低碳排放约束的柔性作业车间调度研究
发布时间：2018-09-08 10:20
【摘要】：随着工业4.0和“中国制造2025”的推进,绿色制造已成趋势。考虑低碳排放是制造业急需解决的问题,节能环保、绿色制造应写入制造行业的发展规划中。在实际车间生产中,机器的加工速度、加工工件的材质等问题都影响制造企业的碳排放量。在国内外与之相关的调度研究中,大多数研究主要关注求解问题的优化算法或者与调度有关的其他约束,然而关于低碳排放约束的柔性作业车间调度成果很少。因此本文主要针对低碳排放约束的柔性作业车间调度的建模和问题求解进行研究。本文研究首先描述了何为柔性作业车间调度,介绍了遗传算法。通过对遗传算法的改进方式进行重组并应用于所建模型的求解,验证了重组的遗传算法对模型求解的有效性。在此基础上,引入了低碳排放参数,设置了机器处于不同状态以及机器加工速度对碳排放量的影响,通过仿真实例说明了低碳排放约束的柔性作业车间调度问题模型的可行性。最后将数据驱动技术融入到动态柔性作业车间调度问题中,利用数据的预测功能,分析出当处于某个时间段时生产现场可能出现的突发状况,最后给出了几种调度情况发生变化后的更新的调度方案验证了所建动态调度模型的有效性。本文创新点主要体现在低碳排放约束的柔性作业车间调度的建模上,在经典的调度问题上,设计了低碳排放参数,增加以往文献没有考虑的工件的装夹和卸载时间,考虑了机器空转、加工及重启状态的碳排放差异,最后结合机器加工速度对生产过程中的机器总碳排放量的影响等,使问题更实际。又将数据驱动技术与动态调度相结合,通过调整不同突发情况下的调度方案验证数据驱动技术能有效解决动态柔性作业车间调度中的干扰因素对生产的影响。
[Abstract]:With the promotion of industry 4.0 and made in China 2025, green manufacturing has become a trend. Considering low carbon emission is an urgent problem in manufacturing industry, energy saving and environmental protection, green manufacturing should be included in the development plan of manufacturing industry. In actual workshop production, the machining speed of machine and the material of workpiece all affect the carbon emission of manufacturing enterprise. In the domestic and foreign related scheduling research, most of the researches mainly focus on the optimization algorithm for solving the problem or other constraints related to scheduling. However, the flexible job shop scheduling with low carbon emission constraints has little results. So this paper mainly focuses on the modeling and problem solving of flexible job shop scheduling with low carbon emission constraints. In this paper, we first describe what is flexible job shop scheduling, and introduce genetic algorithm. By reorganizing the improved genetic algorithm and applying it to the solution of the established model, the validity of the recombined genetic algorithm for solving the model is verified. On this basis, the low carbon emission parameters are introduced, and the effects of different machine states and machining speed on carbon emissions are set up. The feasibility of the flexible job shop scheduling model with low carbon emission constraints is illustrated by a simulation example. Finally, the data-driven technology is integrated into the dynamic flexible job shop scheduling problem. Using the prediction function of the data, the burst situation of the production site is analyzed when it is in a certain period of time. Finally, several updated scheduling schemes are presented to verify the validity of the proposed dynamic scheduling model. The innovation of this paper is mainly reflected in the modeling of flexible job shop scheduling with low carbon emission constraints. In the classical scheduling problem, the low carbon emission parameters are designed to increase the clamping and unloading time of the workpiece that has not been considered in previous literatures. Considering the difference of carbon emission between idle, processing and restarting states, the problem is more practical by considering the effect of machining speed on the total carbon emission of the machine in the process of production. The data-driven technology is combined with dynamic scheduling to verify that the data-driven technology can effectively solve the impact of interference factors in dynamic flexible job shop scheduling by adjusting the scheduling schemes in different burst situations.
【学位授予单位】：郑州航空工业管理学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TH165
[Abstract]:With the promotion of industry 4.0 and made in China 2025, green manufacturing has become a trend. Considering low carbon emission is an urgent problem in manufacturing industry, energy saving and environmental protection, green manufacturing should be included in the development plan of manufacturing industry. In actual workshop production, the machining speed of machine and the material of workpiece all affect the carbon emission of manufacturing enterprise. In the domestic and foreign related scheduling research, most of the researches mainly focus on the optimization algorithm for solving the problem or other constraints related to scheduling. However, the flexible job shop scheduling with low carbon emission constraints has little results. So this paper mainly focuses on the modeling and problem solving of flexible job shop scheduling with low carbon emission constraints. In this paper, we first describe what is flexible job shop scheduling, and introduce genetic algorithm. By reorganizing the improved genetic algorithm and applying it to the solution of the established model, the validity of the recombined genetic algorithm for solving the model is verified. On this basis, the low carbon emission parameters are introduced, and the effects of different machine states and machining speed on carbon emissions are set up. The feasibility of the flexible job shop scheduling model with low carbon emission constraints is illustrated by a simulation example. Finally, the data-driven technology is integrated into the dynamic flexible job shop scheduling problem. Using the prediction function of the data, the burst situation of the production site is analyzed when it is in a certain period of time. Finally, several updated scheduling schemes are presented to verify the validity of the proposed dynamic scheduling model. The innovation of this paper is mainly reflected in the modeling of flexible job shop scheduling with low carbon emission constraints. In the classical scheduling problem, the low carbon emission parameters are designed to increase the clamping and unloading time of the workpiece that has not been considered in previous literatures. Considering the difference of carbon emission between idle, processing and restarting states, the problem is more practical by considering the effect of machining speed on the total carbon emission of the machine in the process of production. The data-driven technology is combined with dynamic scheduling to verify that the data-driven technology can effectively solve the impact of interference factors in dynamic flexible job shop scheduling by adjusting the scheduling schemes in different burst situations.
【学位授予单位】：郑州航空工业管理学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TH165
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