能效调度问题考虑同时优化工期指标和能耗指标，在实际生产过程中具有很强的现实需求，因此其理论与方法的研究在学术与应用层面都意义重大。基于分解的多目标进化通过分解策略将多目标优化问题转化为多个单目标优化问题，有效降低了问题的求解复杂度，已广泛用于解决多目标连续优化问题，但在组合优化尤其是生产调度领域研究还较少。本学位论文针对几类典型的能效调度问题，提出了基于分解策略的多目标优化方法，为能效调度提供理论和方法支撑。在综述能效调度和基于分解策略的多目标进化算法的相关研究现状的基础上，本论文通过深入研究主要取得了以下成果：（1）针对序相关准备时间流水线能效调度问题，建立了混合整数规划模型，设计了权向量动态匹配策略，通过分析序相关准备时间的特性，设计了两种启发式规则用于减少准备时间以及相应的局部搜索环节，进而提出了一种基于分解的权向量动态匹配多目标进化算法。（2）针对分布式多任务处理机混合流水线能效调度问题，建立了混合整数规划模型，设计了权向量自适应调整策略，设计了基于问题特性的编解码方法以及一种合作搜索机制与两种局部搜索操作，进而提出了一种基于分解的权向量自适应调整多目标进化算法。（3）针对分时电价柔性作业车间能效调度问题，建立了混合整数规划模型，设计了一种自适应选择搜索操作的机制以及一种基于关键路径的节省电费操作，进而提出了一种知识引导的基于分解的多目标进化算法。（4）针对分布式作业车间能效调度问题，建立了数学模型，设计了基于距离的协同搜索操作，证明了问题的性质，分析了所设计局部搜索环节的计算复杂度，进而提出了一种基于分解的协同多目标进化算法。（5）针对焊接工厂能效调度问题，设计了基于进化程度的计算资源分配规则、针对不同目标的局部搜索操作及其协同机制，进而提出了一种基于分解的计算资源自适应分配多目标进化算法。论文通过大量仿真实验验证了针对问题特性所设计的策略的有效性，并通过与代表性算法的对比验证了所提算法的高效性。

Energy-efficient scheduling problem should condider the optimization of time and energy consumption objectives simultaneously, which is of strong practical requirements in real manufacturing processes. Therefore, the research of theory and method has significance in both academic and application fields. The multi-objective evolutionary algorithm based on decomposition transforms the multi-objective optimization problem into a number of single-objective optimization problems by a decomposition strategy so as to reduce the complexity in solving the problem. Such solution idea has been widely applied for multi-objective continuous optimization problems, while there is little research about combinatorial optimization especially for production scheduling field. For some typical energy-efficient scheduling problems, this dissertation proposes some multi-objective optimization algorithms based on decomposition, which provides theoretic and methodology support for energy-efficient scheduling.After reviewing the state-of-the-art of the energy-efficient scheduling and the multi-objective evolutionary algorithms based on decomposition, this dissertation has achieved the following results via deep research.(1) For the energy-efficient permuatation flow shop scheduling problem with sequence-dependent setup time, a mixed integer programming model is established, a dynamic strategy matching individual and sub-problem is designed, the characteristic of sequence-dependent setup time is analyzed, and two heuristic rules for reducing setup time and a local search component are designed. Thus, a multi-objective evolutionary algorithm based on decomposition with weight vectors dynamic mating is proposed.(2) For the energy-efficient distributed hybrid flow scheduling problem with multiprocessor tasks, a mixed integer programming model is established, an adaptive strategy for adjusting weight vectors is designed, an encoding scheme and a decoding method based on the characteristics of the problem is designed, and a cooperative search mechanism and two local search operators are designed. Thus, A multi-objective evolutionary algorithm based on decomposition with weight vectors adaptive adjustment is proposed.(3) For the energy-efficient flexible job shop scheduling problem under the time-of-use electricity pricesis, a mixed integer programming model is established, and a mechanism for adaptively selecting search operators as well as an energy saving method based on critical path is designed. Thus, a knowledge-guided multi-objective evolutionary algorithm based on decomposition is proposed.(4) For the energy-efficient distributed job shop scheduling problem, the mathematical model is established, a collaborative search operator based on distance is designed, the property of the problem is proved, and the computational complexity of the search operator is analyzed. Thus, a collaborative multi-objective evolutionary algorithm based on decomposition is proposed.(5) For the energy-efficient welding shop scheduling problem, an adaptive computational resource allocation strategy is designed, and local search operators for different objectives and their cooperative mechanism are designed. Thus, a multi-objective evolutionary algorithm based on decomposition with adaptive resource allocation is proposed.Via extensive simulation tests, the effectiveness of the designed strategies according to the problem characteristics is demonstrated, and the efficiency of the proposed algorithm is also shown by the comparisons to the typical algorithms.