Possibilistic Framework for Multi-Objective Optimization Under Uncertainty

  • Oumayma Bahri
  • Nahla Ben Amor
  • El-Ghazali TalbiEmail author
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)


Optimization under uncertainty is an important line of research having today many successful real applications in different areas. Despite its importance, few works on multi-objective optimization under uncertainty exist today. In our study, we address combinatorial multi-objective problem under uncertainty using the possibilistic framework. To this end, we firstly propose new Pareto relations for ranking the generated uncertain solutions in both mono-objective and multi-objective cases. Secondly, we suggest an extension of two well-known Pareto-base evolutionary algorithms namely, SPEA2 and NSGAII. Finally, the extended algorithms are applied to solve a multi-objective Vehicle Routing Problem (VRP) with uncertain demands.


Multi-objective optimization Uncertainty Possibilty theory Evolutionary algorithms Vehicle routing problem 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Oumayma Bahri
    • 1
  • Nahla Ben Amor
    • 2
  • El-Ghazali Talbi
    • 3
    Email author
  1. 1.LARODEC and INRIA Lab.University Lille 1LilleFrance
  2. 2.LARODEC LaboratoryISG TunisLe BardoTunisia
  3. 3.INRIA Laboratory, CRISTAL/CNRSUniversity Lille 1LilleFrance

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