Concurrent assembly planning with genetic algorithms

This work investigates the application of genetic algorithm (GA)-based search techniques to concurrent assembly planning, where product design and assembly process planning are performed in parallel, and the evaluation of a design configuration is influenced by the performance of its related assembly process. Several types of GAs and an exhaustive combinatorial approach are compared, in terms of reliability and speed in locating the global optimum. The different algorithms are tested first on a set of artificially generated assembly planning problems, which are intended to represent a broad spectrum of combinatorial complexity; then an industrial case study is presented. Test problems indicate that GAs are slightly less reliable than the combinatorial approach in finding the global, but are capable of identifying solutions which are very close to the global optimum with consistency, soon outperforming the combinatorial approach in terms of execution times, as the problem complexity grows. For an industrial case study of low combinatorial complexity, such as the one chosen in this work, GAs and combinatorial approach perform almost equivalently, both in terms of reliability and speed. In summary, GAs seem a suitable choice for those planning applications where response time is an important factor, and results which are close enough to the global optimum are still considered acceptable such as in concurrent assembly planning, where response time is a key factor when assessing the validity of a product design configuration in terms of the performance of its assembly plan.