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计算机系统应用:2019,28(1):119-126
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基于改进GSO算法的柔性作业车间E/T调度问题
(东华大学 旭日工商管理学院, 上海 200051)
Flexible Job Shop Scheduling Based on Improved Glowworm Swarm Optimization
(The Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China)
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投稿时间:2018-07-05    修订日期:2018-07-27
中文摘要: 针对机器资源和加工路线可选择情况下的柔性车间调度,以最小最大完工时间和时间惩罚成本为目标建立柔性车间E/T调度模型.根据问题特点,提出一种改进的萤火虫算法(GSO),算法设计了一种具有贪婪思想的编码策略,一个萤火虫个体表示工序加工顺序和工序加工位置;采用自适应选择策略,使步长自适应,提高算法精度;引入POX交叉、邻域交换和反序排序方法提高算法局部和全局寻优能力,并利用贪婪思想,提高算法的收敛速度.通过经典算例和实例验证算法性能,实验结果表明改进的萤火虫算法求解柔性车间调度问题的有效性.
Abstract:For flexible shop scheduling in the case of machine resources and processing route selectable, a flexible shop model is established with minimum and maximum completion time and time penalty costs as targets. According to the characteristics of the problem, an improved firefly algorithm is proposed. The algorithm designs a coding strategy with greedy ideas. A firefly individual represents the processing sequence and process processing position. It adopts an adaptive selection strategy to adapt the step length and improve the accuracy of the algorithm. The introduction of POX cross strategy to improve the algorithm's local and global optimization capabilities, and the use of greed to improve the convergence speed of the algorithm. The performance of the algorithm is verified by comparison with example simulations and algorithms. The experimental results show that the improved firefly algorithm is effective for solving the flexible shop scheduling problem.
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夏俊红,郑建国.基于改进GSO算法的柔性作业车间E/T调度问题.计算机系统应用,2019,28(1):119-126
XIA Jun-Hong,ZHENG Jian-Guo.Flexible Job Shop Scheduling Based on Improved Glowworm Swarm Optimization.COMPUTER SYSTEMS APPLICATIONS,2019,28(1):119-126

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