融入概率学习的混合差分进化算法求解绿色分布式可重入作业车间调度
Hybrid differential evolution integrated with probability learning for green distributed reentrant job shop scheduling
胡蓉 1伍星 2毛剑琳 3钱斌3
作者信息
- 1. 昆明理工大学信息工程与自动化学院,云南昆明 650500;昆明理工大学机电工程学院,云南昆明 650500
- 2. 云南机电职业技术学院,云南昆明 650000;昆明理工大学机电工程学院,云南昆明 650500
- 3. 昆明理工大学信息工程与自动化学院,云南昆明 650500
- 折叠
摘要
本文针对绿色分布式可重入作业车间调度问题(GDRJSSP),提出一种融入概率学习的混合差分进化算法(HDE_PL),以实现最大完工时间和总能耗最小.根据GDRJSSP的问题特点,设计编码和解码规则,并采用差分进化算法执行全局搜索来发现优质解区域.为能更明确地引导全局搜索方向,设计基于贝叶斯网络结构的多维概率模型合理学习和积累优质解(即当前种群中的较优解)的模式信息.结合问题解的结构特征,提出基于关键路径的4种邻域结构来构造局部搜索,并设计基于非关键路径的节能策略来提升算法获取低能耗非劣解的能力.仿真实验和算法对比验证了HDE_PL可有效求解GDRJSSP.
Abstract
Aiming at the green distributed reentrant job shop scheduling problem(GDRJSSP),a hybrid differential evolution incorporated with probabilistic learning(HDE_PL)is proposed to minimize the maximum completion time and the total energy consumption.According to the problem characteristics of the GDRJSSP,the rule of job allocation among factories and the encoding and decoding rules are designed,and the differential evolution algorithm is used to perform global search to find high-quality solution regions.In order to guide the global search direction more clearly,a multi-dimensional probability model based on Bayesian network structure is designed to reasonably learn and accumulate the pattern information of high-quality solutions(i.e.,the better solutions in the current population).Combined with the structural characteristics of the problem solution,four neighborhoods based on the critical path are proposed to construct the local search,and an energy saving strategy based on the non-critical path is devised to enhance the ability of the algorithm to obtain low-power non-dominated solutions.Simulation experiments and algorithm comparisons verify that HDE_PL can effectively solve the GDRJSSP.
关键词
差分进化/绿色调度/分布式调度/可重入作业车间调度问题Key words
differential evolution/green scheduling/distributed scheduling/reentrant job shop scheduling problem引用本文复制引用
基金项目
国家自然科学基金(62173169)
国家自然科学基金(61963022)
云南省基础研究重点项目(202201AS070030)
出版年
2024
国家科技期刊平台
NSTL
万方数据