基于改进的混合免疫算法的车间调度问题研究

发布时间：2018-05-01 17:27

本文选题：车间调度 + 免疫算法　；参考：《大连交通大学》2014年硕士论文

【摘要】：随着全球经济竞争的不断加剧,企业的生产方式也在不断变化,车间生产调度在制造业中占有愈来愈重要的地位。混合流水车间是在基本流水车间的基础上推广而来,在调度的各阶段增加并行设备数,达到工件的并行生产。它普遍的存在于石油化工、制药、钢铁等流程制造业中。在柔性作业车间中,需要考虑更多的约束限制,各种设备的加工状况不确定,需要制定更加灵活的调度方案。而解决生产车间调度问题,就是根据工件的加工要求,满足前提约束条件的同时,将资源进行合理配置,使企业获得更多的效益。因此,对车间调度问题的中的混合流水和柔性作业车间问题进行研究,具有很好的代表性,顺应生产发展的要求,具有重要的应用价值。车间调度问题是一类组合优化问题,目前求解该类问题的算法很多,但是应用传统算法求解大规模复杂问题往往存在各种限制。智能优化算法在解决复杂的NP-hard问题时,呈现出无可比拟的优越性。免疫算法是受生物免疫系统的启发,建立在免疫学理论基础之上的一种新的智能优化算法。但是免疫算法的研究还处于起步阶段,在具体优化问题中应用时,还存在一定的缺陷。算法在进行一定的迭代次数之后,会出现搜索退化的现象,容易陷入局部最优。模拟退火算法具有完整的理论基础,在进行问题的全局优化时表现出强有力的竞争优势。通过对免疫算法和模拟退火算法的性能分析,明确了算法进行结合的优势。针对实际生产中存在较多的混合流水车间调度问题和柔性作业车间调度问题设计了改进的混合免疫算法,并用该算法解决车间调度问题。本文在将免疫算法和模拟退火算法进行融合时,设计了混合算法的整体结构,制定了合理的编码规则,对免疫算子、多样性评价等进行了研究,结合自适应的退火操作共同完成了混合算法的设计。为了检验改进算法的有效性,分别选择了流水车间和作业车间两种类型问题的实例进行测试,将对应改进算法的性能进行了评估。最后,将算法应用到了车间调度问题的模拟系统中,结果显示改进的算法能够很好的收敛到可行解,说明本文的改进算法相比传统的调度算法而言‘,在解决实际问题时更有优越性
[Abstract]:With the aggravation of the global economic competition, the production mode of the enterprise is also changing constantly, the workshop production scheduling plays an increasingly important role in the manufacturing industry. Hybrid flow shop is extended on the basis of basic flow shop to increase the number of parallel equipment in each stage of scheduling to achieve the parallel production of workpieces. It is common in petrochemical, pharmaceutical, steel and other process manufacturing. In the flexible job shop, more constraints need to be considered, and the processing conditions of various equipments are uncertain, and a more flexible scheduling scheme is needed. To solve the production shop scheduling problem is to meet the requirements of workpiece processing and meet the prerequisite constraints, at the same time, the reasonable allocation of resources, so that enterprises can get more benefits. Therefore, the study of hybrid flow and flexible job shop in the job shop scheduling problem has good representativeness, conforms to the requirement of production development, and has important application value. Job shop scheduling problem is a kind of combinatorial optimization problem. At present, there are many algorithms to solve this kind of problem. However, there are many limitations in solving large-scale complex problems with traditional algorithms. The intelligent optimization algorithm has unparalleled superiority in solving the complex NP-hard problem. Immune algorithm is a new intelligent optimization algorithm inspired by biological immune system and based on immunology theory. However, the study of immune algorithm is still in its infancy, and there are still some defects when it is applied to specific optimization problems. After a certain number of iterations, the algorithm will appear the phenomenon of search degradation, which is easy to fall into local optimum. The simulated annealing algorithm has a complete theoretical foundation and shows a strong competitive advantage in the global optimization of the problem. By analyzing the performance of immune algorithm and simulated annealing algorithm, the advantages of combining the algorithm are clarified. An improved hybrid immune algorithm is designed to solve the problem of hybrid flow shop scheduling problem and flexible job shop scheduling problem in practical production, and the algorithm is used to solve the job shop scheduling problem. In this paper, when the immune algorithm and simulated annealing algorithm are fused, the whole structure of the hybrid algorithm is designed, and the reasonable coding rules are worked out. The immune operator, the diversity evaluation and so on are studied. Combined with adaptive annealing operation, the hybrid algorithm is designed. In order to test the effectiveness of the improved algorithm, the performance of the improved algorithm is evaluated by choosing two kinds of problems, the flow shop and the job shop, respectively. Finally, the algorithm is applied to the simulation system of job shop scheduling problem. The results show that the improved algorithm can converge to the feasible solution well, which shows that the improved algorithm in this paper is better than the traditional scheduling algorithm. Have more advantages in solving practical problems
【学位授予单位】：大连交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP18;TB497

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