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NAUTILUS Navigator: free search interactive multiobjective optimization without trading-off

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Abstract

We propose a novel combination of an interactive multiobjective navigation method and a trade-off free way of asking and presenting preference information. The NAUTILUS Navigator is a method that enables the decision maker (DM) to navigate in real time from an inferior solution to the most preferred solution by gaining in all objectives simultaneously as (s)he approaches the Pareto optimal front. This means that, while the DM reaches her/his most preferred solution, (s)he avoids anchoring around the starting solution and, at the same time, sees how the ranges of the reachable objective function values shrink without trading-off. The progress of the motion towards the Pareto optimal front is also shown and, thanks to the graphical user interface, this information is available in an understandable form. The DM provides preference information to direct the movement in terms of desirable aspiration levels for the objective functions, bounds that are not to be exceeded as well as the motion speed. At any time, (s)he can change the navigation direction and even go backwards if needed. One of the major advantages of this method is its applicability to any type of problem, as long as an approximation set of the Pareto optimal front is available and, particularly, to problems with time-consuming function evaluations. Its functionality is demonstrated with an example problem.

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References

  1. Allmendinger, R., Ehrgott, M., Gandibleux, X., Geiger, M.J., Klamroth, K., Luque, M.: Navigation in multiobjective optimization methods. J. Multi Criteria Decis. Anal. 24, 57–70 (2017)

    Article  Google Scholar 

  2. Aloysius, J.A., Davis, F.D., Wilson, D.D., Taylor, A.R., Kottemann, J.E.: User acceptance of multi-criteria decision support systems: the impact of preference elicitation techniques. Eur. J. Oper. Res. 169, 273–285 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  3. Arbel, A., Korhonen, P.: Using apiration levels in an interior primal-dual multiobjective linear programming algorithm. J. Multi Criteria Decis. Anal. 5, 61–71 (1996)

    Article  MATH  Google Scholar 

  4. Buchanan, J.Y., Corner, J.: The effects of anchoring in interactive MCDM solution methods. Comput. Oper. Res. 24(10), 907–918 (1997)

    Article  MATH  Google Scholar 

  5. Chankong, V., Haimes, Y.Y.: Multiobjective Decision Making Theory and Methodology. Elsevier, New York (1983)

    MATH  Google Scholar 

  6. Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007)

    MATH  Google Scholar 

  7. Cuate, O., Lara, A., Schutze, O.: A local exploration tool for linear many objective optimization problems. In: 13th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) pp. 1–6 (2016)

  8. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  9. Eskelinen, P., Miettinen, K., Klamroth, K., Hakanen, J.: Pareto Navigator for interactive nonlinear multiobjective optimization. OR Spectr. 32(1), 211–227 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  10. Gold, J.I., Shadlen, M.N.: The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007)

    Article  Google Scholar 

  11. Hartikainen, M., Miettinen, K., Klamroth, K.: Interactive Nonconvex Pareto Navigator for multiobjective optimization. Eur. J. Oper. Res. 275(1), 238–251 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hwang, C.L., Masud, A.S.M.: Multiple Objective Decision Making—Methods and Applications: A State-of-the-Art Survey. Springer, Berlin (1979)

    Book  MATH  Google Scholar 

  13. Janis, I.L., Mann, L.: Decision Making: A Psychological Analysis of Conflict, Choice and Commitment. The Free Press, New York (1977)

    Google Scholar 

  14. Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica 47(2), 263–291 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  15. Korhonen, P., Wallenius, J.: A Pareto race. Nav. Res. Logist. 35(6), 615–623 (1988)

    Article  MATH  Google Scholar 

  16. Korhonen, P., Wallenius, J.: Behavioural issues in MCDM: neglected research questions. J. Multi Criteria Decis. Anal. 5, 178–182 (1996)

    Article  MATH  Google Scholar 

  17. Lin, K.M., Ehrgott, M.: Multiobjective navigation of external radiotherapy plans based on clinical criteria. J. Multi Criteria Decis. Anal. 25, 31–41 (2018)

    Article  Google Scholar 

  18. Luque, M., Miettinen, K., Eskelinen, P., Ruiz, F.: Incorporating preference information in interactive reference point methods for multiobjective optimization. Omega 37(2), 450–462 (2009)

    Article  Google Scholar 

  19. Meignan, D., Knust, S., Frayret, J.M., Pesant, G., Gaud, N.: A review and taxonomy of interactive optimization methods in operations research. ACM Trans. Interact. Intell. Syst. 5(3), 17:1–17:43 (2015)

    Article  Google Scholar 

  20. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston (1999)

    MATH  Google Scholar 

  21. Miettinen, K., Eskelinen, P., Ruiz, F., Luque, M.: NAUTILUS method: an interactive technique in multiobjective optimization based on the nadir point. Eur. J. Oper. Res. 206(2), 426–434 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  22. Miettinen, K., Podkopaev, D., Ruiz, F., Luque, M.: A new preference handling technique for interactive multiobjective optimization without trading-off. J. Glob. Optim. 63(4), 633–652 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  23. Miettinen, K., Ruiz, F.: NAUTILUS framework: towards trade-off-free interaction in multiobjective optimization. J. Bus. Econ. 86(1), 5–21 (2016)

