摘要
针对考虑机器加工约束的不相关并行机分批调度问题,以工件种类切换次数和机器启停评价函数为优化目标,提出一种多目标启发式狼群算法进行求解。该算法在生成初始种群的过程中,融入列表反向学习和基于机器加工效率的启发式策略,并设计了一种不规则实数矩阵编码方式来实现任务分批。采用局部和全局邻域搜索相结合的方式实现狼群算法中智能行为搜索,通过分批调整学习机制对当前结果进行邻域搜索,利用改进整数解Pareto非支配排序方式循环迭代。最后通过不同规模实际算例测试和相关算法比较,验证了该算法的有效性和优越性。
In this study,we propose a multi-objective heuristic wolf pack algorithm to tackle the issues presented by several workpiece type switches and machine start-stop evaluation functions as the optimization objectives for the unrelated parallel machine batch scheduling.During the generation of the initial population,the algorithm incorporate list-based backward learning and heuristic strategies using machine processing efficiency and design an irregular real matrix encoding method for achieving task binning.The proposed model used a combination of local and global neighborhood search to implement intelligent behavioral search in the wolf pack algorithm.Furthermore,a neighborhood search is performed using batched adjustment learning mechanism for the current results,and the improve integer solution Pareto non-dominated sorting method is used for circular iteration.Finally,the effectiveness and superiority of the algorithm are verified by testing practical arithmetic cases of different scales and comparing related algorithms.
作者
荀洪凯
陶翼飞
张源
何李
XUN Hongkai;TAO Yifei;ZHANG Yuan;HE Li(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650504,China;Kunming Cigarette Factory,Hongyun Honghe Tobacco(Group)Co.Ltd.,Kunming 650202,China)
出处
《信息与控制》
CSCD
北大核心
2023年第1期93-103,114,共12页
Information and Control
基金
国家自然基金项目(51165014)。
关键词
并行机分批调度
多目标启发式狼群算法
工件种类切换次数
启停评价函数
列表反向学习策略
分批调整学习机制
parallel machine batch scheduling
multi-objective heuristic wolf pack algorithm
the number of workpiece type switching
start-stop evaluation function
list-based reverse learning strategy
batch adjustment learning mechanism
