Abstract: Aiming at the randomness and fuzziness，existing in the remanufacturing shop-floor, the fuzzy random variables is applied here to describe the reprocessing time of used parts in order to depict the dual uncertainty of shop-floor conditions; on the foundation of uncertain theory, remanufacturing reprocessing shop scheduling problem model was constructed, based on the fuzzy random chance-constrained, and the hybrid intelligent optimization algorithm was proposed：based on the Arena, using fuzzy random simulation technique to generate input and output data, then, employed particle swarm optimization algorithm to train radial basis function neural network so as to approximate the uncertain functions, after that, embedded the trained neural network into genetic algorithm, which optimized the remanufacturing reprocessing shop scheduling problem; the hybrid intelligent algorithm is approved its effectiveness and feasibility by simulation under the uncertain environment in which the reprocessing time is fuzzy random.

Key words: remanufacturing;dual uncertainty;fuzzy random variables;fuzzy random chance-constrained programming;hybrid intelligent algorithm