[1]轩华,樊银格,李冰.含忽略工序和不相关机的混合流水车间调度[J].智能系统学报,2022,17(3):459-470.[doi:10.11992/tis.202103006]
XUAN Hua,FAN Yinge,LI Bing.Hybrid flowshop scheduling with missing operations and unrelated machines[J].CAAI Transactions on Intelligent Systems,2022,17(3):459-470.[doi:10.11992/tis.202103006]
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XUAN Hua,FAN Yinge,LI Bing.Hybrid flowshop scheduling with missing operations and unrelated machines[J].CAAI Transactions on Intelligent Systems,2022,17(3):459-470.[doi:10.11992/tis.202103006]
含忽略工序和不相关机的混合流水车间调度
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第3期
页码:
459-470
栏目:
学术论文—机器学习
出版日期:
2022-05-05
- Title:
- Hybrid flowshop scheduling with missing operations and unrelated machines
- Keywords:
- missing operations; unrelated parallel machines; hybrid flowshop; global search; self-adaptive genetic algorithm; neighborhood search; maximum completion time; genetic migrating birds optimization algorithm
- 分类号:
- TP39;TB49
- 摘要:
- 研究从炼钢等生产过程提炼出的含忽略工序和不相关并行机的混合流水车间调度问题，以最小化最大完工时间为目标，建立整数规划模型，并提出结合全局搜索、自适应遗传算法和候鸟优化的遗传候鸟优化算法以求解该模型。在算法中采用与处理时间相关的全局搜索和随机程序以获得初始种群，提出自适应交叉和变异操作改进遗传算法解，在迭代进程中，引入基于工件、机器和工序位3种邻域搜索结构的候鸟优化算法更新最佳解。仿真实验中将遗传候鸟优化算法的实验结果与几种启发式算法进行对比，证明了模型和算法的有效性。
- Abstract:
- The hybrid flowshop scheduling problem with missing processes and unrelated parallel machines extracted from steelmaking and other production processes is studied. An integer programming model is formulated to minimize the maximum completion time, and a genetic migrating birds optimization algorithm based on the global search, self-adaptive genetic algorithm, and migrating birds optimization is proposed to solve the model. The initial population is obtained by the global search considering the machine processing time and random procedure. Then, self-adaptive crossover and mutation operators are developed to improve the solutions of the genetic algorithm. In the iterative process, migrating birds optimization combined with three neighborhood search structures based on the job, machine, and operation position is introduced to update the best solutions. Finally, the experimental results of the genetic migrating birds optimization algorithm are compared with several heuristic algorithms to prove the effectiveness of the proposed model and algorithm.
- 参考文献/References:
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备注/Memo
收稿日期:2021-03-03。
基金项目:国家自然科学基金项目（U1804151）；河南省科技攻关计划项目（202102310310）
作者简介:轩华，教授，主要研究方向为生产计划与调度、物流优化与控制。主持国家自然科学基金项目、教育部人文社科项目、博士后基金特殊资助项目、博士后基金面上项目等科研项目8项。发表学术论文42篇;樊银格，硕士研究生，主要研究方向为物流优化与控制;李冰，教授，博士生导师，主要研究方向为物流优化与控制。河南省省级青年骨干教师。发表学术论文36篇
通讯作者:轩华.E-mail：hxuan@zzu.edu.cn
更新日期/Last Update:
1900-01-01