非紧密衔接工序车间多目标调度方法的设计与优化

发布时间：2018-06-08 15:17

本文选题：多目标车间调度 + 工序非紧密衔接　；参考：《合肥工业大学》2017年硕士论文

【摘要】：车间调度是影响制造业生产效率的重要因素,多目标柔性调度模型更适应实际生产环境。然而现存的调度模型往往没有考虑到作业加工工序间的衔接问题,导致其优化目标函数模型往往过于理想化,数据统计忽略了工件运输和加工准备情况,数据处理相对于实际生产过程是间断的,得出的解难免脱离生产调度实际。因而在考虑工序存在衔接动作的同时进行多目标调度的研究具有重要的理论意义和应用价值。首先,介绍了课题的研究背景和意义,并通过对研究现状的分析概述了目前的发展趋势。总结了车间调度问题和多目标优化问题的理论研究。通过对工序非紧密衔接作业车间的生产特点的分析,针对工序间衔接操作分别提出了基于工序和基于快速非支配排序算法的调度策略。其次,基于对现有遗传算法(NSGA-II)的改进给出了求解非紧密衔接工序动态车间多目标调度问题的调度算法,设计了新的精英保留策略,既确保了种群分布具有多样性,也保证了种群进化方向更具实际意义。采用多目标决策方法AHP从得到的Pareto解集中选出性优的调度方案。并通过实例验证,表明了改进后的调度算法能够更具实际意义,能更好地解决非紧密衔接工序车间多目标调度问题。最后,以某企业生产加工线为研究对象,建立最大完工时间最小、生产成本最低和加工不合格率最低为调度优化目标的柔性作业车间调度数学模型,运用经典车间多目标调度算法和本文提出的调度算法分别进行求解,通过调度结果的对比分析,进一步验证了本文所设计调度方法的可行性与优越性。
[Abstract]:Job shop scheduling is an important factor affecting the production efficiency of manufacturing industry, and the multi-objective flexible scheduling model is more suitable for the actual production environment. However, the existing scheduling models often do not take into account the problem of convergence between job processing processes, resulting in the optimization of the objective function model is often too idealized, data statistics ignore the work piece transportation and processing preparation. The data processing is discontinuous relative to the actual production process, and the solution obtained is inevitably divorced from the actual production scheduling. Therefore, it is of great theoretical significance and practical value to study multi-objective scheduling while considering the existence of cohesive action. Firstly, the background and significance of the research are introduced, and the development trend is summarized through the analysis of the present research situation. The theoretical research of job shop scheduling problem and multi-objective optimization problem is summarized. Based on the analysis of the production characteristics of the non-tightly connected job shop, the scheduling strategies based on the process and the fast non-dominated sorting algorithm are proposed for the interworking operation respectively. Secondly, based on the improvement of the existing genetic algorithm (NSGA-II), a scheduling algorithm is proposed to solve the dynamic multi-objective scheduling problem in non-tightly connected processes, and a new elite retention strategy is designed, which ensures the diversity of population distribution. It also ensures that the direction of population evolution is more practical. The multi-objective decision method AHP is used to select the optimal scheduling scheme from the Pareto solution set. The results show that the improved scheduling algorithm has more practical significance and can better solve the multi-objective scheduling problem. Finally, taking the production and processing line of a certain enterprise as the research object, a mathematical model of flexible job shop scheduling is established, in which the maximum completion time is the minimum, the production cost is the lowest and the processing failure rate is the lowest. The classical job-shop multi-objective scheduling algorithm and the scheduling algorithm proposed in this paper are used to solve the problem respectively. The feasibility and superiority of the proposed scheduling method are further verified by the comparative analysis of the scheduling results.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TB497

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