作　　者： ; ; ; ;

机构地区： 西北工业大学机电学院

出　　处： 《数学的实践与认识》 2007年第17期42-52,共11页

摘　　要： 实际生产系统的车间作业调度一般是多约束多目标柔性Job-Shop调度,比经典的Job-Shop调度更复杂,存在多约束、多目标、动态柔性、建模复杂等特性.建立了多约束多目标柔性Job-Shop调度模型,提出了一种自适应蚁群算法,采用自适应机制和遗传原理防止算法过早停滞和加快收敛速度.西安航空发动机(集团)有限公司制造单元调度实例表明,提出的自适应蚁群算法是求解多约束多目标柔性Job-Shop调度的有效方法. Job-Shop scheduling in practical production system is generally multi-restriction and multi-objective flexible Job-Shop （MROFJS）, and is more complicated than classical Job-Shop because of multi-restriction, multi-objective, dynamic disturb, flexible process and complex model, and therefore it can＇t usually been solved by the ordinary optimization methods. The model of MROFJS is set up and accordingly an adaptive ant colony algorithm （AACA） is brought forward. The adaptive mechanism＇ and the genetic principle are introduced into the algorithm to avoid stagnation and accelerate convergence. MROFJS and AACA are applied to the manufacture cell Job-Shop of Xi＇An Aero-engine （Group） LTD in China and the optimization result is gained, and consequently the practice application indicates that the proposed algorithm is a powerful and effective for MROFJS.

关 键 词： 蚁群算法 自适应 遗传算子 柔性 调度 多约束 多目标

领　　域： [自动化与计算机技术] [自动化与计算机技术]