摘要
实际中大多数生产调度问题具有多目标优化的性质,本文讨论在不确定加工时间和机器故障的情况下,如何优化多目标流水车间调度问题.首先设计最大流程时间和最大延迟时间两类指标的求解方法,在此基础上提出一种多目标遗传算法,用来迭代求解不确定条件下两类目标的最优化问题.模拟实验的结果表明,本文算法方案可较好解决不确定条件下的流水车间调度问题.
The model of optimizing multi-objective flow shop scheduling with stochastic processing time and machine breakdown is analyzed. The multi-objective flow shop scheduling problem is modeled with the stochastic processing time and machine breakdown. A mathematical scheme is designed for the solutions with the longest flow time or the longest delay time. A hybrid multi-objective genetic algorithm is proposed to solve the optimization problems iteratively in uncertain condition. The simulation results show that the proposed algorithm has good performance for the flow shop scheduling problems in uncertain condition.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第1期101-107,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.60773129)
安徽省优秀青年科技基金(No.08040106808)资助项目
关键词
不确定性
流水车间调度
多目标优化
遗传算法
Uncertainty, Flow Shop Scheduling, Multi-Objective Optimization, Genetic Algorithm
