【作者】 王宁；

【导师】 李晔；

【作者基本信息】 大连海事大学 ， 工业工程（专业学位）， 2021， 硕士

【摘要】 随着我国实体经济的飞速发展,制造业在工业发展体系中所占有的地位也越来越重要,企业间的竞争也越演越烈。为了增强企业在整个行业中的竞争力,企业内部对作业调度问题进行了不断深化的研究,作业调度车间作为复杂度最高的生产加工车间,成为了主要的研究对象。在该类型的生产车间,工序加工不再局限于一台机器,可以在多台设备上进行,每台设备上的加工时间互不相同,车间需要对工序的加工顺序及相应的设备进行调度安排,因此,成为了一直以来研究的热点。本文主要以蚁群算法作为主要的优化算法,通过信息素的挥发和更新机制的改进,结合遗传算法的优势,建立了新的算法模型,以最小化最大完工时间为目标函数,对作业车间调度问题进行理论和分析。具体工作内容如下:(1)通过对车间效益影响因素的分析,确立以最优化最大加工时间为目标函数。(2)针对蚁群算法的生物学思想,对蚁群算法的信息素挥发因子的适应性区间进行动态设定和求解,对于信息素的更新机制进行了两阶段的设置,使局部与全局结合的方式进行信息素更新,即第一阶段是每完成一次加工工序,信息素进行一次局部更新,在达到设定的迭代条件后进入第二阶段,由于一次迭代完成后只对最优路径上的信息素进行更新,为了克服蚁群算法初期对信息素的依赖,使用遗传算法对信息素进行初始化。通过基于工序原则的遗传算子设计,运用子父代收敛关系设计并得到最佳收敛率比值,加快整体算法的收敛效果,改善蚂蚁全局的搜索能力。(3)对提出的改进蚁群算法在作业车间调度问题中的应用,进行了步骤设计,给出了具体流程,确定了信息素挥发更新、参数自适应机制及最佳收敛率阈值等相关关键参数,通过Python编程实现了所提出的算法,并采用标准数据集对不同算法进行多次仿真实验,验证改进后算法的有效性及鲁棒性。(4)以沈阳某发动机公司生产作业车间作为研究对象,运用现有调度方案和改进蚁群算法的调度方案,从多个角度与实际数据进行对比分析,验证改进蚁群算法在处理实际生产问题中的可行性。更多还原

【Abstract】 With the rapid development of China’s real economy,the status of manufacturing industry in the industrial development system is becoming more and more important,and the competition among enterprises is becoming more and more intense.In order to enhance the competitiveness of the enterprise in the whole industry,the enterprise has carried on the deepening research on the job scheduling problem.As the most complex production and processing workshop,the job scheduling workshop has become the main research object.In this type of workshop,the process processing is no longer limited to one machine,it can be carried out on multiple equipment,and the processing time of each equipment is different from each other.The workshop needs to schedule the process sequence and the corresponding equipment,so it has become a research hotspot all the time.In this thesis,ant colony algorithm is used as the main optimization algorithm.Through the improvement of pheromone volatilization and update mechanism,combined with the advantages of genetic algorithm,a new algorithm model is established.With minimizing the maximum completion time as the objective function,the job shop scheduling problem is analyzed theoretically.The specific work contents are as follows:(1)Based on the analysis of the factors affecting the workshop benefit,the objective function is established to optimize the maximum processing time.(2)According to the biological idea of ant colony algorithm,the adaptive range of pheromone volatilization factor of ant colony algorithm is set and solved dynamically.The pheromone update mechanism is set in two stages,which makes pheromone update in the way of combining local and global.In the first stage,pheromone is updated locally every time a processing procedure is completed,In order to overcome the dependence on pheromone in the initial stage of ant colony algorithm,genetic algorithm is used to initialize pheromone.Through the design of genetic operators based on the principle of operation,the optimal ratio of convergence rate is obtained by using the convergence relation of children and parents,which speeds up the convergence effect of the whole algorithm and improves the global search ability of ants.(3)The application of the improved ant colony algorithm in the scheduling problem of the workshop is designed step by step,the specific flow is given,and the key parameters such as pheromone volatilization update,parameter adaptive mechanism and optimal convergence rate threshold are determined.The proposed algorithm is realized by python programming,and the simulation experiments of different algorithms are carried out by using standard data set,The effectiveness and robustness of the improved algorithm are verified.(4)Taking the production workshop of an engine company in Shenyang as the research object,using the existing scheduling scheme and the scheduling scheme of improved ant colony algorithm,this paper compares and analyzes the actual data from many angles,and verifies the feasibility of the improved ant colony algorithm in dealing with the actual production problems.更多还原