Department of Automation, Tsinghua University
The National Science Fund for Distinguished Young Scholars
当今社会环境问题日益严重, 能源成本日益提高, 峰值能耗在生产制造中备受关注. 针对带峰值能耗约束的流水线调度问题, 即生产过程中各时间节点机器总功耗不得超过给定阈值, 以最小化最大完工时间为目标, 提出一种协同群智能算法. 首先, 协同多种解码方法产生多样化的可行调度, 融合启发式方法与随机方法初始化种群. 其次, 设计两类基于问题特性的搜索操作, 分别调整工件序列和加工速度. 进而, 根据目标空间中个体的分布, 设计多种搜索操作的协同机制, 对不同区域的个体执行不同的搜索操作. 另外, 对精英个体进行局部增强搜索以进一步改善性能. 采用大量算例开展了数值实验, 验证了所设计协同机制的有效性, 并通过与数学求解器和现有算法的对比结果表明所提算法能够更有效求解带峰值能耗约束的流水线调度问题.
With the increasing energy costs and the serious environmental issue, peak power consumption attracts much attention by manufacturing industries. It requires that the real-time power consumption cannot exceed a given peak power at any time during manufacturing process. Aiming at the permutation flowshop scheduling problem with peak power consumption constraint (PFSPP), a cooperative memetic algorithm is proposed. First, multiple decoding methods are collaborated to generate diverse and feasible schedules, and a heuristic and random method are fused to initialize population. Second, two problem-specific search operators are designed according to the characteristics of the problem for adjusting the job sequence and speed selection, respectively. Furthermore, according to distribution of individuals in the objective space a cooperation scheme is designed. Different search operators are used for the individuals in different regions. In addition, a local intensification is performed on the elite individual to further improve the performance. Numerical tests are conducted by using extensive instances. The effectiveness of the designed cooperation mechanisms is demonstrated. Moreover, the comparisons with the math solver and the existing algorithms show that the proposed algorithm is more effective in solving the PFSPP.