考虑资源约束及预防性维修的混合流水车间调度问题研究

发布时间：2018-07-10 09:09

本文选题：混合流水车间调度 + 资源约束　；参考：《电子科技大学》2012年硕士论文

【摘要】：本论文的研究基于四川省青年基金项目“面向迂回生产流程的多目标生产规划研究”（批准号：09ZQ026-054）。混合流水车间调度(Hybrid Flow Shop Scheduling,HFSS)问题是两类经典调度问题Flow Shop和并Parallel Shop的推广，相比之下，HFSS问题要求在整个加工流程中，至少有一个阶段具有两台及以上的并行机器，属于较为复杂的NP-Hard难题。此类问题至少需要同时解决两个问题：各工件的加工顺序以及各工件在各加工阶段加工机器的分配情况。针对目前国内外车间调度的研究现状以及存在的实际问题，本文重点研究了考虑资源约束的混合流水车间调度问题以及具有预防性维修的资源约束混合流水车间调度问题。针对混合流水车间稀缺资源约束的调度问题，将稀缺资源约束对最小化最大完工时间(makespan)的影响考虑到目标函数中建立了基于资源约束的数学规划模型，有效的解决了多台并行机因竞争稀缺资源而造成的时延等待、设备利用率低的调度问题。由于传统遗传算法存在早熟收敛的不足，提出采用NEH启发式算法产生初始种群的改进遗传算法。实例仿真结果表明，在此模型下，改进的遗传算法较传统遗传算法能更好的解决有限稀缺资源约束的混合流水车间调度问题。在生产过程中，由于需要定期对设备进行预防性维修，因此需考虑设备预防性维修对车间调度的影响。本文在建考虑资源约束调度模型的基础上，将预防性维修与稀缺资源等待对工序起始加工时间的影响考虑到目标函数中，建立具有预防性维修的资源约束混合流水车间调度模型，该模型能够满足合理优化配置有限资源以及定期对设备进行预防性维修的同时，使得设备利用率最高，生产周期最短。为解决此类复杂约束调度问题，本文提出新的启发式规则，结合遗传算法对调度模型进行求解。通过实例验证，改进的算法能够取得较好的调度方案。同时，本文通过收集某风电叶片制造厂实际车间数据，，对文中所提出的两类调度模型分别与传统调度模型作对比，通过计算各项调度指标，验证了本文所建模型能够很好的解决稀缺资源约束及统筹优化设备预防性维修与调度的问题，达到提高资源利用率、缩短生产周期的目的。
[Abstract]:The research of this thesis is based on Sichuan Youth Foundation Project "Multi-objective production Planning for circuitous production process" (Grant No.: 09ZQ026-054). Hybrid flow shop scheduling (HFSS) problem is a generalization of two classical scheduling problems, flow Shop and parallel Shop. In contrast, HFSS problem requires that there are at least two parallel machines in one stage in the whole process. It belongs to the more complicated NP-Hard problem. This kind of problem needs to solve at least two problems simultaneously: the processing order of each workpiece and the distribution of each workpiece in each processing stage. In view of the current situation and practical problems of job shop scheduling at home and abroad, this paper focuses on the hybrid flow-shop scheduling problem with resource constraints and the resource-constrained hybrid flow-shop scheduling problem with preventive maintenance. In order to solve the scheduling problem of resource constraints in mixed flow shop, a mathematical programming model based on resource constraints is established by taking into account the influence of scarce resource constraints on minimizing the maximum completion time (makespan). It effectively solves the problem of delay waiting caused by competing for scarce resources and low utilization of equipment on many parallel machines. Because the traditional genetic algorithm has the deficiency of premature convergence, an improved genetic algorithm using NEH heuristic algorithm to generate initial population is proposed. The simulation results show that the improved genetic algorithm is better than the traditional genetic algorithm in solving the mixed flow shop scheduling problem with limited scarce resource constraints. In the process of production, the preventive maintenance of equipment is required, so it is necessary to consider the influence of preventive maintenance on workshop scheduling. Based on the resource constrained scheduling model, the effects of preventive maintenance and scarce resource waiting on the process starting processing time are taken into account in the objective function in this paper. A resource constrained hybrid flow shop scheduling model with preventive maintenance is established. The model can meet the requirements of rational allocation of limited resources and periodic preventive maintenance of the equipment, which makes the equipment utilization rate highest and the production cycle shortest. In order to solve this kind of complex constrained scheduling problem, a new heuristic rule is proposed in this paper, and a genetic algorithm is used to solve the scheduling model. An example shows that the improved algorithm can achieve a better scheduling scheme. At the same time, by collecting the actual workshop data of a wind turbine blade factory, the two kinds of scheduling models proposed in this paper are compared with the traditional scheduling models, and each scheduling index is calculated. It is verified that the proposed model can solve the problem of scarce resource constraints and optimize the preventive maintenance and scheduling of equipment as a whole to improve the utilization of resources and shorten the production cycle.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH186

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