基于混合遗传算法的云制造环境下柔性作业车间调度方案

李云龙; 罗国富; 文笑雨; 李凯; 杨幸博; 张俊豪

doi:10.12187/2020.03.012

CN 41-1437/TS ISSN 2096-1553

李云龙 , 罗国富 , 文笑雨 , 李凯 , 杨幸博 , 张俊豪

李云龙, 罗国富, 文笑雨, 等. 基于混合遗传算法的云制造环境下柔性作业车间调度方案[J]. 轻工学报, 2020, 35(3): 99-108. doi: 10.12187/2020.03.012

引用本文: 李云龙, 罗国富, 文笑雨, 等. 基于混合遗传算法的云制造环境下柔性作业车间调度方案[J]. 轻工学报, 2020, 35(3): 99-108. doi: 10.12187/2020.03.012

LI Yunlong, LUO Guofu, WEN Xiaoyu, et al. Flexible job shop scheduling in cloud manufacturing environment based on hybrid genetic algorithm[J]. Journal of Light Industry, 2020, 35(3): 99-108. doi: 10.12187/2020.03.012

Citation: LI Yunlong, LUO Guofu, WEN Xiaoyu, et al. Flexible job shop scheduling in cloud manufacturing environment based on hybrid genetic algorithm[J]. Journal of Light Industry, 2020, 35(3): 99-108. doi: 10.12187/2020.03.012

基于混合遗传算法的云制造环境下柔性作业车间调度方案

郑州轻工业大学机电工程学院/河南省机械装备智能制造重点实验室, 河南郑州 450002

作者简介: 李云龙(1993-),男,河南省周口市人,郑州轻工业大学硕士研究生,主要研究方向为数字化设计与制造.;

基金项目: 国家自然科学基金项目（51905494）；郑州轻工业大学硕士科技创新基金项目（2018015）
中图分类号: F416.2;TH165

Flexible job shop scheduling in cloud manufacturing environment based on hybrid genetic algorithm

College of Mechanical and Electrical Engineering/He'nan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Received Date: 2019-08-30

CLC number: F416.2;TH165

摘要: 针对云制造环境下柔性作业车间调度产生的离散型加工设备的空闲时间利用及其冲突问题，提出了一种基于混合遗传算法的云制造环境下柔性作业车间调度方案：在保证车间任务顺利完成的前提下，对车间剩余能力进行界定后封装发布到云平台上；以最小惩罚总成本为目标，结合车间生产调度实际情况选择云订单任务一起加工；采用遗传变邻域混合算法求解云任务工件最优调度顺序.基准算例测试结果表明，该方案实现了车间自身生产任务和云平台任务协同生产，提高了企业的收益和资源利用率.
- 云制造 /
- 剩余能力 /
- 柔性作业车间调度 /
- 混合遗传算法
Abstract: Aiming at the problem of idle time utilization and conflict of discrete processing equipment generated by flexible job shop scheduling in cloud manufacturing environment, a flexible job shop scheduling scheme in cloud manufacturing environment based on hybrid genetic algorithm was proposed.Under the premise of ensuring the smooth completion of workshop tasks, the residual capacity of the workshop was defined and then packaged and released to the cloud platform. Taking the minimum penalty total cost as the goal,combined with the actual situation of workshop production scheduling, the cloud order tasks were selected to process together, and the genetic variable neighborhood hybrid algorithm was used to solve the optimal scheduling sequence of cloud tasks, and the optimal scheduling scheme was formulated. The benchmark test results showed that the scheme realized the collaborative production of workshop production tasks and cloud platform tasks, and improved the enterprise's revenue and resource utilization.
- cloud manufacturing /
- surplus capacity /
- flexible job shop scheduling /
- hybrid genetic algorithm
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沈阳化工大学材料科学与工程学院沈阳 110142

李云龙, 罗国富, 文笑雨, 等. 基于混合遗传算法的云制造环境下柔性作业车间调度方案[J]. 轻工学报, 2020, 35(3): 99-108. doi: 10.12187/2020.03.012

引用本文: 李云龙, 罗国富, 文笑雨, 等. 基于混合遗传算法的云制造环境下柔性作业车间调度方案[J]. 轻工学报, 2020, 35(3): 99-108. doi: 10.12187/2020.03.012

基于混合遗传算法的云制造环境下柔性作业车间调度方案

作者简介:李云龙(1993-),男,河南省周口市人,郑州轻工业大学硕士研究生,主要研究方向为数字化设计与制造.

郑州轻工业大学机电工程学院/河南省机械装备智能制造重点实验室, 河南郑州 450002

收稿日期: 2019-08-30

基金项目: 国家自然科学基金项目（51905494）；郑州轻工业大学硕士科技创新基金项目（2018015）

关键词:

摘要: 针对云制造环境下柔性作业车间调度产生的离散型加工设备的空闲时间利用及其冲突问题，提出了一种基于混合遗传算法的云制造环境下柔性作业车间调度方案：在保证车间任务顺利完成的前提下，对车间剩余能力进行界定后封装发布到云平台上；以最小惩罚总成本为目标，结合车间生产调度实际情况选择云订单任务一起加工；采用遗传变邻域混合算法求解云任务工件最优调度顺序.基准算例测试结果表明，该方案实现了车间自身生产任务和云平台任务协同生产，提高了企业的收益和资源利用率.

English Abstract

Flexible job shop scheduling in cloud manufacturing environment based on hybrid genetic algorithm

College of Mechanical and Electrical Engineering/He'nan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China

Received Date: 2019-08-30

Abstract: Aiming at the problem of idle time utilization and conflict of discrete processing equipment generated by flexible job shop scheduling in cloud manufacturing environment, a flexible job shop scheduling scheme in cloud manufacturing environment based on hybrid genetic algorithm was proposed.Under the premise of ensuring the smooth completion of workshop tasks, the residual capacity of the workshop was defined and then packaged and released to the cloud platform. Taking the minimum penalty total cost as the goal,combined with the actual situation of workshop production scheduling, the cloud order tasks were selected to process together, and the genetic variable neighborhood hybrid algorithm was used to solve the optimal scheduling sequence of cloud tasks, and the optimal scheduling scheme was formulated. The benchmark test results showed that the scheme realized the collaborative production of workshop production tasks and cloud platform tasks, and improved the enterprise's revenue and resource utilization.

参考文献 (16)