Weighted Euclidean centers

A. Arbel*, R. Sadka

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This article addresses the derivation of a Euclidean center, which is defined as the point in decision space from which one can inscribe the largest sphere contained by the constraints. We extend this concept by introducing its weighted version, which we term the weighted Euclidean center. We show that by assigning weights to the different decision variables we can traverse the entire decision space. In addition, we show that the concept of a weighted Euclidean center and that of the achievement scalarizing function introduced by Wierzbicki are intimately related.

Original languageEnglish
Pages (from-to)239-251
Number of pages13
Issue number3
StatePublished - Jun 2005


  • Achievement scalarizing function
  • Euclidean centers
  • Multiple-objective linear programming (MOLP)


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