European Journal of Operational Research, 292(1), 43-59. https://doi.org/10.1016/j.ejor.2020.10.032
• Soong, Y. J., Woo, J. H., & Shin., J. G. (2011). Research on systematization and advancement of shipbuilding production management for flexible and agile response for high value offshore platform. International Journal of Naval Architect Ocean Engineering, 3, 181~192. https://doi.org/10.2478/IJNAOE-2013-0061
• Sprecher, A., & Drexl, A. (1998). Multi-mode resource-constrained project scheduling by a simple, general and powerful sequencing algorithm. European Journal of Operational Research, 107(2), 431-450. https://doi.org/10.1016/S0377-2217(97)00348-2
• Sprecher, A., Kolisch, R., & Drexl, A. (1995). Semi-active, active, and non-delay schedules for the resource-constrained project scheduling problem. European Journal of Operational Research, 80, 94-102. https://doi.org/10.1016/0377-2217(93)E0294-8
• Statista. (2023). Size of the global shipbuilding market in 2020 and 2021, with a forecast through 2030. Retrieved on July 1, 2023, from https://www.statista.com/statistics/1102252/size-of-the-global-shipbuilding-market/
• Stopford, M. (2009). Maritime economics. Routledge.
• Tasan, S. O., & Gen, M. (2013). An integrated selection and scheduling for disjunctive network problems. Computers & Industrial Engineering, 65(1), 65-76. https://doi.org/10.1016/j.cie.2011.12.022
• Tesch, A. (2020). A polyhedral study of event-based models for the resource-constrained project scheduling problem. Journal of Scheduling, 23(2), 233-251. https://doi.org/10.1007/s10951-020-00647-6
• Tian, M., Liu, R. J., & Zhang, G. J. (2020). Solving the resource-constrained multi-project scheduling problem with an improved critical chain method. Journal of the Operational Research Society, 71(8), 1243-1258. https://doi.org/10.1080/01605682.2019.1609883
• Toklu, Y. C. (2002). Application of genetic algorithms to construction scheduling with or without resource constraints. Canadian Journal of Civil Engineering, 29, 421-429. https://doi.org/10.1139/l02-034
• Tseng, L. Y., & Chen, S. C. (2006). A hybrid metaheuristic for the resource-constrained project scheduling problem. European Journal of Operational Research, 175(2), 707-721. https://doi.org/10.1016/j.ejor.2005.06.014
• Tseng, L. Y., & Chen, S. C. (2009). Two-phase genetic local search algorithm for the multimode resource-constrained project scheduling problem. IEEE Transactions on Evolutionary Computation, 13(4), 848-857. https://doi.org/10.1109/TEVC.2008.2011991
• Ulusoy, G., & Özdamar, L. (1996). A framework for an interactive project scheduling system under limited resources. European Journal of Operational Research, 90(2), 362-375. https://doi.org/10.1016/0377-2217(95)00360-6
• Ulusoy, G. (2002). Proje planlamada kaynak kısıtlı çizelgeleme. In M. Köksalan, & N. Erkip (Eds.), Yöneylem Araştırması – Halim Doğrusöz’e Armağan (pp. 89-128). ODTÜ Basım.
• UNCTAD. (2022). Review of Maritime Transport 2022. United Nations Publications.
• Valls, V., Ballestin, F., & Quintanilla, M. S. (2004). Justification and RCPSP: A technique that pays. European Journal of Operational Research, 165(2), 375-386. https://doi.org/10.1016/j.ejor.2004.04.008
• Valls, V., Ballestin, F., & Quintanilla, S. (2008). A hybrid genetic algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research, 185(2), 495-508. https://doi.org/10.1016/j.ejor.2006.12.033
• Van Eynde, R., & Vanhoucke, M. (2022). A theoretical framework for instance complexity of the resource-constrained project scheduling problem. Mathematics of Operations Research, 47(4), 3156-3183. https://doi.org/10.1287/moor.2021.1237
• Van Peteghem, V., & Vanhoucke, M. (2008). A genetic algorithm for the multi-mode resource-constrained project scheduling problem. Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/494. Ghent University, Faculty of Economics and Business Administration.
