摘要
针对军工企业中常见的研制订单和批产订单混合生产调度问题,提出研制批产混合调度模型.在作业车间柔性调度问题的基础上,考虑加工时间受加工熟练程度的影响和研制订单的工时不确定性,利用学习效应对研制订单工时进行修正,使用区间数方法对工时不确定性进行描述.以最小化提前/拖期惩罚区间为目标,对工序的加工顺序和加工设备进行决策.使用改进的遗传算法对模型进行求解,算法的改进在于提出基于区间数的选择、精英交叉和变概率变异方法.通过数值实验对模型和算法的性能进行分析.实验结果表明,模型有效,算法在求解速度和质量上表现良好.
A hybrid scheduling model was proposed for military enterprises, which featured the mixture of new product development and batch production process. The model was based on the flexible job shop problem. The learning effect was applied to modify the processing time under the influence of the processing proficiency while an interval number method was used to describe the uncertain processing time of new developing product. The objective was to minimize the earliness and tardiness penalty interval by making optimal decisions on the processing sequence and machine assignment. An improved genetic algorithm was developed to solve the model, combining an interval number based selection method, elite strategy and mutation operator with changeable probability. Numerical experiments have been conducted to validate the model and approach. Results showed the effectiveness of the model, as well as high performance of the proposed genetic algorithm in terms of the computational speed and solution quality.
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2016年第11期2224-2230,2244,共8页
Journal of Zhejiang University：Engineering Science
基金
国家自然科学基金资助项目(51475304)
上海市自然科学基金资助项目(12ZR1414400)
关键词
生产调度
研制批产混合
学习效应
成组生产
production schedule
mixed schedule
learning effect
group manufacturing
