Optimization Model to Manage Ship Fuel Consumption and Navigation Time

1. M. A. Dulebenets, “A comprehensive multi-objective optimization model for the vessel scheduling problem in liner shipping,” Int. J. Prod. Econ., 2018, doi: 10.1016/j.ijpe.2017.10.027. Open DOISearch in Google Scholar

2. R. Zaccone, E. Ottaviani, M. Figari, and M. Altosole, “Ship voyage optimization for safe and energy-efficient navigation: A dynamic programming approach,” Ocean Eng., 2018, doi: 10.1016/j.oceaneng.2018.01.100. Open DOISearch in Google Scholar

3. R. Szłapczyński and H. Ghaemi, “Framework of an evolutionary multi-objective optimisation method for planning a safe trajectory for a marine autonomous surface ship,” Polish Marit. Res., 2020, doi: 10.2478/pomr-2019-0068. Open DOISearch in Google Scholar

4. E. Sobecka, R. Szłapczynski, and M. Zyczkowski, “Evolutionary multi-objective weather routing of sailboats,” Polish Marit. Res., 2020, doi: 10.2478/pomr-2020-0054. Open DOISearch in Google Scholar

5. L. Yang, G. Chen, N. G. M. Rytter, J. Zhao, and D. Yang, “A genetic algorithm-based grey-box model for ship fuel consumption prediction towards sustainable shipping,” Ann. Oper. Res., 2019, doi: 10.1007/s10479-019-03183-5. Open DOISearch in Google Scholar

6. A. Cheaitou and P. Cariou, “Greening of maritime transportation: a multi-objective optimization approach,” Ann. Oper. Res., 2019, doi: 10.1007/s10479-018-2786-2. Open DOISearch in Google Scholar

7. A. Priftis, E. Boulougouris, O. Turan, and G. Atzampos, “Multi-objective robust early stage ship design optimisation under uncertainty utilising surrogate models,” Ocean Eng., 2020, doi: 10.1016/j.oceaneng.2019.106850. Open DOISearch in Google Scholar

8. T. P. Scholcz and C. H. J. Veldhuis, “Multi-objective surrogate based hull-form optimization using high-fidelity rans computations,” 2017. Search in Google Scholar

9. S. Zhang, B. Zhang, T. Tezdogan, L. Xu, and Y. Lai, “Computational fluid dynamics-based hull form optimization using approximation method,” Eng. Appl. Comput. Fluid Mech., 2018, doi: 10.1080/19942060.2017.1343751. Open DOISearch in Google Scholar

10. J. Čerka et al., “Optimization of the research vessel hull form by using numerical simulaton,” Ocean Eng., 2017, doi: 10.1016/j.oceaneng.2017.04.040. Open DOISearch in Google Scholar

11. Z. Baoji, “Research on Ship Hull Optimisation of High-Speed Ship Based on Viscous Flow/Potential Flow Theory,” Polish Marit. Res., 2020, doi: 10.2478/pomr-2020-0002. Open DOISearch in Google Scholar

12. A. I. Ölçer, “A hybrid approach for multi-objective combinatorial optimisation problems in ship design and shipping,” Comput. Oper. Res., 2008, doi: 10.1016/j.cor.2006.12.010. Open DOISearch in Google Scholar

13. S. Su, Y. Zheng, J. Xu, and T. Wang, “Cabin Placement Layout Optimisation Based on Systematic Layout Planning and Genetic Algorithm,” Polish Marit. Res., 2020, doi: 10.2478/pomr-2020-0017. Open DOISearch in Google Scholar

14. Y. L. Wang, C. Wang, and Y. Lin, “Ship cabin layout optimization design based on the improved genetic algorithm method,” 2013, doi: 10.4028/www.scientific.net/AMM.300-301.146. Open DOISearch in Google Scholar

15. J. Li, H. Guo, S. Zhang, X. Wu, and L. Shi, “Optimum Design of Ship Cabin Equipment Layout Based on SLP Method and Genetic Algorithm,” Math. Probl. Eng., 2019, doi: 10.1155/2019/9492583. Open DOISearch in Google Scholar

16. X. Liu, Z. Liu, S. Yu, and T. Gong, “Adapted particle swarm optimization algorithm–based layout design optimization of passenger car cockpit for enhancing ergonomic reliability,” Adv. Mech. Eng., 2019, doi: 10.1177/1687814019837808. Open DOISearch in Google Scholar

