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Interactive Multiobjective Optimization from a Learning Perspective

  • Chapter
Multiobjective Optimization

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5252))

Abstract

Learning is inherently connected with Interactive Multiobjective Optimization (IMO), therefore, a systematic analysis of IMO from the learning perspective is worthwhile. After an introduction to the nature and the interest of learning within IMO, we consider two complementary aspects of learning: individual learning, i.e., what the decision maker can learn, and model or machine learning, i.e., what the formal model can learn in the course of an IMO procedure. Finally, we discuss how one might investigate learning experimentally, in order to understand how to better support decision makers. Experiments involving a human decision maker or a virtual decision maker are considered.

Reviewed by: Kalyanmoy Deb, Indian Institute of Technology Kanpur, India; Kaisa Miettinen, University of Jyväskylä, Finland

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References

  • Aksoy, Y., Butler, T.W., Minor, E.D.: Comparative studies in interactive multiple objective mathematical programming. European Journal of Operational Research 89(2), 408–422 (1996)

    Article  MATH  Google Scholar 

  • Argyris, C., Schon, D.A.: Organisational Learning: A Theory of Action and Perspective. Addison-Wesley, Reading (1978)

    Google Scholar 

  • Belton, V., Elder, M.D.: Decision support systems: Learning from visual interactive modelling. Decision support systems 12, 355–364 (1994)

    Article  Google Scholar 

  • Belton, V., Stewart, T.J.: Muliple Criteria Decision Analysis: An Integrated Approach. Kluwer, Boston (2002)

    Book  Google Scholar 

  • Belton, V., Vickers, S.P.: V.I.S.A - VIM for MCDA. In: Lockett, G., Islei, G. (eds.) Improving Decision Making in Organizations, pp. 287–304. Springer, Berlin (1989)

    Chapter  Google Scholar 

  • Benayoun, R., de Montgolfier, J., Tergny, J., Larichev, O.: Linear programming with multiple objective functions: STEP method (STEM). Mathematical Programming 1, 366–375 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  • Buchanan, J.T., Corner, J.L.: The effects of anchoring in interactive MCDM solution methods. Computers and Operations Research 24(10), 907–918 (1997)

    Article  MATH  Google Scholar 

  • Buchanan, J.T., Daellenbach, H.G.: Comparative evaluation of interactive solution methods for multiple objective decision models. European Journal of Operational Research 29, 353–359 (1987)

    Article  Google Scholar 

  • Buchanan, J.T., Gardiner, L.: A comparison of two reference point methods in multiple objective mathematical programming. European Journal of Operational Research 149, 17–34 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  • Caballero, R., Luque, M., Molina, J., Ruiz, F.: PROMOIN: an interactive system for multiobjective programming. Information Technologies and Decision Making 1, 635–656 (2002)

    Article  Google Scholar 

  • Chankong, V., Haimes, Y.Y.: Multiobjective Decision Making Theory and Methodology. Elsevier Science Publishing Co., New York (1983)

    MATH  Google Scholar 

  • Chankong, V., Haimes, Y.Y.: The interactive surrogate worth trade-off (ISWT) method for multiobjective decision-making. In: Zionts, S. (ed.) Multiple Criteria Problem Solving, pp. 42–67. Springer, Berlin (1987)

    Google Scholar 

  • CIA: The 2008 World Factbook. Central Intelligence Agency (2008), https://www.cia.gov/library/publications/the-world-factbook/index.html

  • Dunn, R., Dunn, K., Price, G.E.: Learning Style Inventory. Price Systems, Lawrence, KS (1984)

    Google Scholar 

  • Dyer, J.S.: An empirical investigation of a man-machine interactive approach to the solution of the multiple criteria problem. In: Cochrane, J., Zeleny, M. (eds.) Multiple Criteria Decision Making, pp. 202–216. University of South Carolina Press, Columbia (1973)

    Google Scholar 

  • Dyer, J.S., Fishburn, P.C., Steuer, R.E., Wallenius, J., Zionts, S.: Multiple criteria decision making, multiattribute utility theory: The next 10 years. Management Science 38, 645–654 (1992)

    Article  MATH  Google Scholar 

  • Easterby-Smith, M., Thorpe, R., Lowe, A.: Management Research: an Introduction. Sage, London (2002)

