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A Multi-objective Genetic Optimization Technique for the Strategic Design of Distribution Networks

  • Vitoantonio Bevilacqua
  • Mariagrazia Dotoli
  • Marco Falagario
  • Fabio Sciancalepore
  • Dario D’Ambruoso
  • Stefano Saladino
  • Rocco Scaramuzzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6839)

Abstract

We address the optimal design of a Distribution Network (DN), presenting a procedure employing Multi-Objective Genetic Algorithms (MOGA) to select the (sub) optimal DN configuration. Using multi-objective genetic optimization allows solving a nonlinear design problem with piecewise constant contributions in addition to linear ones. The MOGA application allows finding a Pareto frontier of (sub) optimal solutions, which is compared with the frontier obtained solving the same problem with linear programming, where piecewise constant contributions are linearly approximated. The two curves represent, respectively, the upper and the lower limit of the region including the real Pareto curve. Both the genetic optimization model and the linear programming are applied under structural constraints to a case study describing the DN of an Italian enterprise.

Keywords

Supply Chain Pareto Frontier Pareto Curve Objective Genetic Algorithm Supply Chain Strateg 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Vitoantonio Bevilacqua
    • 1
  • Mariagrazia Dotoli
    • 1
  • Marco Falagario
    • 2
  • Fabio Sciancalepore
    • 2
  • Dario D’Ambruoso
    • 1
  • Stefano Saladino
    • 1
  • Rocco Scaramuzzi
    • 1
  1. 1.Dipartimento di Elettrotecnica ed ElettronicaPolitecnico di BariItaly
  2. 2.Dipartimento di Ingegneria Meccanica e GestionalePolitecnico di BariItaly

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