17. V. Bolbot, N. L. Trivyza, G. Theotokatos, E. Boulougouris, A. Rentizelas, and D. Vassalos, “Cruise ships power plant optimisation and comparative analysis,” Energy, 2020, doi: 10.1016/j.energy.2020.117061. Open DOISearch in Google Scholar

18. H. Ghassemi and H. Zakerdoost, “Ship hull-propeller system optimization based on the multi-objective evolutionary algorithm,” Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., 2017, doi: 10.1177/0954406215616655. Open DOISearch in Google Scholar

19. John Huisman; Evert-Jan Foeth, “Automated multi-objective optimization of ship propellers,” 2017. Search in Google Scholar

20. F. Vesting and R. E. Bensow, “Particle swarm optimization: an alternative in marine propeller optimization?,” Eng. Optim., 2018, doi: 10.1080/0305215X.2017.1302438. Open DOISearch in Google Scholar

21. S. Mirjalili, A. Lewis, and S. A. M. Mirjalili, “Multi-objective optimisation of marine propellers,” 2015, doi: 10.1016/j.procs.2015.05.504. Open DOISearch in Google Scholar

22. S. Gaggero et al., “Efficient and multi-objective cavitating propeller optimization: An application to a high-speed craft,” Appl. Ocean Res., 2017, doi: 10.1016/j.apor.2017.01.018. Open DOISearch in Google Scholar

23. R. Zhao, X. Xie, and W. Yu, “Repair equipment allocation problem for a support-and-repair ship on a deep sea: A hybrid multi-criteria decision making and optimization approach,” Expert Syst. Appl., 2020, doi: 10.1016/j.eswa.2020.113658. Open DOISearch in Google Scholar

24. A. K. Verma, A. Srividya, A. Rana, and S. K. Khattri, “Optimization of maintenance scheduling of ship borne machinery for improved reliability and reduced cost,” Int. J. Reliab. Qual. Saf. Eng., 2012, doi: 10.1142/S0218539312500143. Open DOISearch in Google Scholar

25. Y. Zhao, Y. Fan, J. Zhou, and H. Kuang, “Bi-objective optimization of vessel speed and route for sustainable coastal shipping under the regulations of emission control areas,” Sustain., 2019, doi: 10.3390/su11226281. Open DOISearch in Google Scholar

26. K. Rudzki, “Two-objective optimization of engine ship propulsion settings with controllable pitch propeller using artificial neural networks,” Gdynia Maritime University, 2014. Search in Google Scholar

27. K. Rudzki and W. Tarelko, “A decision-making system supporting selection of commanded outputs for a ship’s propulsion system with a controllable pitch propeller,” Ocean Eng., 2016, doi: 10.1016/j.oceaneng.2016.09.018. Open DOISearch in Google Scholar

28. J. Kozak and W. Tarełko, “Case study of masts damage of the sail training vessel POGORIA,” Engineering Failure Analysis. 2011, doi: 10.1016/j.engfailanal.2010.11.016. Open DOISearch in Google Scholar

29. W. Tarełko, “The effect of hull biofouling on parameters characterising ship propulsion system efficiency,” Polish Marit. Res., 2014, doi: 10.2478/pomr-2014-0038. Open DOISearch in Google Scholar

30. A. Tuan Hoang et al., “A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels,” Sustain. Energy Technol. Assessments, vol. 47, p. 101416, Oct. 2021, doi: 10.1016/j.seta.2021.101416. Open DOISearch in Google Scholar

31. W. Tarelko and K. Rudzki, “Applying artificial neural networks for modelling ship speed and fuel consumption,” Neural Computing and Applications. 2020, doi: 10.1007/s00521-020-05111-2. Open DOISearch in Google Scholar

32. R. Tadeusiewicz, “Neural network as a tool for medical signals filtering, diagnosis aid, therapy assistance and forecasting improving,” 2009, doi: 10.1007/978-3-642-03882-2-406. Open DOISearch in Google Scholar

33. R. Matignon, “Neural Network Modeling using SAS Enterprise Miner,” AuthorHouse,London, 2005. Search in Google Scholar

34. J. Andersson, “A survey of multiobjective optimization in engineering design,” 2000. Search in Google Scholar

35. R. T. Marler and J. S. Arora, “Survey of multi-objective optimization methods for engineering,” Structural and Multidisciplinary Optimization. 2004, doi: 10.1007/s00158-003-0368-6. Open DOISearch in Google Scholar