Line-based optimization of LTL-shipments using a multi-step genetic algorithm

Motivated by the so-called “Cloud Logistic”-concept as an innovative, line-based way for dealing with less than truck load (LTL) shipments in cooperation networks, this paper introduces a genetic algorithm as a heuristical approach for dealing with multi-objective optimization problems. Based on the implied optimization problem - the NP-hard multi-depot heterogeneous fleet vehicle routing problem with time windows and assignment restrictions (m-VRPTWAR) - four different optimization goals of the “CloudLogistic”-concept are introduced and a multi-step approach is motivated. Therefore, two different optimization steps are presented and transferred into a genetic algorithm. Additionally, two innovative problem-specific genetic operators are introduced by combining a generation-based approach and a usage-based approach in order to create a useful mutation process. A further usage-based approach is used to realize a problem-specific crossover operator. The presented genetic multi-step approach is a useful concept for dealing with multi-objective optimization problems without the need of a single combined fitness function.