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. In such a collaborative and flexible environment, planning activities are required to identify families of acceptable plans for products which may not be fully detailed, and to promptly provide gross estimates of assembly process performance as the product configuration evolves towards its final form. In this context, different means of encoding an assembly planning problem into a genetic algorithm are compared in terms of reliability and speed in locating acceptable solutions. The different algorithms are tested on a set of artificially generated assembly planning problems and on an industrial case study.
Concurrent Assembly Planning with Genetic Algorithms
SENIN, Nicola;
2002
Abstract
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. In such a collaborative and flexible environment, planning activities are required to identify families of acceptable plans for products which may not be fully detailed, and to promptly provide gross estimates of assembly process performance as the product configuration evolves towards its final form. In this context, different means of encoding an assembly planning problem into a genetic algorithm are compared in terms of reliability and speed in locating acceptable solutions. The different algorithms are tested on a set of artificially generated assembly planning problems and on an industrial case study.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
