含不相关机的多目标混合流水车间调度
Multi-objective hybrid flow shop scheduling with unrelated parallel machines
轩华 1关潇风 1王薛苑1
作者信息
- 1. 郑州大学管理工程学院,河南郑州 450001
- 折叠
摘要
考虑不相关机和传送等因素的多阶段混合流水车间问题,以最小化最大完工时间和总能耗为优化目标建立整数规划模型.针对该问题,提出一种多 目标离散灰狼优化算法来求解.设计基于机器分配码和速度选择码的编码方式和基于最短处理时间原则的解码方案;采用反向学习策略改进初始灰狼种群质量;将基于多点变异的 自走模式和基于均匀两点交叉与多点交叉的跟随模式结合构成搜索模式以协调开发和搜索能力;引入精英保留策略确保优良个体不丢失.通过一系列的仿真实验验证了该算法的有效性.
Abstract
For the multi-stage hybrid flow shop scheduling problem with unrelated parallel machines and transportation,a integer programming model was established to minimize maximum completion time and energy consumption.For this problem,a multi-objective discrete gray wolf optimization algorithm was proposed.The coding method based on machine allocation code and speed selection code and the decoding method based on the principle of minimum processing time were designed.The reverse learning strategy was used to improve the quality of initial wolf population.The self-walking mode based on multi-point mutation and the following mode based on uniform two-point crossover and multi-point crossover were combined to form a search mode to coordi-nate exploitation and exploration abilities.In the end,elite retention strategy was introduced to ensure that good individuals were not lost.Results of a series of simulation experiments show that the algorithm is effective.
关键词
多阶段混合流水车间/离散灰狼优化算法/不相关机/多/目标优化/绿色调度/最小化最大完工时间/传送时间Key words
multi-stage hybrid flow shop/discrete gray wolf optimization algorithm/unrelated parallel machines/multi-objective optimization/green scheduling/makespan/transportation time引用本文复制引用
基金项目
国家自然科学基金项目(U1804151)
河南省科技攻关计划基金项目(202102310310)
出版年
2024
NETL
NSTL
万方数据