Hostname: page-component-7479d7b7d-rvbq7 Total loading time: 0 Render date: 2024-07-11T03:22:38.132Z Has data issue: false hasContentIssue false

OPTIMISATION OF NAVAL SHIP COMPARTMENT LAYOUT DESIGN USING GENETIC ALGORITHM

Published online by Cambridge University Press:  27 July 2021

Venkata Aditya Dharani Pragada*
Affiliation:
Indian Institute of Technology Delhi
Akanistha Banerjee
Affiliation:
Indian Institute of Technology Delhi
Srinivasan Venkataraman
Affiliation:
Indian Institute of Technology Delhi
*
Dharani Pragada, Venkata Aditya, Indian Institute of Technology Delhi, Department of Design, India, ddz198405@design.iitd.ac.in

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

An efficient general arrangement is a cornerstone of a good ship design. A big part of the whole general arrangement process is finding an optimized compartment layout. This task is especially tricky since the multiple needs are often conflicting, and it becomes a serious challenge for the ship designers. To aid the ship designers, improved and reliable statistical and computation methods have come to the fore. Genetic algorithms are one of the most widely used methods. Islier's algorithm for the multi-facility layout problem and an improved genetic algorithm for the ship layout design problem are discussed. A new, hybrid genetic algorithm incorporating local search technique to further the improved genetic algorithm's practicality is proposed. Further comparisons are drawn between these algorithms based on a test case layout. Finally, the developed hybrid algorithm is implemented on a section of an actual ship, and the findings are presented.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Barrera, J., 2020. dijstra very simple, s.l.: MATLAB Central File Exchange.Google Scholar
Chatterjee, S., Carrera, C. & Lynch, L. A., 1995. Genetic algorithms and travelling salesman problems. European Journal of Operations Research, Volume 1996, pp. 490510.Google Scholar
Coit, W. D., Smith, A. E. & Tate, D. M., 1995. Adaptive penalty methods for genetic optimization of constrained combinatorial problem. ORSA Journal on Computing, Volume 1995, p. 31.Google Scholar
Gillespie, J., 2012. A network science approach to understanding and generating ship arrangement in early-stage design, s.l.: University of Michigan.Google Scholar
Hussain, A. et al. , 2017a. Genetic algorithm for travelling salesman problem with modified cycle crossover operator. Computer Intelligence and Neuroscience, Volume 2017, p. 7.10.1155/2017/7430125CrossRefGoogle Scholar
Hussain, Abid, Muhammad, Yousaf Shad, Nauman Sajid, M., Hussain, Ijaz, Shoukry, Alaa Mohamd, Gani, Showkat, “Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator”, Computational Intelligence and Neuroscience, vol. 2017b, Article ID 7430125, 7 pages, 2017. https://doi.org/10.1155/2017/7430125CrossRefGoogle Scholar
Igrec, Bojan & Pawling, Rachel & Sobey, Adam & Rigby, Jake & Thomas, Giles. (2019). An interactive layout exploration and optimisation method for early-stage ship design.Google Scholar
Ingalls-Ship-Building-Corporation-Mississippi, 1942. General arrangement HMS Battler, s.l.: Plan No. AVG-P9-01.Google Scholar
Islier, A. A., 1998. A genetic algorithm approach for multiple criteria facility layout design. International Journal of Production Research.10.1080/002075498193165CrossRefGoogle Scholar
Lee, K. Y., Han, S. N. & Roh, M. I., 2002. Optimal compartment layout design for a naval ship using an improved genetic algorithm. Marine Technology, Volume 39, pp. 159167.Google Scholar
Parsons, Michael & Chung, Hyun & Kirtley, Eleanor & Daniels, Anthony & Su, Liu & Jignesh, Patel. (2008). Intelligent Ship Arrangements: A New Approach to General Arrangement. Naval Engineers Journal. 120. 51 - 65. 10.1111/j.1559-3584.2008.00153. x.10.1111/j.1559-3584.2008.00153.xCrossRefGoogle Scholar
Smith, A. E. & Coit, D. W., 1997. Constraint-handling techniques- penalty functions. Handbook of Evolutionary Computation, Volume Section C5.2, p. 11.Google Scholar
Wan, W. & Birch, J. B., 2013. An improved hybrid genetic algorithm with a new local search procedure. Journal of Applied Mathematics, Volume 2013.10.1155/2013/103591CrossRefGoogle Scholar
Watson, D. G., 1998. Practical ship design. vol 1 ed. s.l. Elsevier.Google Scholar