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Multi-scenario Multi-objective Optimization with Applications in Engineering Design

In: Multiobjective Programming and Goal Programming

Author

Listed:
  • Margaret M. Wiecek

    (Clemson University)

  • Vincent Y. Blouin

    (Clemson University)

  • Georges M. Fadel

    (Clemson University)

  • Alexander Engau

    (University of Waterloo)

  • Brian J. Hunt

    (Clemson University)

  • Vijay Singh

    (Clemson University)

Abstract

The notion of multi-scenario multi-objective optimization is proposed as a methodological framework for handling engineering design and other decision problems represented as a collection of multi-criteria optimization problems. Three specific research issues are discussed in this context, namely, the modelling of decision maker's preferences, the development of a concept of optimality, and the development of solution approaches to finding a preferred feasible solution for the overall problem. Two models of preferences that generalize the classical Pareto preference and two solution approaches to a class of multi-scenario multi-objective optimization problems are presented. Illustrative examples are included.

Suggested Citation

  • Margaret M. Wiecek & Vincent Y. Blouin & Georges M. Fadel & Alexander Engau & Brian J. Hunt & Vijay Singh, 2009. "Multi-scenario Multi-objective Optimization with Applications in Engineering Design," Lecture Notes in Economics and Mathematical Systems, in: Vincent Barichard & Matthias Ehrgott & Xavier Gandibleux & Vincent T'Kindt (ed.), Multiobjective Programming and Goal Programming, pages 283-298, Springer.
    • Handle: RePEc:spr:lnechp:978-3-540-85646-7_26
    DOI: 10.1007/978-3-540-85646-7_26
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    Citations

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    Cited by:

    1. Yue Zhou-Kangas & Kaisa Miettinen & Karthik Sindhya, 2019. "Solving multiobjective optimization problems with decision uncertainty: an interactive approach," Journal of Business Economics, Springer, vol. 89(1), pages 25-51, February.
    2. Botte, Marco & Schöbel, Anita, 2019. "Dominance for multi-objective robust optimization concepts," European Journal of Operational Research, Elsevier, vol. 273(2), pages 430-440.
    3. Ridha, Hussein Mohammed & Gomes, Chandima & Hizam, Hashim & Mirjalili, Seyedali, 2020. "Multiple scenarios multi-objective salp swarm optimization for sizing of standalone photovoltaic system," Renewable Energy, Elsevier, vol. 153(C), pages 1330-1345.
    4. Hunt, Brian J. & Wiecek, Margaret M. & Hughes, Colleen S., 2010. "Relative importance of criteria in multiobjective programming: A cone-based approach," European Journal of Operational Research, Elsevier, vol. 207(2), pages 936-945, December.

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