摘要
针对低碳柔性作业车间调度问题,提出一种基于新型优化机理的教学优化(TLBO)算法,以同时最小化总碳排放和平均延迟时间.利用3个串对问题的3个子问题单独编码,其主要步骤为教师的自学阶段和教学阶段,并运用多邻域搜索和全局搜索分别模拟教师的自学和教学活动.计算实验和结果分析表明,TLBO对于所研究的问题具有较强的搜索能力.
For the low carbon flexible job shop scheduling problem, a teaching-learning-based optimization（TLBO） algorithm with a novel optimization mechanism is presented to minimize total carbon footprint and average tardiness simultanesouly. A three-string coding method is used to represent three sub-problems independently. The main steps of TLBO algorithm are a self-learning phase and a teaching phase. Multiple neighborhood search and global search are respectively executed in the above phases. Extensive experiments are conducted and the results demonstrate that the proposed algorithm has strong search abilities for the considered problem.
出处
《控制与决策》
EI
CSCD
北大核心
2017年第9期1621-1627,共7页
Control and Decision
基金
国家自然科学基金项目(61573264
71471151)
关键词
柔性作业车间调度
教学优化算法
总碳排放
低碳调度
flexible jobshopscheduling
teaching-learning-based optimization algorithm
total carbonfootprint
lowcarbon scheduling
