Abstract: To improve the production efficiency and reduce the energy consumption,by analyzing the research status of low-carbon scheduling problem,a mathematical model was established with the criterion to minimize the weighted sum of energy-consumption cost and completion-time cost in flexible job shop.According to the characteristics of the established model,a series of design and improvement were conducted to Grey Wolf Optimization (GWO).In this algorithm,a two-phase encoding/decoding mechanism of individual position and a heuristic-based population initialization method were employed to enhance the quality of initial solutions;a nonlinear adjustment strategy of convergence factor and an individual update method with weight coefficients were developed to act on the evolutionary process;a local search algorithm was embedded into the algorithm to enhance the local search ability.The extensive simulation data demonstrated that the proposed algorithm was effective for solving the low-carbon scheduling problem in the flexible job shop.

Key words: low-carbon strategy, flexible job shop, production scheduling, grey wolf optimization

摘要： 为了提高生产效率、降低能源消耗量,通过分析低碳调度问题的研究现状,建立一种柔性作业车间内以能耗成本和完工时间成本加权和最小为目标的低碳调度问题的数学模型。根据所建模型的特点,对灰狼优化算法进行一系列设计和改进。首先采用两段式个体位置编码/解码机制,并利用基于启发式算法的种群初始化方法提高初始解的质量;分别设计了收敛因子非线性调整策略以及带权重系数的个体位置更新方法,并将其作用于算法进化过程;在算法中嵌入了局部搜索算法,用于加强局部搜索能力。大量仿真数据表明,所提算法在求解柔性作业车间低碳调度问题方面具有一定的有效性。

关键词: 低碳策略, 柔性作业车间, 生产调度, 灰狼优化算法