Advertisement

Many objective visual analytics: rethinking the design of complex engineered systems

  • Matthew J. Woodruff
  • Patrick M. Reed
  • Timothy W. Simpson
Research Paper
First Online:
Received:
Revised:
Accepted:

Abstract

Many cognitive and computational challenges accompany the design of complex engineered systems. This study proposes the many-objective visual analytics (MOVA) framework as a new approach to the design of complex engineered systems. MOVA emphasizes learning through problem reformulation, enabled by visual analytics and many-objective search. This study demonstrates insights gained by evolving the formulation of a General Aviation Aircraft (GAA) product family design problem. This problem’s considerable complexity and difficulty, along with a history encompassing several formulations, make it well-suited to demonstrate the MOVA framework. The MOVA framework results compare a single objective, a two objective, and a ten objective formulation for optimizing the GAA product family. Highly interactive visual analytics are exploited to demonstrate how decision biases can arise for lower dimensional, highly aggregated problem formulations.

Keywords

Multi-objective optimization Multidimensional data visualization Product family design 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

The second author of this work was partially supported by the US National Science Foundation under Grant CBET-0640443. The computational resources for this work were provided in part through instrumentation funded by the National Science Foundation through Grant OCI-0821527. Any opinions,findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the US National Science Foundation.

References

  1. Arrow K (1950) A difficulty in the concept of social welfare. J Polit Econ 58(4):328–346CrossRefGoogle Scholar
  2. Balling R (1999) Design by shopping: a new paradigm? In: Proceedings of the Third World Congress of structural and multidisciplinary optimization (WCSMO-3), pp 295–297Google Scholar
  3. Balling RJ, Taber JT, Brown MR, Day K (1999) Multiobjective urban planning using genetic algorithms. J Urban Plan Dev 125(2):86–99CrossRefGoogle Scholar
  4. Bloebaum C, McGowan A-M (2010) Design of complex engineered systems. J Mech Des 132(12):120301–1–120301–2CrossRefGoogle Scholar
  5. Brill ED, Flach JM, Hopkins LD, Ranjithan S (1990) MGA: a decision support system for complex, incompletely defined problems. IEEE Trans Syst Man Cybern 20(4):745–757CrossRefGoogle Scholar
  6. Brockhoff D, Friedrich T, Hebbinghaus N, Klein C, Neumann F, Zitzler E (2007) Do additional objectives make a problem harder? In: Genetic and evolutionary computation conference (GECCO ‘07), London, England, pp 765–772Google Scholar
  7. Chen W, Elliot JG, Simpson TW, Virasak J (1995) Designing a general aviation aircraft as an open engineering system. Design Report for ME8104, Georgia Institute of TechnologyGoogle Scholar
  8. Climaco J (2004) A critical reflection on optimal decision. Eur J Oper Res 153(2):506–516MATHCrossRefGoogle Scholar
  9. Coello Coello CA, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Kluwer Academic Publishers. doi: 10.1007/978-0-387-36797-2
  10. Deb K, Agrawal RB (1994) Simulated binary crossover for continuous search space. Tech. Rep. Technical Report IITK/ME/SMD-94027, Indian Institute of Technology, Kanpur, Kanpur, UP, IndiaGoogle Scholar
  11. Deb K, Joshi D, Anand A (2002) Real-coded evolutionary algorithms with parent-centric re-combination. In: Proceedings of the World on Congress on computational intelligence, vol 1, pp 61–66Google Scholar
  12. Deb K, Mohan M, Mishra S (2005) Evaluating the \(\varepsilon \)-domination based multiobjective evolutionary algorithm for a quick computation of pareto-optimal solutions. Evol Comput J 13(4):501–525CrossRefGoogle Scholar
  13. Di Pierro F, Khu S-T, Savi DA (2007) An investigation on preference order ranking scheme for multiobjective evolutionary optimization. IEEE Trans Evol Comput 11(1):17–45. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4079613 CrossRefGoogle Scholar
