Abstract: In the realm of flexible flow shop scheduling,conventional theoretical models often presume unlimited interim storage capacity,a premise inconsistent with the spatial limitations faced by actual enterprises.Moreover,with the advancement of the manufacturing sector,scheduling efforts must now reconcile production efficiency with energy consumption.A mathematical model for flexible flow shops with restricted interim storage,incorporating equipment processing,standby modes,and mold changing during continuous operations was developed.The objective was to minimize both the maximum completion time and the overall energy consumption within the workshop.To achieve global optimization,a Pareto-based Multi-objective Imperialist Competitive Algorithm (MOICA) was proposed,which introduced rapid non-dominated sorting for initial population assimilation,neighborhood search and reverse learning in empire updating to ensure efficiency and effectiveness.Case studies validated the algorithm's feasibility and superior solution quality,effectively balancing completion time against energy usage and notably enhancing scheduling performance.This approach offered innovative avenues for green and efficient production scheduling in manufacturing enterprises,contributing significantly to the sustainable development of production systems.

Key words: workshop manufacturing, flexible flow shop, production scheduling, energy consumption

摘要： 为了优化柔性流水车间的生产调度策略,针对柔性流水车间生产调度问题理论研究中通常假设暂存区具有无限容量与实际企业空间资源受限不符的问题,以及制造业发展对生产调度提出兼顾生产效率与能源消耗的要求,对带有限暂存区的柔性流水车间,考虑设备加工、待机状态以及连续作业时的换模状态,建立了以最小化最大完工时间和车间总能耗为优化目标的数学模型,并提出基于Pareto思想的多目标帝国竞争算法作为全局优化方法,在初始种群、帝国同化引入快速非支配排序,在帝国更新过程中引入变量邻域搜索与逆向学习,最后通过算例扩展验证了算法的可行性和求解效率的优越性。该算法可以有效平衡完工时间能耗,显著提升调度方案的综合性能,为制造企业的绿色高效生产调度技术提供了新思路,对推动生产系统的可持续发展具有实际应用价值。

关键词: 车间制造, 柔性流水车间, 生产调度, 能源消耗