[PDF][PDF] An improved method for solving multiobjective integer linear fractional programming problem
MA Mehdi, MEA Chergui… - Advances in Decision …, 2014 - pdfs.semanticscholar.org
MA Mehdi, MEA Chergui, M Abbas
Advances in Decision Sciences, 2014•pdfs.semanticscholar.orgWe describe an improvement of Chergui and Moulaı's method (2008) that generates the
whole efficient set of a multiobjective integer linear fractional program based on the branch
and cut concept. The general step of this method consists in optimizing (maximizing without
loss of generality) one of the fractional objective functions over a subset of the original
continuous feasible set; then if necessary, a branching process is carried out until obtaining
an integer feasible solution. At this stage, an efficient cut is built from the criteria's growth …
whole efficient set of a multiobjective integer linear fractional program based on the branch
and cut concept. The general step of this method consists in optimizing (maximizing without
loss of generality) one of the fractional objective functions over a subset of the original
continuous feasible set; then if necessary, a branching process is carried out until obtaining
an integer feasible solution. At this stage, an efficient cut is built from the criteria's growth …
We describe an improvement of Chergui and Moulaı’s method (2008) that generates the whole efficient set of a multiobjective integer linear fractional program based on the branch and cut concept. The general step of this method consists in optimizing (maximizing without loss of generality) one of the fractional objective functions over a subset of the original continuous feasible set; then if necessary, a branching process is carried out until obtaining an integer feasible solution. At this stage, an efficient cut is built from the criteria’s growth directions in order to discard a part of the feasible domain containing only nonefficient solutions. Our contribution concerns firstly the optimization process where a linear program that we define later will be solved at each step rather than a fractional linear program. Secondly, local ideal and nadir points will be used as bounds to prune some branches leading to nonefficient solutions. The computational experiments show that the new method outperforms the old one in all the treated instances.
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