含批处理特征的多阶段柔性流水车间优化研究

发布时间：2018-02-25 20:19

本文关键词： 柔性流水车间调度批处理特征总加权完成时间自适应遗传算法自适应调节炼钢-连铸-热轧钢铁生产　出处：《郑州大学》2017年硕士论文　论文类型：学位论文

【摘要】：在钢铁行业,炼钢、连铸、热轧作为炼钢的主要工序,生产出的铁道钢材、钢板桩及大中小型钢等极大地促进了国民经济的发展,在整个流程中起着重要作用。从炼钢-连铸-热轧生产过程中提炼出的多阶段柔性流水车间调度问题(Flexible Flowshop Scheduling Problem,FFSP),不仅需要满足钢铁生产的一系列约束条件,而且具有批处理的特征。带有批处理特征的多阶段FFSP是经典FFSP的延伸,要求同一批次内所有工件都要按照已知的优先级顺序,在同一台机器上进行无间断地加工。本文结合实际情况,对FFSP进行相关理论分析,并对国内外相关领域的研究进行学习。通过分析,对FFSP的应用现状进行总结,确定本文的研究问题。从钢铁生产的炼钢-连铸-热轧工艺中提炼出含有串行批处理特征的多阶段FFSP,综合考虑实际生产中的各种约束条件,以总加权完成时间最小化为目标建立数学模型。首先对连铸-热轧结构进行分析,可以将其看做为第一阶段有多台串行批处理机而其它阶段为离散机的FFSP,考虑工件在各加工阶段间的运输时间,利用本文提出的改进的自适应遗传算法进行优化求解。其次将连铸-热轧工艺向上游延伸,剖析炼钢-连铸-热轧生产过程的特点,归纳出中间阶段有多台批处理机,其它阶段为离散机的多阶段柔性流水车间调度问题。结合工件动态到达,各加工阶段间的运输时间以及机器的调整时间等生产特征,对问题进行数学描述并求解。针对不同的问题,本文分别对多达240个工件和150个工件的不同规模的大量随机数据进行仿真测试。并将拉格朗日松弛算法以及传统的遗传算法与本文所提出的改进的自适应遗传算法进行比较,结果表明,与常规遗传算法相比,所提出的自适应遗传算法能在较短的计算时间内得到更好的解;与拉格朗日松弛算法对比,当所要求解的问题为中大规模时,所提算法在解的质量方面优势较为明显。
[Abstract]:In the steel industry, steelmaking, continuous casting, hot rolling as the main process of steelmaking, railway steel production of steel sheet pile and the small and medium-sized steel has greatly promoted the development of the national economy, plays an important role in the whole process. Multi stage flexible flow shop scheduling problem derived from steelmaking continuous casting hot rolling production process (the Flexible Flowshop Scheduling Problem, FFSP), not only need to meet a series of constraints of steel production, but also has the characteristics of batch processing. Multi stage FFSP with batch characteristics is the extension of the classic FFSP requirements within the same batch of all jobs according to the known priority of uninterrupted processing in the same on a single machine. Combining with the actual situation, analyzes the related theories of FFSP, and the domestic and foreign research related fields of study. Through the analysis, the application of FFSP are summarized, indeed Study on the problem in this paper. From the production of iron and steel steelmaking continuous casting hot rolling process to extract containing multi stage FFSP serial batch processing feature, considering various constraints in actual production, to minimize the total weighted completion time to establish the mathematical model for the goal. Firstly, continuous casting and hot rolling structure analysis, can be seen as for the first stage of a serial batching machine and other stage for discrete machine FFSP, considering the workpiece in each processing stage of the transport time, optimize the use of the improved adaptive genetic algorithm is proposed in this paper. Secondly, continuous casting and hot rolling process to extend upstream, analyze the characteristics of steelmaking continuous casting hot rolling production process, summed up the intermediate stage of a plurality of batch processing machines, other stages of multistage flexible flow shop scheduling problem of discrete machine. Combined with dynamic job arrivals, each processing stage between transportation And adjust the time machine production characteristics, mathematical description and solving the problem. According to different problems, this paper respectively up to 240 pieces and 150 workpieces of different sizes in a random data simulation test. And the comparison of improved adaptive genetic algorithm Lagrange relaxation algorithm and traditional genetic algorithm and the the results show that compared with the conventional genetic algorithm, the proposed adaptive genetic algorithm can get a better solution within a short time; compared with the Lagrange relaxation algorithm to solve the problem when in large scale, the proposed algorithm is more obvious in the solution quality advantages.

【学位授予单位】：郑州大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TF758

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