• CN:11-2187/TH
  • ISSN:0577-6686

›› 2009, Vol. 45 ›› Issue (10): 137-142.

• 论文 • 上一篇    下一篇

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不确定条件下车间动态重调度优化方法

刘明周;单晖;蒋增强;葛茂根;扈静;张铭鑫   

  1. 合肥工业大学机械与汽车工程学院
  • 发布日期:2009-10-15

Dynamic Rescheduling Optimization of Job-shop under Uncertain Conditions

LIU Mingzhou;SHAN Hui;JIANG Zengqiang;GE Maogen;HU Jing;ZHANG Mingxin   

  1. School of Mechanical and Automotive Engineering, Hefei University of Technology
  • Published:2009-10-15

摘要: 分析车间生产环境复杂、多变以及生产过程中各种随机扰动所导致的不确定性问题,将扰动分为显性扰动和隐性扰动两类。分别采用主动和被动触发式重调度驱动规则,对各种扰动进行响应,并通过建立重调度优化集,结合滚动时域优化方法,对大规模动态重调度优化问题进行了简化。提出重调度优化集内待加工工件的选取规则,以减少工序间机器空闲时间。最后提出混合粒子群调度优化算法,对优化集内待加工工件进行智能优化调度,并采用该算法结合具体的仿真实例验证了该动态随机重调度优化方法的有效性。

关键词: 动态重调度, 工件选取规则, 混合粒子群算法, 重调度驱动规则, ANSYS, 过盈配合, 机械密封, 接触分析

Abstract: The uncertainties caused by complex and changeable workshop production environment and various stochastic disturbances during the production process are analyzed. Disturbances are classified into dominant disturbances and recessive disturbances, and initiative and passive rescheduling driven rules are adopted respectively to respond to various disturbances. A rescheduling optimization set is built, combined with rolling horizon optimization method, to simplify the large scale dynamic scheduling problem. A selecting rule of jobs is proposed to reduce vacancy between working procedures. A hybrid particle swarm optimization (PSO) scheduling algorithm is given. Finally simulation results show the efficiency of the method of optimization of dynamic rescheduling with the hybrid PSO.

Key words: Dynamic rescheduling, Hybrid particle swarm optimization, Rescheduling driven rules, Selecting rule of jobs, ANSYS, Contact analysis, Interference fit, Mechanical seals

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