Abstract: Energy-saving scheduling is a workshop scheduling problem oriented to green manufacturing,which has become a research hot spot in the manufacturing field.Aiming at the flexible job shop with dual resource constraints of machine and worker,the effects of worker learning and job transportation time were considered simultaneously to minimize the energy consumption of the workshop,and a Modified Biology Migration Algorithm (MBMA) was proposed.In the algorithm,a job-machine-worker based three-segment encoding method was adopted to represent the scheduling solution,and a population initialization approach was design to improve the quality of initial scheduling solutions.Considering that the basic biology migration algorithm cannot be directly applied to the discrete workshop scheduling problem,a discrete biological migration operator based on crossover operations was proposed,by which the algorithm could search directly in the discrete scheduling domain.Furthermore,a dynamic adjustment strategy of the conversion probability was introduced into the migration operator to balance exploration and exploitation of the algorithm,and a memory pool mechanism was added to avoid the premature convergence.For the individual updating operator,a local search algorithm was designed and embedded to enhance the local search ability of the algorithm.Finally,a large number of experimental results showed that the computational results of MBMA were superior to other algorithms.

Key words: dual-resource constraint, worker learning effect, job transportation time, flexible job shop, energy-saving scheduling, biology migration algorithm, green manufacturing

摘要： 节能调度是面向绿色制造的车间调度问题,已成为制造领域的研究热点。针对具有机器和工人双资源约束的柔性作业车间,综合考虑工人学习效应和工件运输时间的影响,以最小化车间能耗为目标,提出一种改进的生物迁徙算法(MBMA)。该算法采用基于工件-机器-工人的三段式编码方法表示调度解,并设计了一种种群初始化方法,以改善初始调度解的质量。考虑到基本生物迁徙算法无法直接应用于离散车间调度问题,提出一种基于交叉操作的离散迁徙算子,使算法能够直接在离散调度空间内进行搜索。此外,在迁徙算子中引入转换概率动态调整策略,以平衡算法探索与开发能力,另外增加了一种记忆池机制,避免算法过早收敛。对于个体更新算子,设计了一种局部搜索算法嵌入其中,以增强算法局部搜索能力。大量实验结果表明,MBMA算法的计算结果优于其他算法。

关键词: 双资源约束, 工人学习效应, 工件运输时间, 柔性作业车间, 节能调度, 生物迁徙算法, 绿色制造