Abstract
Traditional large-scale process manufacturing is gradually transformed into customized discrete manufacturing with the fierce global competition. Production planning has an important impact on improving manufacturing efficiency in the ever-changing from the view of engineering management. However, many nonprocessing-related factors in the flexible manufacturing system make it different between the formulation and implementation of the production plan. We established a multi-target optimization model based on the scheduling data of a discrete manufacturing company. In order to optimize the local effect of the scheduling model, we proposed an improved genetic algorithm with local search (GALS). The results of the experiments show that GALS is far superior to the current genetic algorithm scheduling in terms of the number and quality of scheduling solutions. Compared with the current scheduling strategy of the enterprise, the scheduling strategy given by GALS achieved an average improvement of 29.61% in minimizing completion time, achieved 44.8% in minimizing transportation time, and achieved 44.64% in machine load balancing.
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