基于Flexsim的车间调度优化

基于三维动画仿真软件Flexsim,文中对航空附件加工车间这种多品种、小批量生产的作业车间（Job-Shop）进行了调度优化研究。介绍了Flexsim连接数据库的技术与遗传算法求解生产调度的方法;在Flexsim中建立虚拟生产车间模型,并且在Flexsim虚拟车间模型内部嵌入C＋＋数据库操纵程序,将仿真模型与生产管理数据库连接,使模型可以实时采集生产数据;最后通过实例说明Flexsim仿真与调度优化相结合的方法可以有效地提高航空附件加工车间的效益,证明了方法的有效性。     

基于混合粒子群算法的航空零件生产车间柔性调度研究

航空零件生产车间调度具有目标多样性和不确定性问题,普通方法难以求得最佳调度顺序。针对这个问题,采用了混合粒子群算法优化加工工序。该算法通过个体交叉,变异的方法来反复迭代寻优,从而得到更好的零件加工工序。通过仿真实验验证了该算法的有效性,从而为航空零件加工车间调度优化提供了一种新的思路。     

基于Flexsim的加工车间的仿真

本文以某生产车间及其仓储为例,探讨了加工生产及物流的规划设计.基于面向对象建模理论,将厂房生产线系统设计方案转换成仿真模型,在Flexsim仿真平台基础上对这个模型进行仿真,得到加工机床的仿真结果数据,利用这些仿真数据可以画出两种不同机床加工区的空闲率折线图和机器使用率折线图,通过折线图分析,可以分析各个机器的使用率,设置适当的参数使机床达到系统资源配置优化的目的.     

基于窗口的印刷车间作业动态调度研究

为了解决实际印刷车间突发设备故障和紧急插单问题,采用滚动窗口技术结合遗传算法的方法,建立适合实际印刷车间生产的动态再调度模型;设定若干印品订单、机器设备的加工工序以及各工序加工时间、工序约束条件等,以订单的最大最小加工时间和再调度的偏离度为多目标优化,采用周期与事件混合驱动策略,将滚动窗口再调度机制和遗传算法相结合进行流程设计和编码,构建印刷车间再调度模型;采用标准问题FT06和FT01验证了文章设计的模型算法的有效性和可行性;运行程序,模拟正常加工时紧急插单和机器故障突发时,系统生产新的调度计划即调度甘特图,仿真结果表明该动态调度模型可以用于印刷作业的正常排产调度,在遇突发状况时可生成稳定、符合交货日期的再调度方案。     

基于多Agent柔性制造单元的生产调度研究

针对基于制造单元的作业车间的生产调度问题进行了研究,结合多代理的智能性、灵活性和遗传算法的智能优化能力,建立基于多智能体的柔性制造单元的作业车间的调度系统模型.然后,提出了集成多智能体和遗传算法的动态调度策略和调度协商机制;最后,应用此方法完成了常规调度和异常调度的仿真算例.结果表明所开发系统可以解决基于加工单元的制造...     

基于超网络的集成工艺决策与调度问题研究

针对多工艺产品的加工路线决策与车间调度方案不能同步制定的问题,在制造车间数字化背景下,提出集成车间不同要素信息的特征—工序—机器—工人的超网络结构,建立基于超网络的加工路线决策与车间调度模型,设计一种集成工艺决策与车间调度的两阶段混合遗传算法求解模型。在工艺决策阶段,设计特征—工序双层矩阵编码染色体保持加工路线的多样性,并在遗传算法的执行过程中使用变邻域搜索方法增强算法的局部搜索能力;在车间调度阶段,采用NSGA-Ⅱ算法优化调度模型,将得到的调度方案多目标值返回至工艺决策阶段用于加工路线的适应度评价。最后通过仿真实验验证了该算法的可行性与有效性。     

汽车冲压车间生产调度仿真系统的设计

生产调度是冲压车间生产管理的基本内容,通过仿真方法可以优化生产调度,提高生产线的利用率。通过分析某汽车制造企业冲压生产线的工艺流程和调度要求,提出了有关合理假设,以最大化节约时间成本为指导思想,建立了汽车冲压车间生产调度仿真模型。在仿真算法设计原则基础上,详细说明了算法的执行流程。最后简单介绍了系统的功能,并给出一个生产调度实例。实例结果表明该系统能获得合理的调度方案,适用于企业冲压生产线的调度问题。     

以多Agent系统为架构的实时调度模型

构建了一个适用于柔性作业车间(FJSP)调度的实时模型,将车间加工设备和作业封装为Agent,通过Agent之间的招投标实现实时调度。采用了虚拟货币机制来构造实时调度模型,该机制可使交货期紧张的作业拥有更高的优先级选择加工机器,使调度方案在满足实时性的同时得到优化;模型还引入急件插入机制,在处理紧急任务时可在尽量减少对其他任务干扰的前提下顺利实现对紧急任务的调度。与其他同类调度模型相比,提出的模型在实时性、调度质量上和用户满意度上都拥有其独特优势。     

