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Optimization model to manage ship fuel consumption and navigation time

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Owners of vessels are interested in the lowest possible operating costs. These costs are mainly related to fuel consumption during navigation. To manage it rationally, the main decision-making problem is selecting the proper parameters of the ship’s propulsion system during navigation. In practice, operators of ships equipped with controllable pitch propellers controlled in manual mode make a selection of the commanded outputs based on their own knowledge, intuition, and all accessible information regarding sea conditions. In many cases, their decisions are unreasonable or incorrect. Therefore, it would be desirable to support their decision-making in selecting the commanded outputs. For this reason, we have decided to develop a decision support system in the form of an expert system. This computer-aided system supports the selection of the commanded outputs of the ship’s propulsion system. The most important component of this system is the two-criteria optimization model, allowing the rational management of the ship fuel consumption and navigation time.
Rocznik
Tom
Strony
141--153
Opis fizyczny
Bibliogr. 35 poz., rys., tab.
Twórcy
  • Gdynia Maritime University Faculty of Marine Engineering Gdynia Poland
  • Gdańsk University of Technology Narutowicza 11/12 80-233 Gdańsk Poland
  • HUTECH University Ho Chi Minh City Viet Nam
Bibliografia
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 8. T. P. Scholcz and C. H. J. Veldhuis, “Multi-objective surrogate based hull-form optimization using high-fidelity rans computations,” 2017.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 19. John Huisman; Evert-Jan Foeth, “Automated multi-objective optimization of ship propellers,” 2017.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 26. K. Rudzki, “Two-objective optimization of engine ship propulsion settings with controllable pitch propeller using artificial neural networks,” Gdynia Maritime University, 2014.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 33. R. Matignon, “Neural Network Modeling using SAS Enterprise Miner,” AuthorHouse,London, 2005.
  • 34. J. Andersson, “A survey of multiobjective optimization in engineering design,” 2000.
  • 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.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e7cbb178-ade3-4a94-85c4-7bdf15e61928
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