n this paper, we propose a genetic algorithm that generates and assesses assembly plans. An appropriately modified version of the well-known partially matched crossover, and purposely defined mutation operators allow the algorithm to produce near-optimal assembly plans starting from a randomly initialised population of (possibly non-feasible) assembly sequences. The quality of a feasible assembly sequence is evaluated based on the following three optimisation criteria: (i) minimising the orientation changes of the product; (ii) minimising the gripper replacements; and (iii) grouping technologically similar assembly operations. Two examples that endorse the soundness of our approach are also included.

A genetic algorithm for generating optimal assembly plans

LAZZERINI, BEATRICE;MARCELLONI, FRANCESCO
2000

Abstract

n this paper, we propose a genetic algorithm that generates and assesses assembly plans. An appropriately modified version of the well-known partially matched crossover, and purposely defined mutation operators allow the algorithm to produce near-optimal assembly plans starting from a randomly initialised population of (possibly non-feasible) assembly sequences. The quality of a feasible assembly sequence is evaluated based on the following three optimisation criteria: (i) minimising the orientation changes of the product; (ii) minimising the gripper replacements; and (iii) grouping technologically similar assembly operations. Two examples that endorse the soundness of our approach are also included.
Lazzerini, Beatrice; Marcelloni, Francesco
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/200044
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