摘要
为充分发挥再制造产业的潜力,在保证商业利益的同时提升其对于环境保护的贡献,针对带有流水线型重加工线的再制造系统的特点,在混合流水线调度决策研究中加入对节约能源因素的考虑,提出一种改进的多目标人工蜂群算法,并应用机器闲时判断开关机策略进一步减少能源消耗.以最小化最大完工时间与最小化能源消耗量为优化目标,建立双目标数学模型;修改经典单目标人工蜂群算法为双目标算法后引入了精英策略、双重邻域搜索,保证改编算法的收敛速度,加入局部最优逃脱算子,加强改编算法的局部搜索能力.由于产品组装具有成套要求,产品的分解与重加工工序可发生变动,产生大量机器闲时,通过判断开关机策略节能,并利用带有精英策略的改进模拟退火算法求解能源消耗量计算子问题.对算法进行数值计算并与已有代表性算法比较,结果表明该方法是有效、可行的,在目标系统中应用此方法能够在保证完工时间的同时取得可观的节能效果.
To explore the potential of remanufacturing industry and enhance its contribution to environmental protection without reducing commercial interests,energy saving is considered in the study of scheduling decisions for the remanufacturing system with parallel flow-shop-type reprocessing lines. An improved multi-objective artificial bee colony algorithm is proposed while turning off idle machine policy helps further cut down energy consumption.First,bi-objective mathematical model is established to minimize the makespan and energy consumption. On this basis,an original artificial bee colony algorithm was adapt to multi-objective algorithm with elite strategy and double neighborhood search to ensure the convergence of the algorithm,and local optimal escape operator was brought to improve the exploitation of the algorithm. Because of the component matching requirement of product assembly,the disassembly operation and reprocessing operation are not fixed,leaving machines lots of idle time to be decided if closing machine benefits saving energy,and the energy consumption subproblem is solved by a simulated annealing algorithm with elite strategy. Finally,numerical calculation and comparison with the existing typical algorithm demonstrate that the proposed algorithm is valid and feasible,and the energy can be saved substantially in desired makespan.
作者
周炳海
刘文龙
ZHOU Binghai, LIU Wenlong(College of Mechanical Engineering, Tongji University, Shanghai 201804, Chin)
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2018年第7期111-118,共8页
Journal of Harbin Institute of Technology
基金
国家自然科学基金资助项目(71471135)
关键词
再制造
节约能源
人工蜂群算法
多目标
调度
remanufacturing
energy saving
artificial bee colony algorithm
multi-objective
scheduling