    Article  Google Scholar 

  24. Monz, M., Kufer, K.H., Bortfeld, T.R., Thieke, C.: Pareto navigation—algorithmic foundation of interactive multi-criteria IMRT planning. Phys. Med. Biol. 53(4), 985–998 (2008)

    Article  Google Scholar 

  25. Munzner, T.: Process and pitfalls in writing information visualization research papers. In: Kerren, A., Stasko, J.T., Fekete, J.D., North, C. (eds.) Information Visualization: Human-Centered Issues and Perspectives, pp. 134–153. Springer, Berlin (2008)

    Chapter  Google Scholar 

  26. Nielsen, R.P.: Varieties of win-win solutions to problems with ethical dimensions. J. Bus. Ethics 88(2), 333–349 (2009)

    Article  Google Scholar 

  27. Raiffa, H., Richardson, J., Metcalfe, D.: Negotiation Analysis: The Science and Art of Collaborative Decision Making. Harvard University Press, Cambridge (2002)

    Google Scholar 

  28. Rangel, A., Camerer, C., Montague, P.R.: A framework for studying the neurobiology of value-based decision making. Neuroscience 9, 545–556 (2008)

    Google Scholar 

  29. Ravaja, N., Korhonen, P., Köksalan, M., Lipsanen, J., Salminen, M., Somervuori, O., Wallenius, J.: Emotional–motivational responses predicting choices: the role of asymmetrical frontal cortical activity. J. Econ. Psychol. 52, 56–70 (2016)

    Article  Google Scholar 

  30. Ruiz, A.B., Luque, M., Ruiz, F., Saborido, R.: A combined interactive procedure using preference-based evolutionary multiobjective optimization application to the efficiency improvement of the auxiliary services of power plants. Exp. Syst. Appl. 42(1), 7466–7482 (2015)

    Article  Google Scholar 

  31. Ruiz, A.B., Sindhya, K., Miettinen, K., Ruiz, F., Luque, M.: E-NAUTILUS: a decision support system for complex multiobjective optimization problems based on the NAUTILUS method. Eur. J. Oper. Res. 246(1), 218–231 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  32. Ruzika, S., Wiecek, M.M.: Approximation methods in multiobjective programming. J. Optim. Theory Appl. 126(3), 473–501 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  33. Steuer, R.E.: Multiple Criteria Optimization: Theory, Computation and Application. Wiley, New York (1986)

    MATH  Google Scholar 

  34. Stewart, T., Bandte, O., Braun, H., Chakraborti, N., Ehrgott, M., Gobelt, M., Jin, Y., Nakayama, H., Poles, S., Di Stefano, D.: Real-world applications of multiobjective optimization. In: Branke, J., Deb, K., Miettinen, K., Slowinski, R. (eds.) Multiobjective Optimization. Interactive and Evolutionary Approaches, pp. 285–327. Springer, Berlin (2008)

    Chapter  Google Scholar 

  35. Szczepanski, M., Wierzbicki, A.P.: Application of multiple criteria evolutionary algorithm to vector optimization, decision support and reference-point approaches. J. Telecommun. Inf. Technol. 3(3), 16–33 (2003)

    Google Scholar 

  36. Tarkkanen, S., Miettinen, K., Hakanen, J., Isomäki, H.: Incremental user-interface development for interactive multiobjective optimization. Exp. Syst. Appl. 40(8), 3220–3232 (2013)

    Article  Google Scholar 

  37. Trinkaus, H.L., Hanne, T.: knowCube: a visual and interactive support for multicriteria decision making. Comput. Oper. Res. 32(5), 1289–1309 (2005)

    Article  MATH  Google Scholar 

  38. Wierzbicki, A.P.: The use of reference objectives in multiobjective optimization. In: Gal, T. (ed.) Multiple Criteria Decision Making, Theory and Applications, pp. 468–486. Springer, Berlin (1980)

    Chapter  Google Scholar 

  39. Worthy, D.A., Gorlickand, M.A., Pacheco, J.L., Schnyer, D.M., Maddox, W.T.: With age comes wisdom: decision making in younger and older adults. Psychol. Sci. 22(11), 1375–1380 (2011)

    Article  Google Scholar 

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Acknowledgements

This work was partially financed by the Academy of Finland (Project 287496) and the Spanish Ministry of Economy and Competitiveness (Projects ECO2016-76567-C4-4-R and ECO2014-56397-P). Ana B. Ruiz would like to thank the University of Málaga for the financial support received (post-doctoral fellowship “Captación de Talento para la Investigación”). This research is related to the thematic research area Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO) of the University of Jyvaskyla. This work was supported by the research project ECO2013-47129-C4-2-R of Universidad de Málaga granted by the Spanish Ministry of Science and Innovation, where Ms. Laura Delgado-Antequera is also part of, via the fellowship BES-2014-068507.

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Ruiz, A.B., Ruiz, F., Miettinen, K. et al. NAUTILUS Navigator: free search interactive multiobjective optimization without trading-off. J Glob Optim 74, 213–231 (2019). https://doi.org/10.1007/s10898-019-00765-2

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