汽车冲压车间生产调度仿真系统的设计

生产调度是冲压车间生产管理的基本内容,通过仿真方法可以优化生产调度,提高生产线的利用率.通过分析某汽车制造企业冲压生产线的工艺流程和调度要求,提出了有关合理假设,以最大化节约时间成本为指导思想,建立了汽车冲压车间生产调度仿真模型.在仿真算法设计原则基础上,详细说明了算法的执行流程.最后简单介绍了系统的功能,并给出一个生产调度实例.实例结果表明该系统能获得合理的调度方案,适用于企业冲压生产线的调度问题.     

基于MAS的生产车间动态调度系统的研究

简要介绍了车间调度的基本问题,发展现状以及多智能体技术.在分析车间生产调度特点的基础上,提出了基于MAS的动态车间生产调度模型.该模型把车间生产调度系统分为调度代理、任务代理和资源代理等.代理之间采用了基于改进的合同网的关系网模型,并引入了基于混合遗传算法的调度模块,为解决车间加工动态调度问题提供了一种新的方法.仿真实验结果表明,该系统更好满足了车间调度的动态化、高效化、智能化、实用化的要求.     

面向柔性作业分布式车间的分层调度模型研究

针对多车间分布式制造系统调度优化问题,结合车间实际生产情况,提出一种基于目标级联法和遗传算法的层次调度模型。模型将生产调度过程划分为生产计划层、车间调度层和零件规划层,并将整体时间最短的优化目标划分到各个层次,通过层层优化达到时间最优后反馈至上层,以实现整体调度时间最优。以3个制造车间协调调度问题为例,验证了该模型在零件分配和零件的工艺路线选择上的合理性和有效性。     

Digital Twin Enhanced Dynamic Job-Shop Scheduling

For dynamic scheduling, which is daily decision-making in a job-shop, machine availability prediction, disturbance detection and performance evaluation are always common bottlenecks. Previous research efforts on addressing the bottlenecks primarily emphasize on the analysis of data from the physical job-shop, but with little connection and convergence with its virtual models and simulated data. By introducing digital twin (DT), further convergence between physical and virtual spaces of the job-shop can be achieved, which greatly enables dynamic scheduling. DT fuses both real and simulated data to provide more information for the prediction of machine availability on one hand; and on the other hand, it helps to detect disturbances through comparing the physical machine with its continuously updated digital counterpart in real time, triggering timely rescheduling when needed. It also enables comprehensive performance evaluation for rescheduling using multiple-dimension models, which can describe geometric properties, physics parameters and behaviors of the machines. In the paper, a five-dimension DT for a machine in the job-shop is introduced first, then the DT-based machine availability prediction, disturbance detection and performance evaluation methods are explored. Based on this, a DT-enhanced dynamic scheduling methodology is proposed. A scheduling process of making hydraulic valves in a machining job-shop is taken as a case study to illustrate the effectiveness and advantages of the proposed method.     

A fast estimation of distribution algorithm for dynamic fuzzy flexible job-shop scheduling problem

Due to the complicated circumstances in workshop, most of the conventional scheduling algorithms fail to meet the requirements of instantaneity, complexity, and dynamicity in job-shop scheduling problems. Compared with the static algorithms, dynamic scheduling algorithms can better fulfill the requirements in real situations. Considering that both flexibility and fuzzy processing time are common in reality, this paper focuses on the dynamic flexible job-shop scheduling problem with fuzzy processing time (DfFJSP). By adopting a series of transforming procedures, the original DfFJSP is simplified as a traditional static fuzzy flexible job-shop problem, which is more suitable to take advantage of the existing algorithms. In this paper, estimation of distribution algorithm (EDA) is brought into address the post-transforming problem. An improved EDA is developed through making use of several elements omitted in original EDA, including the historical-optimal solution and the standardized solution vectors. The improved algorithm is named as fast estimation of distribution algorithm (fEDA) since it performs better in convergence speed and computation precision, compared with the original EDA. To sum up, the ingenious transformation and the effective fEDA algorithm provide an efficient and practical way to tackle the dynamic flexible fuzzy job-shop scheduling problem.     

