Skip to main content
Log in

A Pseudo-Global Optimization Approach with Application to the Design of Containerships

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

In this paper, we present a new class of pseudo-global optimization procedures for solving formidable optimization problems in which the objective and/or constraints might be analytically complex and expensive to evaluate, or available only as black-box functions. The proposed approach employs a sequence of polynomial programming approximations that are constructed using the Response Surface Methodology (RSM), and embeds these within a branch-and-bound framework in concert with a suitable global optimization technique. The lower bounds constructed in this process might only be heuristic in nature, and hence, this is called a pseudo-global optimization approach. We develop two such procedures, each employing two alternative branching techniques, and apply these methods to the problem of designing containerships. The model involves five design variables given by the design draft, the depth at side, the speed, the overall length, and the maximum beam. The constraints imposed enforce the balance between the weight and the displacement, a required acceptable length to depth ratio, a restriction on the metacentric height to ensure that the design satisfies the Coast Guard wind heel criterion, a minimum freeboard level as governed by the code of federal regulations (46 CFR 42), and a lower bound on the rolling period to ensure sea-worthiness. The objective function seeks to minimize the required freight rate that is induced by the design in order to recover capital and operating costs, expressed in dollars per metric ton per nautical mile. The model formulation also accommodates various practical issues in improving the representation of the foregoing considerations, and turns out to be highly nonlinear and nonconvex. A practical test case is solved using the proposed methodology, and the results obtained are compared with those derived using a contemporary commercialized design optimization tool. The prescribed solution yields an improved design that translates to an estimated increase in profits of about $18.45 million, and an estimated 27% increase in the return on investment, over the life of the ship.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Alexandrov, N.M., Dennis, J. E. Jr., Lewis, R. M. and Torczon, V. (1998), A Trust-region framework for managing the use of approximation models in optimization, Structural Optimization 15, 16-23.

    Google Scholar 

  • Barthelemy, J.-F. M. and Haftka, R. T. (1993), Approximation concepts for optimum structural design - a review, Structural Optimization 5, 129-144.

    Google Scholar 

  • Bazaraa, M. S., Sherali, H. D. and Shetty, C. M. (1993), Nonlinear Programming: Theory and Algorithms, 2nd edition, John Wiley and Sons, Inc., New York, NY.

    Google Scholar 

  • Bjorkman, M. and Holmstrom, K. (2001), Global optimization of costly nonconvex functions using radial basis functions, Manuscript, Center for Mathematical Modeling, Department of Mathematics and Physics, Malardalen University, Vasteras, Sweden.

    Google Scholar 

  • Booker, A. J., Dennis, J. E. Jr., Frank, P. D., Serafini, D. B., Torczon, V. and Trosset, M. W. (1999), A rigorous framework for optimization of expensive functions by surrogates, Structural Optimization 17, 1-13.

    Google Scholar 

  • Brown, D. K. and Tupper, E. C. (1989), The naval architecture of surface warships, Transactions of the Royal Institute of Naval Architects.

  • Chryssosstomidis, C. (1967), Optimization methods applied to containership design, M.S. Thesis, Department of Naval Architecture and Marine Engineering, Massachusetts Institute of Technology, January.

  • Coast Guard (1997), Department of Transportation, 46 CFR Ch. 1 (10-1-97 Edition).

  • Cox, D. D., and John, S. (1997), SDO: a statistical method for global optimization, in Alexandrov, N. and Hussaini, M. Y. (eds.), Multidisciplinary Design Optimization: State of the Art, SIAM, Philadelpha, PA, pp. 315-329.

    Google Scholar 

  • Cox, S. E., Haftka, R. T., Baker, C. A., Grossman, B., Mason, W. H. and Watson, L. T. (2001), A comparison of global optimization methods for the design of a high-speed civil transport, Journal of Global Optimization 21, 415-433.

    Google Scholar 

  • Erichsen, S. (1971), Optimum capacity of ships and port terminals, Ph.D. Dissertation, Department of Naval Architecture and Marine Engineering, The University of Michigan, Ann Arbor, MI, April.

    Google Scholar 

  • Ganesan, V. (1999), A model for multidisciplinary design optimization of containerships, M.S. Thesis, Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, July.

    Google Scholar 

  • Ganesan, V. (2001), Global optimization of the nonconvex containership design problem using the reformulation-linearization technique, M.S. Thesis, Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, July.

    Google Scholar 

  • Gutmann, H.-M. ( 2001), A radial basis function method for global optimization, Journal of Global Optimization 19, 201-227.

    Google Scholar 

  • Holtrop, J. and Mennen, G. G. J. (1984), A statistical re-analysis of resistance and propulsion data, International Shipbuilding Progress 31, 272-276.

    Google Scholar 

  • Horst, R. and Tuy, H. (1993), Global Optimization: Deterministic Approaches, 2nd edition, Springer-Verlag, Berlin, Germany.

    Google Scholar 

  • Hovgaard, W. (1920), General Design of Warships, Spon & Chamberlain, New York, NY.

    Google Scholar 

  • Jones, D. R. (2001), A taxonomy of global optimization methods based on response surfaces, Journal of Global Optimization 21, 345-383.

    Google Scholar 

  • Jones, D. R., Perttunen, C. D. and Stuckman, B. E. (1993), Lipschitzan optimization without the Lipschitz constant, Journal of Optimization Theory and Applications 79, 157-181.

    Google Scholar 

  • Jones, D. R., Schonlau, M. and Welch,W. J. (1998), Efficient global optimization of expensive blackbox functions, Journal of Global Optimization 13, 455-492.

