加工车间生产计划和调度的集成建模与优化

发布时间：2018-04-01 04:11

本文选题：生产计划　切入点：调度约束　出处：《沈阳工业大学》2017年硕士论文

【摘要】：企业在生产管理中对生产计划与调度进行集成优化已经成为一项焦点技术,能够使企业在竞争中提升优势。“从上自下”是传统的生产计划与调度的生产方式:先是进行生产计划,接着再根据计划进行调度,然而这种方式存在的缺陷就是容易对调度约束产生忽视,即使制定了生产计划也会产生不能进行调度的情况,使得企业在发展中失去竞争优势。因此,要想合理地进行生产管理就必须把计划和调度统一进行优化,使得即使存在一定的约束性也不会影响整个计划的顺利进行,将生产计划和调度进行同步优化,要把握整个生产中供需平衡问题,不能在短时间内经常性地变化产品的类型。把工作的时间降低,使得设备占有率降低,可使得企业的生产速度增加、可产出量提升。因此本文提出一个算法,对中、小型企业的Job Shop中生产计划和调度的集成建模与优化进行研究分析。根据大量的国内外文献资料,探究和分析了企业在加工车间进行生产计划和调度的集成优化的情况。本文利用批量生产和分解的方式构建数学模型,在理论上满足了对生产计划和调度集成优化的生产需求,主要集中在目标的多样化上。根据批量生产计划和存在的约束对问题进行整体评判得到确定函数,实现在整个生产周期内所需要的各种费用总和最小(包括库存费、调度费、生产费等),同时保证最大完工时间最小化。所涉及的约束条件不仅指生产能力约束还有调度规则,也就是机器的加工能力和加工工序顺序的问题,另外还要保证库存不能太少或者爆满,进而使得生产计划顺利进行。本文不仅提出启发式规则和蚁群算法,而且还涉及到它们的结合使用,使得迭代速度加快不会出现僵局,同时又保证运算稳定性,并且降低了常规算法在分析蚂蚁转移概率方面的复杂性,也对比和研究了遗传算法和模拟退火算法的性能。使用该种算法不仅能够实现生产计划和调度集成优化的问题,还能对实例进行了仿真模拟。结果显示提出的新算法一方面计算所需的时间短,另一方面对全局也具有一定的收敛作用。依据上述理论的基础,在敏捷制造环境下以某机床厂中、小批量生产车间为背景,通过实际案例对本文提出的算法进行了对比演算,得出仿真效果,说明了本文提出的模型和求解方法都是可行的,其优化结果远好于基于NEH排序准则编写的遗传算法的结果。
[Abstract]:Integrated optimization of production planning and scheduling in production management has become a focus technology. "from top to bottom" is the traditional production mode of production planning and scheduling: first the production plan, then the scheduling according to the plan, However, the defect of this way is that it is easy to ignore the scheduling constraints, even if the production plan is made, it will lead to the situation that the scheduling can not be carried out, which makes the enterprise lose its competitive advantage in the development. In order to carry out the production management reasonably, the planning and scheduling must be optimized in a unified way, so that even if there are certain constraints, the smooth progress of the whole plan will not be affected, and the production plan and scheduling should be optimized synchronously. In order to grasp the problem of balance between supply and demand in the whole production, we should not change the type of products regularly in a short period of time. By reducing the working time and reducing the share of equipment, we can increase the production speed of enterprises. Therefore, this paper proposes an algorithm to study and analyze the integrated modeling and optimization of production planning and scheduling in Job Shop of small and medium-sized enterprises. This paper probes into and analyzes the integrated optimization of production planning and scheduling in the processing workshop. In this paper, a mathematical model is constructed by means of batch production and decomposition, which meets the production requirements of integrated optimization of production planning and scheduling theoretically. It is mainly focused on the diversification of objectives. According to the overall evaluation of the problem according to the batch production plan and existing constraints, the definite function is obtained, and the sum of all kinds of costs required in the whole production cycle is minimized (including inventory cost, scheduling cost, etc.). Production costs, etc., while ensuring maximum completion time minimization. The constraints involved not only refer to production capacity constraints but also scheduling rules, that is, the machining capacity of machines and the sequence of processing processes, In addition, we also need to ensure that the inventory is not too small or full, so that the production plan can proceed smoothly. This paper not only proposes heuristic rules and ant colony algorithm, but also involves their combined use, so that the iteration speed can be accelerated without deadlock. At the same time, the computational stability is guaranteed, and the complexity of the conventional algorithm in analyzing ant transfer probability is reduced. The performance of genetic algorithm (GA) and simulated annealing algorithm (SA) are compared and studied. The simulation results show that the proposed new algorithm has a certain convergent effect on the whole, on the one hand, the computing time is short, on the other hand, it has a certain convergent effect on the whole. Based on the background of a machine tool factory in an agile manufacturing environment, the algorithm proposed in this paper is compared and calculated by a practical case, and the simulation results are obtained. The results show that the proposed model and solution method are feasible. The result of optimization is much better than that of genetic algorithm based on NEH ranking criterion.
【学位授予单位】：沈阳工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TB497

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