作为一类先进自动化制造系统，自动化制造单元因其在提高生产效率和降低劳动力成本等方面的优势，在机械和电子制造行业的应用日趋广泛。本项目研究加工速度可变的节能型自动化制造单元调度问题。首先将研究变速机的节能机理，分析加工速度变化对生产效率和能耗量的影响。其次，为节能型自动化制造单元调度问题建立单目标和多目标混合整数规划模型，并分析模型的结构特性。在此基础上，为中小规模问题开发Relax-and-Optimize精确调度算法，并提出预处理技术和有效不等式来进一步提高算法的求解效率；为大规模问题开发融合问题特征的差分进化文化基因调度算法，包括个体的编解码、变异和交叉算子、个体的评价和选择、局部搜索算法和基于自适应学习机制的局部搜索策略等。最后，以典型自动化制造单元为对象，对提出的调度方法进行验证和应用。研究成果将为节能型自动化制造单元调度问题提供解决思路，并在机械制造和电子行业有较重要的应用价值。

As a kind of advanced automated manufacturing systems, robotic cells have been widely used in the machinery and electronic manufacturing industries because of their advantages on enhancing productivity and reducing labor costs. Energy-saving robotic cells scheduling problems with speed-scaling machines will be investigated in this project. Firstly, the energy-saving mechanism for speed-scaling machines will be studied by analyzing the influence of varying processing speeds on productivity and energy consumption. Secondly, single- and multi-objective mixed integer programming models for energy-saving robotic cells scheduling problems will be developed and their structural properties will be analyzed. Thirdly, Relax-and-Optimize scheduling algorithms will be proposed for small-sized and medium-sized problems. The preprocessing technique and valid inequalities will be developed to further improve the efficiency of the algorithm. For large-sized problems, memetic differential evolution scheduling algorithms will be developed, including individuals coding and decoding scheme, mutation and crossover operators, evaluation and selection of individuals, local search heuristics and local search strategy based on adaptive learning mechanism, etc. Finally, the proposed scheduling approaches will be validated and applied using cases from typical robotic cells. The research results of this project will not only provide a solution to energy-saving robotic cells scheduling problems, but also have important applied value in the machinery and electronic manufacturing industries.