.png){kind=icon}
返回
Multi-objective dynamic distributed flexible job shop scheduling problem considering uncertain processing time
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-024-04803-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a dynamic distributed flexible job-shop scheduling problem considering uncertain processing time of operation (DDFJSPT) is proposed for the first time. A two-stage efficient memetic algorithm (EMA) is presented to solve the DDFJSPT aiming at minimizing the maximum processing time and maximum energy consumption. In the EMA, a new initialization method is designed to balance the load of the initial population, and some efficient crossover and mutation operators and an effective local search operator are proposed to expand the solution space and improve the solution diversity. In addition, three different rescheduling strategies are designed to obtain high quality solutions under different types of disturbances on the processing time of operations. Through a large number of experiments, the superiority of the proposed EMA is verified by comparing its experimental results with the ones of other three well-known algorithms.
Keyword:
Dynamic distributed flexible job-shop scheduling problem
Uncertain processing time
Dynamic rescheduling
Memetic algorithm
Multi-objective optimization
期刊
C
IF:
4.1
论文数:
5.1K
被引数:
7.5K
机构
引用论文
.png){kind=icon}
Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏Validity and sensitivity to change of the Patient Specific Functional Scale used during rehabilitation following proximal humeral fracture肱骨近端骨折后康复期间使用的患者特定功能量表变化的有效性和敏感性
PREAI
分享收藏Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems基于深度强化学习的多agent制造系统动态作业车间调度
PREAI
分享收藏
查看更多内容