作业车间调度
计算机科学
数学优化
序列(生物学)
调度(生产过程)
水准点(测量)
工作车间
解码方法
算法
资源限制
流水车间调度
人工智能
地铁列车时刻表
数学
分布式计算
地理
操作系统
生物
遗传学
大地测量学
作者
Sunfei Ye,Tian-Ming Bu
标识
DOI:10.1109/smc52423.2021.9659113
摘要
The flexible job shop scheduling problem (FJSP) is a popular research topic in the field of production scheduling. Traditional FJSP ignores sequence-dependent setup times and resource constraints. However, these constraints should be taken into account during the manufacturing process. To deal with the extended FJSP with sequence-dependent setup times and resource constraints, this paper proposes a self-learning harris hawks optimization (SLHHO) algorithm. The goal of this algorithm is to get the smallest makespan. In the proposed algorithm, we use two-vector code to encode machine sequence and operation sequence, design a new decoding method to satisfy the resource constraints, and use reinforcement learning to optimize the key parameters of the algorithm intelligently. We compare it with other three effective algorithms on benchmark instances with varying scales. The experimental result shows that SLHHO performs better and can get a more effective solution.
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