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Multi-Objective Optimization: Methods and Applications

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The Palgrave Handbook of Operations Research

Abstract

Multi-objective optimization is concerned with finding solutions to a decision problem with multiple, normally conflicting objectives. This chapter focusses on multi-objective optimization problems that can be characterized within the paradigm of mathematical programming. Three modelling techniques that are well established in the literature are presented: Pareto set generation, goal programming and compromise programming. Each method is described, along with its strengths, weaknesses and areas of application. The underlying assumptions and philosophies of each method, nature of interaction of decision makers and nature of solutions produced is discussed and compared between the three methods. A small but representative example is given for each method and the results are discussed and conclusions are drawn.

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Acknowledgements

The authors would like to thank the following Brazilian grant-making organizations: CNPq (grant 312551/2019-3), FAPESP (grant 2013/07375-0), CAPES (Finance Code 001), UNESP-PROPE and UNESP-FUNDUNESP.

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Authors and Affiliations

  1. Centre for Operational Research and Logistics, School of Mathematics and Physics, University of Portsmouth, Portsmouth, UK

    Dylan F. Jones

  2. Institute of Biosciences, São Paulo State University (Unesp), Botucatu, Brazil

    Helenice O. Florentino

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  1. Dylan F. Jones
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  2. Helenice O. Florentino
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Correspondence to Dylan F. Jones .

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Editors and Affiliations

  1. Kent Business School, University of Kent, Canterbury, UK

    Saïd Salhi

  2. Lancaster University, Lancaster, UK

    John Boylan

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Jones, D.F., Florentino, H.O. (2022). Multi-Objective Optimization: Methods and Applications. In: Salhi, S., Boylan, J. (eds) The Palgrave Handbook of Operations Research . Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-96935-6_6

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