    Google Scholar 

  • Eschenauer, H.E., Osyczka, A., Schäfer, E.: Interactive multicriteria optimisation in design process. In: Eschenauer, H., Koski, J., Osyczka, A. (eds.) Multicriteria Design Optimization Procedures and Applications, pp. 71–114. Springer, Berlin (2000)

    Google Scholar 

  • Figueira, J., Greco, S., Słowiński, R.: Building a set of additive value functions representing a reference preorder and intensities of preference: GRIP method. European Journal of Operational Research. To appear (2008)

    Google Scholar 

  • Fishburn, P.C.: Methods of estimating additive utilities. Management Science 13(7), 435–453 (1967)

    Article  Google Scholar 

  • Gardner, H.: Frames of Mind: The Theory of Multiple Intelligences. Basic Books, New York (1993)

    Google Scholar 

  • Geoffrion, A., Dyer, J., Feinberg, A.: An interactive approach for multi-criterion optimization, with an application to the operation of an academic department. Management Science 19(4), 357–368 (1972)

    Article  MATH  Google Scholar 

  • Gibson, M., Bernardo, J.J., Cheng, C., Badinelli, R.: A comparison of interactive multiple-objective decision making procedures. Computers and Operations Research 50(1), 97–105 (1987)

    Article  MATH  Google Scholar 

  • Greco, S., Matarazzo, B., Słowiński, R.: Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129(1), 1–47 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  • Greco, S., Matarazzo, B., Słowiński, R.: Decision rule approach. In: Figueira, J., Greco, S., Ehrgott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 507–563. Springer, Berlin (2005)

    Chapter  Google Scholar 

  • Greco, S., Mousseau, V., Słowiński, R.: Ordinal regression revisited: Multiple criteria ranking using a set of additive value functions. European Journal of Operational Research 191(2), 415–435 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  • Hobbs, B.F.: What can we learn from experiments in multiobjective decision analysis? IEEE Transactions on Systems, Man, and Cybernetics 3, 384–394 (1986)

    Article  MathSciNet  Google Scholar 

  • Huber, J., Payne, J.W., Puto, C.: Adding asymmetrically dominated alternatives:violations of regularity and the similarity hypothesis. Journal of Consumer Research 9, 90–98 (1982)

    Article  Google Scholar 

  • Jacquet-Lagrèze, E.: Systèmes de décision et acteurs multipies: Contribution à une théorie de l’action pour les sciences des organisations. Thèse d’Etat, Université de Paris-Dauphine, Paris (1981)

    Google Scholar 

  • Jacquet-Lagrèze, E., Siskos, Y.: Assessing a set of additive utility functions for multicriteria decision making: the UTA method. European Journal of Operational Research 10(2), 151–164 (1982)

    Article  MATH  Google Scholar 

  • Jacquet-Lagrèze, E., Meziani, R., Słowiński, R.: MOLP with an interactive assessment of a piecewise-linear utility function. European Journal of Operational Research 31(3), 350–357 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  • Jaszkiewicz, A., Słowiński, R.: The ’Light Beam Search’ approach - an overview of methodology and applications. European Journal of Operational Research 113(2), 300–314 (1999)

    Article  MATH  Google Scholar 

  • Kahneman, D., Slovic, P., Tversky, A. (eds.): Judgment under Uncertainty. Cambridge University Press, Cambridge (1982)

    Google Scholar 

  • Keeney, R.: Value Focussed Thinking: A Path to Creative Decision Making. Harvard University Press, Cambridge (1992)

    Google Scholar 

  • Keeney, R.L., Raiffa, H., Hammond, J.S.: The hidden traps in decision making. In: Harvard Business Review Online (2006)

    Google Scholar 

  • Kolb, D.: Experiential Learning: Experience as the Source of Learning. Prentice-Hall, Englewood Cliffs (1984)

    Google Scholar 

  • Kolb, D., Fry, R.: Toward an applied theory of experiential learning. In: Cooper, C.L. (ed.) Theories of Group Processes, pp. 27–56. John Wiley, London (1975)

    Google Scholar 

  • Korhonen, P.: VIG - a visual interactive support system for multiple criteria decision making. JORBEL 27(1), 4–15 (1987)

