中文摘要：

为解决小批量、多品种浸染生产不合理调度导致高能耗和多污染排放的问题,提出了一种基于遗传算法和多智能体染缸调度与动态优化的方法。该方法基于染缸车间制造执行系统、企业资源计划和过程控制系统实时数据,采用分层调度算法。其中静态层采用支持多产品的批处理、多染缸的非等同性、前期订单、订单交货期和切换成本等约束条件的遗传算法;动态层采用支持染缸运行状态的多智能体的协调动态优化算法。通过对生产过程中多约束条件和多动态变化因素的算法求解,获得染缸作业任务动态优化设计。仿真结果表明,与单纯遗传算法和人工调度相比,基于数据驱动的分层动态优化调度达到了染缸作业排产优化和污染减排的目标和实际应用的可行性。

英文摘要：

To solve the problem that unreasonable scheduling causes many high-energy consumption and pollution emissions in the dyeing and printing industry, a dynamic production scheduling strategy of dip-dye systems based on genetic algorithm and Multi-Agent and optimization methods was proposed. This method used the algorithm of hierarchical scheduling with dye vat MES, ERP and the PCS real-time data. Among them, the static layer supported GA under the constraints of multi-product batch processing, non-equivalence of dye vats, pre-orders, delivery orders, as well as the switching cost; the dynamic layer supported multi-agent coordination of dynamic optimization algorithm at running status. Through the algorithm with multi-constraints conditions of production and many factors of dynamic changes, the dynamic optimal design with dye vat homework tasks could be obtained. The simulation results compared with SGA and artificial scheduling showed that the strategy based on data-driven dynamic optimization under the premise of the static task of the workshop scheduling could improve production efficiency while achieving the objective of energy-saving ＆ pollution emission reduction. The results in the enterprise also showed that the method was effective and feasible.