摘要
以单件小批量生产方式为主的柔性车间调度中,快速得到满足低生产成本、高生产效率,避免瓶颈发生的调度方案,是调度优化算法的设计目标。就此建立了以制造期、机床总负荷和单机最大负荷为综合目标的柔性车间调度问题(Flexible Job-shop Scheduling Problems,FJSP)优化模型;设计了一种以概率值为分量的一维粒子群优化算法,通过概率区间划分将连续粒子分量离散化,结合完工时间最早启发式规则,实现工序的排序与加工机床的选取。通过不同规模算例的比较,分析结果表明该方法在求解较大规模问题时具有一定的优势。
In flexible job-shop scheduling with single piece and small batch production mode,the optimized objective is to reduce production costs,improve production efficiency and avoid bottleneck.This paper investigates an optimization model of Flexible Job-shop Scheduling Problems（FJSP）,which aims at a comprehensive objective combined with minimized makespan,machine total load and single maximum load.It designs a unidimensional-encoded Particle Swarm Optimization（PSO）taking probability as continuous particle component.Combined with completion-time-earliest heuristic rules,these components are discretized by probability interval to solve operation sequence scheduling and machine tools selecting.After comparing and analyzing different sizes of examples,the proposed algorithm is found a distinct advantage in solving large scale problems.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第13期47-51,71,共6页
Computer Engineering and Applications
基金
国家自然科学基金(No.61402361)
陕西省自然科学基础研究计划资助项目(No.2012JQ9005)
教育部博士点基金(新教师类)(No.20136118120018)
陕西省教育厅专项科研计划项目(No.14JK1529)
关键词
柔性车间调度
粒子群算法
一维粒子编码
启发式规则
Flexible Job-shop Scheduling（FJS）
Particle Swarm Optimization（PSO）
unidimensional-encoded particle
heuristic rules