应用Agent理论的生产调度系统研究

生产调度问题,一般可根据生产流程的不同分为Job-shop调度和Flowshop调度两大类(也有学者认为,存在两者相结合的第三类—混合调度)。该文研究以最小化Makespan为目标的Flowshop调度问题。基于Agent理论,提出采用Flowshop复合代理体(Flowshop-Compound-Agent,FSCA)求解Flowshop调度问题的方法。在给出FSCA的结构及其实现的基础上,通过毛纺企业制条车间的实例说明了使用FSCA解决Flowshop调度问题的有效性。     

Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms

In contrast to traditional job-shop scheduling problems, various complex constraints must be considered in distributed manufacturing environments; therefore, developing a novel scheduling solution is necessary. This paper proposes a hybrid genetic algorithm (HGA) for solving the distributed and flexible job-shop scheduling problem (DFJSP). Compared with previous studies on HGAs, the HGA approach proposed in this study uses the Taguchi method to optimize the parameters of a genetic algorithm (GA). Furthermore, a novel encoding mechanism is proposed to solve invalid job assignments, where a GA is employed to solve complex flexible job-shop scheduling problems (FJSPs). In addition, various crossover and mutation operators are adopted for increasing the probability of finding the optimal solution and diversity of chromosomes and for refining a makespan solution. To evaluate the performance of the proposed approach, three classic DFJSP benchmarks and three virtual DFJSPs were adapted from classical FJSP benchmarks. The experimental results indicate that the proposed approach is considerably robust, outperforming previous algorithms after 50 runs.     

面向注塑车间的高效作业调度方法

针对注塑生产车间作业调度效率较低的问题,在“归模”的基础上提出一种改进的作业调度方法。该方法通过合并具有相同模具清单作业减少生产所需时间,再通过小机型注塑机优先排产降低车间能耗。理论分析和实验评测结果表明,该方法可以提高生产能力,降低耗电量超过50%,使注塑车间作业调度效率更高。     

免疫克隆选择算法求解柔性生产调度问题

为减少计算复杂度,将具有解决复杂组合优化问题的免疫克隆选择算法应用于求解柔性生产调度问题.首先设计一种有效的抗原和抗体的数据结构,用抗原表示待调度的生产计划,抗体表示高效的柔性生产调度结果;然后着重设计了用于产生高效的柔性生产调度结果的克隆免疫算子;最后运用该模型对一个实际生产系统进行仿真调度决策,实验评估结果验证了算法的正确性和有效性.     

精英进化策略求解柔性作业车间调度问题

柔性作业车间调度问题允许一道工序可以在多个可选机器上进行加工,减少了机器约束,增加了求解难度,是典型的NP难问题。结合其特点,设计了一种精英进化策略遗传算法求解柔性作业车间调度问题。提出了解阀值的指标,使得外部精英库中不仅保留算法每次迭代过程中的最优解,而且保留最优值相等而调度方案不同的解,为调度人员提供更多选择。通过制造企业中的实际案例和其它文献中的案例对提出的精英进化策略遗传算法进行了测试,结果证明提出方法的有效性。     

基于多智能体的虚拟企业群协同生产调度模式研究

为了实现虚拟企业群的协同生产调度, 提高企业群整体运作水平, 建立了基于多智能体的虚拟企业群制造企业生产调度模型, 分析了生产计划调度及信息交互作用。在企业协同层, 提出了基于多目标和价格协商的供应商选择过程, 确定了合作伙伴的初选过程方法, 企业层采用了基于EDD+SCR规则组合确定企业任务分配, 并为所有订单任务确定交货期, 各车间层则根据企业层的生产计划进行车间生产调度安排。最后, 运用Swarm仿真软件平台对案例进行仿真分析, 验证了提出的生产调度模式可以有效提高各企业的车间负载率, 并使车间负载率在短时间内达到稳定状态, 从而合理有效地利用企业资源。     

Bi-level dynamic scheduling architecture based on service unit digital twin agents

Pure reactive scheduling is one of the core technologies to solve the complex dynamic disturbance factors in real-time. The emergence of CPS, digital twin, cloud computing, big data and other new technologies based on the industrial Internet enables information acquisition and pure reactive scheduling more practical to some extent. However, how to build a new architecture to solve the problems which traditional dynamic scheduling methods cannot solve becomes a new research challenge. Therefore, this paper designs a new bi-level distributed dynamic workshop scheduling architecture, which is based on the workshop digital twin scheduling agent and multiple service unit digital twin scheduling agents.Within this architecture, scheduling a physical workshop is decomposed to the whole workshop scheduling in the first level and its service unit scheduling in the second level. On the first level, the whole workshop scheduling is executed by its virtual workshop coordination (scheduling) agent embedded with the workshop digital twin consisting of multi-service unit digital twins. On the second level, each service unit scheduling coordinated by the first level scheduling is executed in a distributed way by the corresponding service unit scheduling agent associated with its service unit digital twin. The benefits of the new architecture include (1) if a dynamic scheduling only requires a single service unit scheduling, it will then be performed in the corresponding service unit scheduling without involving other service units, which will make the scheduling locally, simply and robustly. (2) when a dynamic scheduling requires changes in multiple service units in a coordinated way, the first level scheduling will be executed and then coordinate the second level service unit scheduling accordingly. This divide-and-then-conquer strategy will make the scheduling easier and practical.The proposed architecture has been tested to illustrate its feasibility and practicality.