    Google Scholar 

  • Joshi, S. S., Sherali, H. D. and Tew, J. D. (1998), An enhanced response surface methodology algorithm using gradient deflection and second-order search Strategies, Computers and Operations Research 25(7/8), 531-541.

    Google Scholar 

  • Keane, A. J., Price, W. G. and Schachter, R. D. (1991), Optimization techniques in ship concept design, Transactions of the Royal Institute of Naval Architects 133(Part A), 123-139.

    Google Scholar 

  • Knill, D. L., Giunta, A. A., Baker, C. A., Grossman, B., Mason, W. H., Haftka, R. T. and Watson, L. T. (1999), Response surface models combining linear and Euler aerodynamics for supersonic transport design, Journal of Aircraft 36, 75-86.

    Google Scholar 

  • Myers, R. H. (1995), Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, Inc., New York, NY.

    Google Scholar 

  • Neddermeijer, H. G., van Oostmarssen, G. J., Piersma, N. and Dekker, R. (2000), In A Framework for Response Surface Methodology for Simulation Optimization, Joines, J. A., Barta, R. R., Kang, K. and Fishwick, P. A. (eds.), pp. 129-136.

  • Neu, W. L., Hughes, O., Mason, W. H., Ni, S., Chen, Y., Ganesan, V., Lin, Z. and Tumma, S. (2000), A prototype tool for multidisciplinary design optimization of ships, Ninth congress of the International Maritime Association of the Mediterranean, Naples, Italy, April.

  • Ni, S.Y. (1998), Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, Personal Communication, December.

    Google Scholar 

  • Peri, D., Rosetti, M. and Campana, E. F. (2001), Design optimization of ship hulls via CFD techniques, Journal of Ship Research 45(2), 140-149.

    Google Scholar 

  • Powell, M. J. D. (2001), On the Lagrange functions of quadratic models that are defined by interpolation, Optimization Methods Software 16, 289-309.

    Google Scholar 

  • Powell, M. J. D. (2002), UOBYQA: unconstrained optimization by quadratic approximation, Mathematical Programming 92(3), 555-582.

    Google Scholar 

  • Rawson, J. J. and Tupper, E. C. (1984), Basic Ship Theory, 3rd Edition, Longman Inc., New York, NY.

    Google Scholar 

  • Ray, T., Gokarn, R. P. and Sha, O. P. (1995), A global optimization model for ship design, Computers in Industry 26, 175-192.

    Google Scholar 

  • Ray, T. and Sha, O. P. (1994), Multicriteria Optimization Model for Containership Design, Department of Naval Architecture, The Indian Institute of Technology, Kharagpur, India.

    Google Scholar 

  • Sahinidis, N. V. (1996), BARON: a general purpose global optimization software package, Journal of Global Optimization 8(2), 201-205.

    Google Scholar 

  • SAS Institute Inc. (2001), JMP Users Manual, 2nd edition, Cary, NC.

  • Schneekluth, H. (1987), Ship Design for Efficiency and Economy, Aachen University of Technology, Aachen, Germany.

    Google Scholar 

  • Schonlau, M., Welch,W. J. and Jones, D. R. (1997), Global versus local search in constrained optimization of computer models, in: Flournoy, N., Rosenberger, W. F. and Wong, W. K. (eds.), New Developments and Applications in Experimental Design, Institute of Mathematical Statistics.

  • Sen, P. (1992), Marine design: the multiple criteria approach, Transactions of the Royal Institute of Naval Architects 134(Part B), 261-272.

    Google Scholar 

  • Sherali, H. D. and Tuncbilek, C. H. (1992), A global optimization algorithm for polynomial programming problems using a reformulation-linearization technique, Journal of Global Optimization 2, 101-112.

    Google Scholar 

  • Sherali, H. D. and Tuncbilek, C. H. (1995), A reformulation-convexification approach for solving nonconvex quadratic programming problems, Journal of Global Optimization 7, 1-31.

    Google Scholar 

  • Sherali, H. D. and Tuncbilek, C. H. (1997), New reformulation-linearization/convexification relaxations for univariate and multivariate polynomial programming problems, Operations Research Letters 21, 1-9.

    Google Scholar 

  • Sherali, H. D. and Wang, H. (2001), Global optimization of nonconvex factorable programming problems, Mathematical Programming 89(3), 459-478.

    Google Scholar 

  • Taggart, R. (1980), Ship Design and Construction, The Society of Naval Architects and Marine Engineers, New York, NY.

    Google Scholar 

  • Tahara, Y., Paterson, E., Stern, F. and Himeno, Y. (2000), Flow-and wave-field optimization of surface combatants using CFD-based optimization methods, 23rd Office of Naval Research Symposium on Naval Hydrodynamcis, Val de Reuil, France.

  • Valorani, M., Peri, D. and Campana, E. F. (2000), Efficient strategies to design optimal ship hulls, American Institute of Aeronautics and Astronautics, Paper 2000-4731, Eighth Multidisciplinary Analysis and Optimization Conference and Exhibit, Long Beach, CA, September 5-8.

  • Vanderplaats Research and Development, Inc. (1995), Dot Users Manual, Version 4.20, Colorado Springs, CO.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sherali, H.D., Ganesan, V. A Pseudo-Global Optimization Approach with Application to the Design of Containerships. Journal of Global Optimization 26, 335–360 (2003). https://doi.org/10.1023/A:1024792717467

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1024792717467

Navigation