• Van Peteghem, V., & Vanhoucke, M. (2010). A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem, European Journal of Operational Research, 201(2), 409-418. https://doi.org/10.1016/j.ejor.2009.03.034
• Vanhoucke, M., & Coelho, J. (2019). Resource-constrained project scheduling with activity splitting and setup times. Computers & Operations Research, 109, 230-249. https://doi.org/10.1016/j.cor.2019.05.004
• Wang, C., Mao, P., Mao, Y., & Shin, J. G. (2016). Research on scheduling and optimization under uncertain conditions in panel block production line in shipbuilding. International Journal of Naval Architecture and Ocean Engineering, 8(4), 398-408. https://doi.org/10.1016/j.ijnaoe.2016.03.009
• Wang, H. W., Lin, J. R., & Zhang, J. P. (2020). Work package-based information modeling for resource-constrained scheduling of construction projects. Automation in Construction, 109, 102958. https://doi.org/10.1016/j.autcon.2019.102958
• Wang, Y. M. (2009). Centroid defuzzification and the maximizing set and minimizing set ranking based on alpha level sets. Computers & Industrial Engineering, 57(1), 228-236. https://doi.org/10.1016/j.cie.2008.11.014
• Xu, J., & Bai, S. (2023). A reactive scheduling approach for the resource-constrained project scheduling problem with dynamic resource disruption. Kybernetes, In press. https://doi.org/10.1108/K-09-2022-1339
• Yang, Z., & Liu, C. (2018). A hybrid multi-objective gray wolf optimization algorithm for a fuzzy blocking flow shop scheduling problem. Advances in Mechanical Engineering, 10(3), 1687814018765535. https://doi.org/10.1177/1687814018765535
• Yuguang, Z., Bo, A., & Yong Z. (2016). A PSO algorithm for multi-objective hull assembly line balancing using the stratified optimization strategy. Computers & Industrial Engineering, 98, 53-62. https://doi.org/10.1016/j.cie.2016.05.026
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
• Zaman, F., Elsayed, S., Sarker, R., Essam, D., & Coello, C. A. C. (2021). An evolutionary approach for resource constrained project scheduling with uncertain changes. Computers & Operations Research, 125, 105104. https://doi.org/10.1016/j.cor.2020.105104
• Zamani, R. (2019). An effective mirror-based genetic algorithm for scheduling multi-mode resource constrained projects. Computers & Industrial Engineering, 127, 914-924. https://doi.org/10.1016/j.cie.2018.11.031
• Zhang, H., Xu, H & Peng, W. (2008) A Genetic Algorithm for Solving RCPSP. 2008 International Symposium on Computer Science and Computational Technology, China. pp. 246-249. https://doi.org/10.1109/ISCSCT.2008.255
• Zhang, S., Song, X., Shen, L., & Xu, L. (2023). Complicated time-constrained project scheduling problems in water conservancy construction. Processes, 11(4), 1110. https://doi.org/10.3390/pr11041110
• Zhong, Y. G. (2017). Hull mixed-model assembly line balancing using a multi-objective genetic algorithm simulated annealing optimization approach. Concurrent Engineering, 25(1), 30-40. https://doi.org/10.1177/1063293X16666204
• Zhou, T., Long, Q., Law, K. M., & Wu, C. (2022). Multi-objective stochastic project scheduling with alternative execution methods: An improved quantum-behaved particle swarm optimization approach. Expert Systems with Applications, 203, 117029. https://doi.org/10.1016/j.eswa.2022.117029
• Zhu, L., Lin, J., & Wang, Z. J. (2019). A discrete oppositional multi-verse optimization algorithm for multi-skill resource constrained project scheduling problem. Applied Soft Computing, 85, 105805. https://doi.org/10.1016/j.eswa.2022.117029