面向网络化制造的多工艺路线规划与调度集成

叶林; 袁晓玲

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面向网络化制造的多工艺路线规划与调度集成

更新时间：2025-07-09

- 面向网络化制造的多工艺路线规划与调度集成
- Integration of Multiple Process Routes Planning and Scheduling in Networked Manufacturing
- 西安交通大学学报 2009年43卷第5期页码：66-70
- 作者机构：
  
  西安交通大学经济与金融学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TP272
- 网络首发：2009-05-10，
  
  纸质出版：2009
- 稿件说明：
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叶林, 袁晓玲. 面向网络化制造的多工艺路线规划与调度集成[J]. 西安交通大学学报, 2009,43(5):66-70.

Integration of Multiple Process Routes Planning and Scheduling in Networked Manufacturing[J]. 2009, 43(5): 66-70.
叶林, 袁晓玲. 面向网络化制造的多工艺路线规划与调度集成[J]. 西安交通大学学报, 2009,43(5):66-70. DOI：

Integration of Multiple Process Routes Planning and Scheduling in Networked Manufacturing[J]. 2009, 43(5): 66-70. DOI：

摘要

针对传统的工艺规划与生产调度方法难以满足网络化制造环境下多工艺规划与生产调度集成的问题

建立了一种多工艺路线规划与生产调度集成数学模型

以实现网络化制造环境下多制造任务的工艺路线规划与生产调度的协同决策

产生符合实际生产需要的工艺路线与生产调度方案.为了实现对该集成数学模型的有效解算

分别设计了遗传算法与免疫算法的模型解算方法进行求解

并比较了这2种解算方法的解算效果.仿真结果表明

采用所建立的集成数学模型与解算方法可有效解决网络化制造环境下多工艺路线规划与调度集成的问题

但在求解效率与精度上免疫算法要优于遗传算法

从而为实际应用提供了借鉴.

Abstract

Due to the production flexibility in networked manufacturing

two functions of process planning and scheduling are tightly interwoven with each other and the integration of process planning and scheduling becomes the key point for an efficient utilization of manufacturing resources. Therefore

a mathematical model for the integration of multiple process routes planning and scheduling in networked manufacturing is presented. It is utilized to collaboratively produce the optimal process routes and scheduling schemes for practical manufacturing tasks production. For resolving this mathematical model

two solution algorithms based on genetic algorithm(GA)and immune algorithm(IA)respectively are designed and the sensitivities are analyzed. The simulation shows that the presented mathematical model and solution algorithms enable to deal with the integration problem of multiple process routes planning and scheduling in networked manufacturing effectively and efficiently

however

the resolving efficiency and precision of IA is better than that of GA

which provides a reference for enterprise production control.

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Keywords

references

MATTFELD D C,BIERWIRTH C. An efficient genetic algorithm for job shop scheduling with tardiness objectives [J]. European Journal of Operational Research,2004,155(13):616-630.

YUN Y S. Genetic algorithm with fuzzy logic controller for preemptive and non-preemptive job-shop scheduling problems [J]. Computers Industry Engineering,2002,43(2):623-644.

KUMARA M, RAJOTIA S. Integration of scheduling with computer aided process planning [J]. Journal of Materials Processing Technology, 2003, 138:297-300.

VINOD V, SRIDHARAN R. Scheduling a dynamic job shop production system with sequence-dependent setups: an experimental study [J]. Robotics and Computer Integrated Manufacturing, 2008,24(3): 435-449.

周光辉,江平宇,黄国全. 客户竞争驱动的任务调度非合作博弈研究 [J]. 机械工程学报,2006,42(7): 56-61.

ZHOU Guanghui, JIANG Pingyu, HUANG Guoquan. Non-cooperation game for customer's competition driven job schedule [J]. Chinese Journal of Mechanical Engineering, 2006, 42(7): 56-61.

MUSILEK P, LAU A, REFORMAT M, et al. Immune programming [J]. Information Sciences,2006,176(8): 972-1002.

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