  14. Eddy J, Lewis K (2002) Visualization of multi-dimensional design and optimization data using cloud visualization In: ASME design technical conferences, design automation conference. Paper No. DETC2002/DAC-34130Google Scholar
  15. English K, Bloebaum C (2008) Visual dependency structure matrix for multidisciplinary design optimization tradeoff studies. J Aerosp Comput Inf Commun 5(1):274–297CrossRefGoogle Scholar
  16. ESTECO SpA (2012) modeFrontier. http://www.modefrontier.com/. Accessed 5 Apr 2012
  17. Ferringer M, Spencer D, Reed P (2009) Many-objective reconfiguration of operational satellite constellations with the large-cluster epsilon non-dominated sorting genetic algorithm-ii. In: Proceedings of the 2009 IEEE congress on evolutionary computation. IEEE, pp 340–349Google Scholar
  18. Fleming PJ, Purshouse RC, Lygoe RJ (2005) Many-objective optimization: an engineering design perspective. In: In evolutionary multi-criterion optimization. Lecture notes in computer science, vol 3410. Springer, Berlin/Heidelberg, pp 14–32Google Scholar
  19. Fonseca CM, Fleming PJ (1998) Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation. IEEE Trans Syst, Man Cybern, A: Syst Humans 28(1):26–37CrossRefGoogle Scholar
  20. Franssen M (2005) Arrow’s theorem, multi-criteria decision problems and multi-attribute preferences in engineering design. Res Eng Des 16(1):42–56CrossRefGoogle Scholar
  21. Goldberg D (2002) The design of innovation: lessons from and for competent genetic algorithms, vol 7. Springer, New YorkGoogle Scholar
  22. Hadka D, Reed P (2012a) Borg: an auto-adaptive many-objective evolutionary computing framework. Evol Comput. doi: 10.1162/EVCO_a_00075 Google Scholar
  23. Hadka D, Reed P (2012b) Diagnostic assessment of search controls and failure modes in many-objective evolutionary optimization. Evol Comput 20(3):423–452CrossRefGoogle Scholar
  24. Hadka D, Reed PM, Simpson TW (2012) Diagnostic assessment of the borg moea for many-objective product family design problems. In: Proceedings of the 2012 IEEE World Congress on computational intelligence, IEEE, pp 986–995Google Scholar
  25. Hitch CJ (1960) On the choice of objectives in systems studies. Tech. Rep. P-1955, The RAND CorporationGoogle Scholar
  26. Hogarth R (1981) Beyond discrete biases: functional and dysfunctional aspects of judgmental heuristics. Psychol Bull 90(2):197–217CrossRefGoogle Scholar
  27. Inselberg A (1997) Multidimensional detective. In: IEEE symposium on information visualization, 1997. Proceedings, IEEE, pp 100–107Google Scholar
  28. Inselberg A (2009) Parallel coordinates: visual multidimensional geometry and its applications. Springer, New YorkGoogle Scholar
  29. Kanukolanu D, Lewis K, Winer E (2006) A multidimensional visualization interface to aid in trade-off decisions during the solution of coupled subsystems under uncertainty. J Comput Inf Sci Eng 6:288CrossRefGoogle Scholar
  30. Kasprzyk JR, Reed PM, Kirsch BR, Characklis GW (2009) Managing population and drought risks using many-objective water portfolio planning under uncertainty. Water Resources Research 45:W12401. doi: 10.1029/2009WR008121 CrossRefGoogle Scholar
  31. Kasprzyk JR, Reed PM, Kirsch BR, Characklis GW (2012) Many-objective de novo water supply portfolio planning under deep uncertainty. Environ Model Softw 34:87–104. doi: 10.1016/j.envsoft.2011.04.003 CrossRefGoogle Scholar
  32. Keim DA, Kohlhammer J, Ellis G, Mansmann F (eds) (2010) Mastering the information age - solving problems with visual analytics. Eurographics. http://www.vismaster.eu/wp-content/uploads/2010/11/VisMaster-book-lowres.pdf
  33. Kipouros T, Mleczko M, Savill A (2008) Use of parallel-coordinates for post-analyses of multi-objective aerodynamic optimisation in turbomachinery. In: Proceedings of the 4th AIAA multi-disciplinary design optimization specialist conference, Schaumburg, IL. Paper No. AIAA-2008-2138Google Scholar
  34. Kita H, Ono I, Kobayashi S (1999) Multi-parental extension of the unimodal normal distribution crossover for real-coded genetic algorithms. In: Proceedings of the 1999 congress on evolutionary computation, pp 1581–1588Google Scholar
