不确定环境下车间调度算法研究

发布时间：2019-02-21 19:06

【摘要】：随着工业技术的进步和全球化市场经济的发展，对制造企业的生产管理提出了更高的要求，尤其是生产管理中的核心技术——车间调度。然而以往对车间调度问题的研究多是集中在确定性车间调度问题上，难以准确描述实际生产中出现的各类随机因素。针对生产中往往存在着的各种不确定性因素，对求解不确定环境下车间调度问题的算法进行分析和设计。对车间调度问题进行数学建模分析，分析模型的建模方法、输入与输出数据、模型优化目标，并对常用的智能求解方法进行分析和比较。研究了基本遗传算法，在考虑到遗传算法的优缺点的条件下，设计出了一种能较快收敛并能收敛于全局最优解的改进的遗传算法：采取精英个体保留和交叉，按群体适应度值的分布调整交叉和变异概率，加快算法的收敛速度，同时避免陷入局部最优解。针对不确定环境下车间调度问题，建立了基于模糊数学规划理论的不确定作业车间调度模型，，不确定加工时间用三角模糊数来表示，不确定交货期用梯形模糊数来表示，以最大化产品平均客户满意度为优化目标，运用基于模糊数学的改进的遗传算法来求解这类问题，并通过MATLAB进行仿真实验，达到求解不确定环境下车间调度问题的目的。
[Abstract]:With the progress of industrial technology and the development of global market economy, higher requirements have been put forward for the production management of manufacturing enterprises, especially the workshop scheduling, which is the core technology of production management. However, in the past, most of the researches on job shop scheduling problems focused on deterministic job shop scheduling problems, and it is difficult to accurately describe all kinds of random factors in actual production. Aiming at various uncertain factors in production, the algorithm for solving job shop scheduling problem in uncertain environment is analyzed and designed. Mathematical modeling and analysis of job shop scheduling problem, analysis of modeling methods, input and output data, optimization objectives of the model, and analysis and comparison of common intelligent solution methods are carried out. In this paper, the basic genetic algorithm is studied. Considering the advantages and disadvantages of the genetic algorithm, an improved genetic algorithm, which can converge quickly and converge to the global optimal solution, is designed. The crossover and mutation probability are adjusted according to the distribution of population fitness to accelerate the convergence speed of the algorithm and avoid falling into the local optimal solution at the same time. In order to solve the job shop scheduling problem in uncertain environment, an uncertain job shop scheduling model based on fuzzy mathematical programming theory is established. The uncertain processing time is represented by triangular fuzzy number, and the uncertain due date is expressed by trapezoidal fuzzy number. With the aim of maximizing the average customer satisfaction of products, the improved genetic algorithm based on fuzzy mathematics is used to solve this kind of problem, and the simulation experiment is carried out by MATLAB to solve the job shop scheduling problem in uncertain environment.
【学位授予单位】：武汉科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH186

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