    Google Scholar 

  • Korhonen, P., Wallenius, J.: Behavioural issues in MCDM: Neglected research questions. Journal of Multi-Criteria Decision Analysis 5, 178–182 (1996)

    Article  MATH  Google Scholar 

  • Larichev, O.I.: Cognitive validity in design of decision-aiding techniques. Journal of Multi-Criteria Decision Analysis 1, 127–138 (1992)

    Article  MATH  Google Scholar 

  • Larichev, O.I., Polyakov, O.A., Nikiforov, A.D.: Multicriterion linear programming problems - analytical survey. Journal of Economic Psychology 8, 389–407 (1987)

    Article  Google Scholar 

  • Lave, J., Wenger, E.: Situated Learning. Legitimate peripheral participation. University of Cambridge Press, Cambridge (1991)

    Book  Google Scholar 

  • March, J.G.: Bounded rationality, ambiguity, and the engineering of choice. The Bell Journal of Economics 9(2), 587–608 (1978)

    Article  Google Scholar 

  • March, J.G.: The technology of foolishness. In: March, J.G. (ed.) Decisions and Organizations, pp. 253–265. Basil Blackwell, New York (1988)

    Google Scholar 

  • Merriam, S.B., Caffarella, R.S.: Learning in Adulthood. A comprehensive guide. Jossey-Bass, San Francisco (1991)

    Google Scholar 

  • Mezirow, J.: Transformative Dimensions of Adult Learning. Jossey-Bass, San Francisco (1991)

    Google Scholar 

  • Michalski, R.S., Bratko, I., Kubat, M.: Machine Learning and Data Mining - Methods and Applications. Wiley, New York (1998)

    Google Scholar 

  • Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer, Boston (1999)

    MATH  Google Scholar 

  • Miettinen, K., Mäkelä, M.M.: Comparative evaluation of some interactive reference point-based methods for multi-objective optimisation. Journal of the Operational Research Society 50, 949–959 (1999)

    Article  MATH  Google Scholar 

  • Miettinen, K., Mäkelä, M.M.: Synchronous approach in interactive multiobjective optimization. European Journal of Operational Research 170(3), 900–922 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  • Miettinen, K., Mäkelä, M.M., Kaario, K.: Experiments with classification-based scalarizing functions in interactive multiobjective optimization. European Journal of Operational Research 29, 931–947 (2006)

    Article  MATH  Google Scholar 

  • Montibeller, G.: Action researching multiple criteria decision analysis interventions. In: Shaw, D. (ed.) 49th Operational Research Society Conference Keynote Papers (2007), http://personal.lse.ac.uk/MONTIBEL/OR49_Action_researching_MCDA.pdf

  • Morecroft, J.D.W., Sterman, J.D.: Modelling for Learning. North Holland, Amsterdam (1992)

    Google Scholar 

  • Morton, A., Fasolo, B.: Behavioural decision theory for multi-criteria decision analysis: a guided tour. In: Journal of the Operational Research Society. To appear (2008), doi:10.1057/palgrave.jors.2602550

    Google Scholar 

  • Mote, J., Olson, D.L., Venkataramanan, M.A.: A comparative multiobjective programming study. Mathematical and Computer Modelling 10(10), 719–729 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  • Narula, S.C., Kirilov, L., Vassilev, V.: Reference direction approach for solving multiple objective nonlinear programming problems. IEEE Transactions on Systems, Man, and Cybernetics 24, 804–806 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  • Olson, D.L.: Review of empirical studies in multiobjective mathematical programming: Subject reflection of nonlinear utility and learning. Decision Sciences 23, 1–20 (1992)

    Article  Google Scholar 

  • Papamichail, K.N., French, S.: Explaining and justifying the advice of a decision support system: a natural language generation approach. Expert Systems with Applications 24(1), 35–48 (2003)

    Article  Google Scholar 

  • Reason, P., Bradbury, H.: Handbook of Action Research. Sage, London (2001)

    Google Scholar 

  • Reeves, G.R., Gonzalez, J.J.: A comparison of two interactive MCDM procedures. European Journal of Operational Research 41(2), 203–209 (1989)

    Article  MATH  Google Scholar 

  • Robson, C.: Real World Research: a Resource for Social Scientists and Practitioner Researchers, 2nd edn. Blackwell, Oxford (1993)