  35. Kollat J, Reed P (2005) The value of online adaptive search: a performance comparison of NSGAII, \(\varepsilon \)-NSGAII and \(\varepsilon \)MOEA. In: Coello Coello C, Hernández Aguirre A, Zitzler E (eds) Evolutionary multi-criterion optimization. Lecture notes in computer science, vol 3410. Springer, Berlin/Heidelberg, pp 386–398Google Scholar
  36. Kollat JB, Reed P (2007a) A framework for visually interactive decision-making and design using evolutionary multi-objective optimization (VIDEO). Environ Modell Softw 22(12):1691–1704. http://linkinghub.elsevier.com/retrieve/pii/S1364815207000308 CrossRefGoogle Scholar
  37. Kollat J, Reed P (2007b) A computational scaling analysis of multiobjective evolutionary algorithms in long-term groundwater monitoring applications. Adv Water Resour 30(3):408–419CrossRefGoogle Scholar
  38. Kollat J, Reed P, Maxwell R (2011) Many-objective groundwater monitoring network design using bias-aware ensemble kalman filtering, evolutionary optimization, and visual analytics. Water Resour Res 47:W02529. doi: 10.1029/2010WR009194 CrossRefGoogle Scholar
  39. Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multiobjective optimization. Evol Comput 10(3):263–282CrossRefGoogle Scholar
  40. Messac A, Martinez M, Simpson T (2002) A penalty function for product family design using physical programming. ASME J Mech Des 124(2):164–172CrossRefGoogle Scholar
  41. Miller GA (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97CrossRefGoogle Scholar
  42. Naim A, Chiu P, Bloebaum C, Lewis K (2008) Hyper-radial visualization for multi-objective decision-making support under uncertainty using preference ranges: the PRUF method. In: 12th AIAA/ISSMO multidisciplinary analysis and optimization conference. Paper No. AIAA 2008-6087Google Scholar
  43. NASA (1978) GASP—general aviation synthesis program. Tech. Rep. NASA-CR-152303National Aeronautics and Space Administration Ames Research Center, Moffet Field, CaliforniaGoogle Scholar
  44. NASA, FAA (1994) General aviation design competition guidelines. Virginia Space Grant Consortium, Hampton, VAGoogle Scholar
  45. Nolan D, Thal J, Henry K, Sandy M (1995) NASA/FAA announce aviation design competition winners [press release]Google Scholar
  46. R project (2012) The R project for statistical computing. http://www.r-project.org. Accessed 5 Apr 2012
  47. Raymer D (1999) Aircraft design: a conceptual approach. American Institute of Aeronautics and Astronautics, Inc., Reston, VAGoogle Scholar
  48. Reed P, Hadka D, Herman J, Kasprzyk J, Kollat J (2013) Evolutionary multiobjective optimization in water resources: the past, present, and future (editor invited submission to 35th anniversary special issue). Adv Water Resour 51:438–456CrossRefGoogle Scholar
  49. Roy B (1971) Problems and methods with multiple objective functions. Math Program 1(1):239–266MATHCrossRefGoogle Scholar
  50. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26MATHCrossRefGoogle Scholar
  51. SAS Institute (2012) JMP statistical discovery software. http://jmp.com. Accessed 5 Apr 2012
  52. See T, Gurnani A, Lewis K (2003) An approach to robust multi-attribute concept selection. In: Proceedings of ASME 2003 design engineering technical conferences, ASME. Paper No. DETC2003/DAC-48707Google Scholar
  53. Seo J, Shneiderman B (2005) A rank-by-feature framework for interactive exploration of multidimensional data. Inf Vis 4(2):96–113. http://www.palgrave-journals.com/doifinder/10.1057/palgrave.ivs.9500091 CrossRefGoogle Scholar
  54. Shah R, Simpson T, Reed P (2011) Many-objective evolutionary optimisation and visual analytics for product family design. In: Wang L, et al (eds) Multi-objective evolutionary optimisation for product design and manufacturing. Springer-Verlag, London, pp 137–159. doi: 10.1007/978-0-85729-652-8_4 CrossRefGoogle Scholar
  55. Simpson T (1995) Development of a design process for realizing open engineering systems. Master’s Thesis, Georgia Institute of TechnologyGoogle Scholar
  56. Simpson TW, D’Souza B (2004) Assessing variable levels of platform commonality within a product family using a multiobjective genetic algorithm. Concurr Eng: Res Appl 12(2):119–130CrossRefGoogle Scholar
