Genetic Algorithms for multiple objective vehicle routing
  The talk describes a general approach of a genetic algorithm for multiple
objective optimization problems. A particular dominance relation between the
individuals of the population is used to define a fitness operator, enabling
the genetic algorithm to adress even problems with efficient, but
convex-dominated alternatives. The algorithm is implemented in a multilingual
computer program, solving vehicle routing problems with time windows under
multiple objectives. The graphical user interface of the program shows the
progress of the genetic algorithm and the main parameters of the approach can
be easily modified. In addition to that, the program provides powerful decision
support to the decision maker. The software has proved it's excellence at the
finals of the European Academic Software Award EASA, held at the Keble college/
University of Oxford/ Great Britain.
