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.

在激烈的全球竞争下，传统的大规模流程制造逐渐向定制化离散制造转变。从工程管理的角度来看，生产计划对提高制造效率有着重要的影响。然而，柔性制造系统中的许多与加工无关的因素使得生产计划的制定和实施之间存在差异。我们基于某离散制造企业的调度数据建立了多目标优化模型。为了优化调度模型的局部效果，我们提出了一种改进的局部搜索遗传算法（GALS）。实验结果表明，GALS在调度方案的数量和质量上都远远优于目前的遗传算法调度。与企业目前的调度策略相比，GALS给出的调度策略在最小化完成时间方面平均提高了29.61%，在最小化运输时间方面达到了44.8%，在机器负载均衡方面达到了44.64%。