Abstract: For the flexible job shop, a mathematical model with the objective of minimizing the energy consumption is established to solve the job shop scheduling problem under low-carbon strategy. For the model, an Improved Migrating Birds Optimization（IMBO） algorithm is proposed to solve the model. Global search, local search and random rule are combined to initialize the population to ensure the solution quality and the convergence speed of the algorithm. Two effective neighborhood structures are adopted to acquire the neighboring solutions of individuals, based on which a local search method is designed to enhance the local searching capability. In addition, a leaping mechanism is introduced to avoid the premature convergence. Extensive computational results demonstrate the feasibility and effectiveness of the proposed model and algorithm.

Key words: flexible job shop, low-carbon scheduling, migrating birds optimization algorithm

摘要： 针对柔性作业车间，建立一种以能耗最小化为目标的数学模型，解决低碳策略下的该车间内的作业调度问题。对于上述模型，提出一种改进型候鸟优化（Improved Migrating Birds Optimization，IMBO）算法进行求解。结合全局搜索、局部搜索和随机规则三种方式初始化种群，确保算法的求解质量和收敛速度。采用两种有效的邻域结构构造个体的邻域解，并在此基础上设计一种局部搜索方法增强算法的局部寻优能力。此外，引入一种跳跃机制避免算法陷入早熟收敛状态。通过大量计算结果验证了模型和算法的可行性和有效性。

关键词: 柔性作业车间, 低碳调度, 候鸟优化算法