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 evolutionary 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.