考虑复杂随机来料的批量流作业调度  

Lot streaming scheduling with complex random arrival

作　　者：章旸 王铖恺 刘冉[1] ZHANG Yang;WANG Chengkai;LIU Ran(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学机械与动力工程学院,上海200240

出　　处：《计算机集成制造系统》2022年第4期1099-1111,共13页Computer Integrated Manufacturing Systems

基　　金：上海市科委重大资助项目(17DZ1101202)。

摘　　要：多种类、可分批的工件生产调度问题是目前车间制造的关键问题之一,需同时考虑分批和调度两方面的优化决策。进一步地,在实际生产环境下还存在子批切换和工件的来料时间不确定的问题,大大增加了生产调度的困难,目前对此类问题的研究也尚未开始。鉴于此,以最小化最大完成时间为优化目标,基于场景采用样本均值近似(SAA)方法建立了考虑换模时间与随机来料的批量流调度模型,对问题分别设计了基于Benders分解的精确求解方法,以及结合Benders分解和禁忌搜索的启发式算法,实现了分批与调度两个问题的优化,通过数值实验验证了方法的有效性。The production scheduling problem of multiple types and batches of jobs is a crucial issue in current workshop manufacturing.In the real production environment,it is necessary to consider the setup time of machines during sub-lot switching and the highly complicated random arrival time of different jobs,which cause great difficulties in production scheduling.Research on such issues has not yet begun.Based on sample average appropriation method,an optimized objective model of lot streaming with setup time and random arrival of jobs was built for minimizing the makespan.Meanwhile,Benders decomposition was used to solve the problem precisely and the heuristic algorithm combing Benders decomposition and tabu search was designed to solve the problem.These algorithms could implement parallel optimization of job splitting and scheduling problems.The effectiveness of the proposed method was verified by numerical experiments.

关 键 词：车间作业调度 复杂随机来料 批量流 样本均值近似 Benders分解 禁忌搜索 

分 类 号：TH186[机械工程—机械制造及自动化]