A parallel genetic algorithm for solving the container loading problem

This paper presents a parallel genetic algorithm (PGA) for the container loading problem with a single container to be loaded. The emphasis is on the case of a strongly heterogeneous load. The PGA follows a migration model. Several separate sub-populations are subjected to an evolutinary process independently of each other. At the same time the best individuals are exchanged between the sub-populations. The evolution of the different sub-populations is carried out on a corresponding number of LAN workstations. The quality of the PGA is demonstrated by an extensive comparative test including well-known reference problems and loading procedures from other authors.