  57. Simpson T, Martins JRRA (2010) The future of multidisciplinary design optimization (mdo): advancing the design of complex engineered systems. Tech. rep., National Science Foundation, Fort Worth, TXGoogle Scholar
  58. Simpson TW, Martins JRRA (2011) Multidisciplinary design optimization for complex engineered systems: Report from a national science foundation workshop. J Mech Des 133(10):101002. doi: 10.1115/1.4004465. http://link.aip.org/link/?JMD/133/101002/1 CrossRefGoogle Scholar
  59. Simpson TW, Chen W, Allen JK, Mistree F (1996) Conceptual design of a family of products through the use of the robust concept exploration method. In: 6th AIAA/USAF/NASA/ISSMO symposium on multidisciplinary analysis and optimization, AIAA, Bellevue, WA, pp 1535–1545Google Scholar
  60. Simpson T, Seepersad C, Mistree F (2001) Balancing commonality and performance within the concurrent design of multiple products in a product family. Concurr Eng 9(3):177–190CrossRefGoogle Scholar
  61. Slingerland LA, Bobuk A, Simpson TW (2010) Product family optimization using a multidimensional data visualization approach. In: 13th AIAA/ISSMO multidisciplinary analysis and optimization converence, Fort Worth, TX. Paper No. AIAA 2010-9031Google Scholar
  62. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetMATHCrossRefGoogle Scholar
  63. Stump G, Yukish M, Simpson T, Harris E (2003) Design space visualization and its application to a design by shopping paradigm. In: ASME design engineering technical conferences-design automation conference, Chicago, IL, ASME. Paper No. DETC2003/DAC-48785Google Scholar
  64. Teytaud O (2006) How entropy-theorems can show that on-line approximating high-dim pareto fronts is too hard. Technical Report, Inria Saclay (CR1)Google Scholar
  65. Teytaud O (2007) On the hardness of offline multi-objective optimization. Evol Comput 15(4):475–491CrossRefGoogle Scholar
  66. Thomas J, Cook K, National Visualization and Analytics Center (2005) Illuminating the path: the research and development agenda for visual analytics. [Book]. IEEE Computer SocietyGoogle Scholar
  67. TIBCO (2012) Spotfire. http://spotfire.tibco.com. Accessed 5 Apr 2012
  68. Tsoukiàs A (2008) From decision theory to decision aiding methodology. Eur J Oper Res 187:138–161CrossRefGoogle Scholar
  69. Tsutsui S, Yamamura M, Higuchi T (1999) Multi-parent recombination with simplex crossover in real coded genetic algorithms. In: Genetic and evolutionary computation conference (GECCO 1999)Google Scholar
  70. Venkataraman S, Haftka RT (2004) Structural optimization complexity: what has moore’s law done for us? Struct Multidisc Optim 28:275–287CrossRefGoogle Scholar
  71. Vrugt J, Robinson B (2007) Improved evolutionary optimization from genetically adaptive multimethod search. Proc Natl Acad Sci USA 104(3):708CrossRefGoogle Scholar
  72. Ware C (2004) Information visualization: perception for design, 2nd edn. Morgan-KauffmanGoogle Scholar
  73. Winer E, Bloebaum C (2002) Development of visual design steering as an aid in large-scale multidisciplinary design optimization. part i: method development. Struct Multidisc Optim 23(6):412–424CrossRefGoogle Scholar
  74. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRefGoogle Scholar
  75. Woodruff M, Hadka D, Reed P, Simpson T (2012) Auto-adaptive search capabilities of the new borg MOEA: a detailed comparison on alternative product family design problem formulations. In: 14th AIAA/ISSMO multidisciplinary analysis and optimization conference, Indianapolis, IA, USA, 17 September 2012Google Scholar
  76. Zeleny M (1986) Optimal system design with multiple criteria: de novo programming approach. Eng Costs Prod Econ 10(1):89–94MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Matthew J. Woodruff
    • 1
  • Patrick M. Reed
    • 2
  • Timothy W. Simpson
    • 1
  1. 1.Department of Industrial and Manufacturing EngineeringThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of Civil and Environmental EngineeringThe Pennsylvania State UniversityUniversity ParkUSA

Personalised recommendations