    Google Scholar 

  • Roy, B.: Decision science or decision-aid science. European Journal of Operational Research 66(2), 184–203 (1993)

    Article  Google Scholar 

  • Russell, S.J., Norvig, P.: Artificial Intelligence: a Modern Approach. Prentice Hall, Upper Saddle River (2003)

    MATH  Google Scholar 

  • Shackelford, M.R.N., Corne, D.W.: A technique for evaluation of interactive evolutionary systems. In: Parmee, I.C. (ed.) Proceedings of the 6th International Conference on Adaptive Computing in Design and Manufacture (ACDM 2004), pp. 197–208. Springer, Heidelberg (2004)

    Google Scholar 

  • Słowinński, R., Greco, S., Matarazzo, B.: Rough set based decision support. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 475–527. Springer, New York (2005)

    Google Scholar 

  • Steuer, R.E.: Multiple Criteria Optimization: Theory, Computation, and Applications. John Wiley & Sons, New York (1985)

    Google Scholar 

  • Steuer, R.E., Choo, E.U.: An interactive weighted tchebycheff procedure for multiple objective programming. Mathematical Programming 26, 326–344 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  • Tversky, A., Kahneman, D.: Loss aversion in riskless choice: a reference-dependent model. The Quarterly Journal of Economics 106, 1039–1061 (1991)

    Article  Google Scholar 

  • Vanderpooten, D.: The interactive approach in MCDA a conceptual framework and some basic conceptions. Mathematical and Computer Modelling 12, 1213–1220 (1990)

    Article  Google Scholar 

  • Vanderpooten, D., Vincke, P.: Description and analysis of some representative interactive multicriteria procedures. Mathematical and Computer Modelling 12, 1221–1238 (1989)

    Article  Google Scholar 

  • von Winterfeldt, D., Edwards, W.: Decision Analysis and Behavioral Research. Cambridge University Press, Cambridge (1982)

    Google Scholar 

  • Wallenius, J.: Comparative evaluation of some interactive approaches to multicriterion optimization. Management Science 21(2), 1387–1396 (1975)

    Article  MATH  Google Scholar 

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

    Chapter  Google Scholar 

  • Zanakis, S.H., Solomon, A., Wishart, N., Dublish, S.: Multi-attribute decision making: A simulation comparison of selected methods. European Journal of Operational Research 107(3), 507–529 (1998)

    Article  MATH  Google Scholar 

  • Zionts, S., Wallenius, J.: An interactive programming method for solving the multiple criteria problem. Management Science 22, 652–663 (1976)

    Article  MATH  Google Scholar 

  • Zionts, S., Wallenius, J.: An interactive multiobjective linear programming method for a class of underlying nonlinear utility functions. Management Science 29, 519–529 (1983)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Management Science, University of Strathclyde, 40 George Street, Glasgow, UK, G1 1QE

    Valerie Belton

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Helsinki School of Economics, P.O. Box 1210, FI-00101, Helsinki, Finland

    Petri Eskelinen

  4. Faculty of Economics, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Salvatore Greco

  5. Department of Applied Economics (Mathematics), University of Málaga, Calle Ejido 6, E-29071, Málaga, Spain

    Julián Molina & Francisco Ruiz

  6. Institute of Computing Science, Poznań University of Technology, 60-965, Poznań, Poland

    Roman Słowiński

  7. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

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  1. Valerie Belton
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  2. Jürgen Branke
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  3. Petri Eskelinen
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  4. Salvatore Greco
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  5. Julián Molina
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  6. Francisco Ruiz
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  7. Roman Słowiński
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Editor information

Editors and Affiliations

  1. Institute AIFB, University of Karlsruhe, Germany

    Jürgen Branke

  2. Department of Business Technology, Helsinki School of Economics, P.O. Box 1210, FI-00101, Helsinki, Finland

    Kalyanmoy Deb

  3. Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35, (Agora), FI-40014, Finland

    Kaisa Miettinen

  4. Institute of Computing Science Poznan, University of Technology, 60-965, Poznan, Poland

    Roman Słowiński

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Belton, V. et al. (2008). Interactive Multiobjective Optimization from a Learning Perspective. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds) Multiobjective Optimization. Lecture Notes in Computer Science, vol 5252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88908-3_15

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  • DOI: https://doi.org/10.1007/978-3-540-88908-3_15

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