Randomised Variable Neighbourhood Search for Multi Objective
  Optimisation
  Various local search approaches have recently been applied to machine
scheduling problems under multiple objectives. Their foremost consideration is
the identification of the set of Pareto optimal alternatives. An important
aspect of successfully solving these problems lies in the definition of an
appropriate neighbourhood structure. Unclear in this context remains, how
interdependencies within the fitness landscape affect the resolution of the
problem.
  The paper presents a study of neighbourhood search operators for multiple
objective flow shop scheduling. Experiments have been carried out with twelve
different combinations of criteria. To derive exact conclusions, small problem
instances, for which the optimal solutions are known, have been chosen.
Statistical tests show that no single neighbourhood operator is able to equally
identify all Pareto optimal alternatives. Significant improvements however have
been obtained by hybridising the solution algorithm using a randomised variable
neighbourhood search technique.
