An adaptive genetic assembly-sequence planner

Assembly sequence planning is a combinatorial optimization problem with highly nonlinear geometric constraints. Most proposed solution methodologies are based on graph theory and involve complex geometric and physical analyses. As a result, even for a simple structure, it is difficult to take all important criteria into account and find real-world solutions. This paper proposes an adaptive genetic algorithm (AGA) for efficiently finding global-optimal or near-global optimal assembly sequences. The difference between an adaptive genetic algorithm and a classical genetic algorithm is that genetic-operator probabilities for an adaptive genetic algorithm are varied according to certain rules, but genetic-operator probabilities for a classical genetic algorithm are fixed. For our AGA, we build a simulation function to preestimate our GA search process, use our simulation function to calculate optimal genetic-operator probability settings for a given structure, and then use our calculated genetic-operator probability settings to dynamically optimize our AGA search for an optimal assembly sequence. Experimental results show that our adaptive genetic assembly-sequence planner solves combinatorial assembly problems quickly, reliably, and accurately.