摘要
在加工企业的生产过程中,初始生产调度(Production Scheduling)方案由于不确定事件的突然发生导致该方案不再最优,甚至不再可行,以致影响整个生产调度过程的后续执行。如何生成不确定事件结束后的调度恢复方案,降低不确定事件对生产调度系统造成的影响已经成为生产调度问题的核心内容。在企业的生产实际中,制定调整方案时应考虑多种因素,如恢复时间、恢复成本等,但是目前解决该类问题的方法往往在考虑成本因素的前提下制定调整方案,使得产生的新方案无法真正满足企业的实际需求。针对此种情况,本文利用干扰管理方法对不确定事件进行有效度量,在考虑多种因素的前提下分别对受客观不确定事件影响和受主客观不确定事件影响的生产调度受扰问题的模型和求解方法进行了研究和探讨,其主要内容如下：
(1)提出了一种不确定事件度量的新方法。针对现有干扰管理方法的扰动度量手段单一且难以全面度量生产调度系统扰动这一情况,本文通过对企业的实际需求进行分析,从企业客户、企业管理者和车间工人三个行为主体角度对不确定事件进行分析,将前景理论与模糊理论有机结合,在行为主体的价值函数基础上构建模糊处理后的不满意隶属函数,从而形成了一种从行为主体角度度量不确定事件的新度量方法,丰富和完善了现有的干扰管理度量手段。
(2)建立了受不确定事件影响的生产调度干扰管理模型。针对车间中机器间的耦合度不同,将生产调度受扰问题简化为多个单机调度问题或作业车间调度问题,并根据不确定事件的性质不同,采用不同的度量方法,分别建立了客观不确定事件和主客观混合不确定事件影响的生产调度干扰管理模型,为获得受扰动影响最小的调整方案奠定必要的理论基础。
(3)针对生产调度干扰管理模型求解的需求特点,提出了一种改进的多目标粒子群优化算法对模型进行求解。为了应对生产调度干扰管理实时求解的需要,采用惯性权重调整策略加快算法的求解速度；为了提高生产调度干扰管理模型对求解精度的要求,采用最优解更新策略、最优个体变异策略等方法来保持种群的多样性,从而获得分布范围较广的最优解,避免搜索早熟。实验表明,本文算法可以较好的满足生产调度干扰管理模型的求解需求。
In the actual production of enterprise, an original plan of production scheduling due to sudden disruption evernts may be no longer optimal, or even no feasible, which affects follow-up execution of the whole procduction scheduling process. How to generate a revising plan after disruption events to minimize the negative impact which is cauesed by disruption events on the production scheduling system is a core problem. The enterprise should consider many factors to make the revising plan, such as recovery time, recovery cost and so on. However, the method for solving the problem often consider only the recovery cost to make the new plan which will not to meet the actual require of enterprises. In this case, this paper uses disruption management to effectively measure disruption events, studies and discusses the model and solving method for production scheduling problem which is affected by the objective disruption events and the subjective-objective disruption events. This research includes the following aspects.
(1) The method to measure disruption events is proposed since the existing method is difficult to measure the deviation in production scheduling system. Combining the prospect theory which is good at analyzing behavior with fuzzy theory which can express uncertain information, the deviation is measured by analyzing the effects of disruption events on the customer, manager and worker. The value functions of the different objectives are constructed and normalized respectively. The dissatisfactory membership functions of the different objectives are obtained. In consideration of human behavior, the functions to measure the behavior perception are constructed, which is helpful to contribute to the theory and method of disruption management.
(2) The model of disruption management in production scheduling problem affected by disruption events is constructed. According to difference of coupling between machines, the production scheduling problem which is affected by the disruption events may simplify into more single machine scheduling problems or a job shop scheduling problem. The models of disruption management of production scheduling problem which is affected by objective disruption events and the subjective-objective disruption events are constructed respectively based on the different methods of measuring disruption events. It provides the theory foundation for requiring revising plan which has minimum disruption.
(3) An improved muti-objective particle swarm optimization is proposed to meet characteristics of the above mode solution. Adjusting the inertia weight is used to speed up velocity to meet need of real-time solution of disruption management model of production scheduling problem; Updating the extremum, variancing optimization solution and so on are adopted to expand the solution space and avoid prematurity. Experiment indicates our method may satisfy solving need of disruption management model of production scheduling problem.
引文
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