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中文摘要：
为了使制造系统的任务分配决策能动态地响应突变环境的变化，使资源得到优化配置，达到制造系统运行的整体优化，以最小化数学模型为目标，将基于神经-内分泌-免疫系统调节机制的具有有效避免近亲繁殖、无需复制操作、有效克服早熟现象和进化缓慢等特点的改进型自适应遗传算法应用到其中，进行响应以得到优化分配方案。经实验验证，文中所述的改进型自适应遗传算法与传统遗传算法相比，在收敛效率和准确性等方面都有很大的改进和提高。
英文摘要：
In order to deal dynamically with the environmental changes for decision-making of task distribution in manufacturing systems and optimize resource collocation for the global optimization of manufacturing systems, a minimal mathematical model was established, and then an improved adaptive genetic algorithm（IAGA） based on neuro-endocrine-immunity system（NEIS） was applied to the task distribution in manufacturing systems. This algorithm can avoid inbreeding, does not need reproductive operation, and can overcome the shortcomings of premature and slow evolution. A machine-based encoding is applied to the IAGA and a satisfactory distribution scheme is obtained. The experimental results indicate that the proposed IAGA is better than traditional genetic algorithms in convergent efficiency and accuracy.
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