基于事件及变周期驱动的作业车间动态调度

提出了事件驱动和周期性驱动相结合的动态调度策略,以变周期滚动窗口技术作为周期性驱动调度策略的实现技术;在此基础上,以融合带权重的精英策略、最优最差蚂蚁机制和阶段控制机制的混合蚁群算法作为动态调度的优化算法,对作业车间动态调度中工件取消、紧急工件、机器故障以及生产负荷变化等动态事件调度进行了研究.仿真结果表明,提出的策略和方法可行有效.     

混合粒子群算法在job-shop动态调度中的应用

提出了基于事件驱动的动态调度策略,以融合遗传算法的粒子群算法来实现作业车间生产调度,有很好的收敛精度;在此基础上,对作业车间生产调度中的工件增加及取消、机器故障等各种动态事件进行了研究,能在扰动后提供新的调度计划,有效地解决了车间动态调度的一致性和连续性的问题。     

柔性作业车间调度的多Agent协商策略

柔性作业车间调度问题是经典作业车间调度问题的扩展。为此,提出一种新的基于招投标的多Agent协商调度策略,并研究各Agent协商时的价格函数。系统主要由工件Agent和机器Agent组成,工件Agent通过招投标的方式,选择合适的机器完成加工任务,机器Agent按照市场机制通过自由竞争获得工件的加工权,根据基于规则的调度策略处理工件。用Java设计仿真实验程序,并通过实验验证所提价格协商函数的有效性。     

考虑机器故障的HFS重调度研究

针对机器故障下的混合流水车间重调度问题,在考虑工序等待时间受限约束的前提下,建立了以最大化重调度前后方案完工时间相似度和机器指派一致性为目标的重调度模型,并设计了自适应遗传算法对其进行求解。仿真实验结果表明,该模型和算法是有效的。     

基于DBR理论的柔性流水车间动态调度

针对柔性流水车间动态调度求解困难的问题,首先分析调度问题的特征,构建问题模型;然后运用DBR（鼓-缓冲器-绳子）理论对问题进行分解简化,并采用混合重调度策略和启发式算法进行动态调度;最后建立瓶颈和非瓶颈资源调度的协调机制,实现问题求解.仿真实例表明,所提出的算法是可行而有效的.     

混合流水车间提前/拖期调度问题的DE优化解

针对JIT生产模式下的混合流水车间调度问题特点,提出了采用DE算法与指派规则联合调度策略求解流水车间提前/拖期调度问题。构建了混合流水车间的提前/拖期调度模型。详细论述了DE算法的实施流程和关键问题。在算法实施过程中,首先,采用DE算法进行全局寻优,完成生产任务指派,确定某个工件在某个工序在哪个工位加工;然后采用局部指派规则来确定工件在该工序的开工时间。在满足目标完成时间(交货期)的前提下,使提前惩罚费用与拖期惩罚费用之和最小。数值计算结果证明了该算法的有效性。     

可重入混合流水车间调度的拉格朗日松弛算法

为了有效提升多重入车间的生产效率,考虑了实际生产中检查和修复过程对于逐层制造的可重入生产系统的重要性,提出了基于拉格朗日松弛算法的可重入混合流水车间的调度方法.首先进行了问题域的描述,并在此基础上以最小化加权完成时间为调度目标,建立数学规划模型.针对该调度问题提出了基于松弛机器能力约束的拉格朗日松弛算法,使松弛问题分解成工件级子问题,并使用动态规划方法建立递归公式,求解工件级子问题.随后,使用次梯度算法求解拉格朗日对偶问题.最后,对各种不同问题规模进行了仿真实验,结果表明,所提出的调度算法能够在合理的时间内获得满意的近优解.     

改进离散粒子群算法求解柔性流水车间调度问题

针对以最小化完工时间为目标的柔性流水车间调度问题(FFSP),提出了一种改进离散粒子群(DPSO)算法.所提算法重新定义粒子速度和位置的相关算子,并引入编码矩阵和解码矩阵来表示工件、机器以及调度之间的关系.为了提高柔性流水车间调度问题求解的改进离散粒子群算法的初始群体质量,通过分析初始机器选择与调度总完工时间的关系,首次提出一种基于NEH算法的最短用时分解策略算法.仿真实验结果表明,该算法在求解柔性流水车间调度问题上有很好的性能,是一种有效的调度算法.     

基于改进帝国竞争算法的柔性作业车间机器故障重调度

针对机器故障下的柔性作业车间重调度问题,提出了一种改进的帝国竞争算法（ICA）。首先,以最大完工时间、机器能耗和总延迟时间为目标函数建立柔性作业车间动态重调度模型,并对三个目标采用线性加权法;然后提出了改进的ICA来把优良的信息保留到下一代,即在传统ICA的同化和革命步骤后加入一个轮盘赌的选择机制,使初始帝国中的优秀基因得以保留,并且更新后的帝国质量更优,更加贴近最优解;最后,在机器发生故障后,采用事件驱动的重调度策略对故障点后未加工的工序进行重新调度。通过生产实例,对假设的三种机器故障情景进行仿真实验,并把所提算法与改进遗传算法（GA）和遗传算法与模拟退火混合算法（GASA）这两种算法进行比较。实验结果表明了提出的改进ICA是有效且可行的。     

改进GWO算法求解柔性钣金车间动态重调度问题

根据钣金生产线特点建立了具有工件优先级约束的多目标柔性作业车间动态调度模型,并提出改进的多目标灰狼优化算法用于求解该模型。首先,针对该模型设计出一种同时满足工件优先级约束、工序优先级约束和设备加工约束条件的剪枝式解码方案;其次,提出一种非线性收敛因子和动态位置更新策略,用于平衡经典灰狼优化算法的探索能力和利用能力;最后,为减少设备故障对原始调度方案的影响,设计了一种动态重调度策略。通过实验验证了改进多目标灰狼优化算法求解钣金车间动态调度问题的有效性和动态重调度策略的可行性。     

炼钢机器故障下炼钢-连铸调度DCSP建模与算法

针对机器故障下的炼钢-连铸动态调度问题,基于动态约束满足技术开发了能够灵活反映各种动态因素的建模机制。从变量、值域和约束三个角度将生产过程中的机器故障的影响映射为约束满足模型的动态变化;提出了重调度前后调度方案在时间安排和机器指派上的一致性度量方法,以满足不同炉次对时间和机器一致性的不同要求;将机器故障扰动按影响程度分为3个层级,建立了故障扰动与约束满足调度模型间的映射关系。根据炼钢、精炼阶段的机器故障扰动程度,制定不同的求解策略,并为机器指派变量赋值;基于约束传播技术,通过调整开工时间和柔性加工时间分步消解时间约束冲突。仿真实验表明,提出的模型和算法是可行和有效的。     

冶铸轧一体化生产下动态调度策略和方法研究

为了解决冶铸轧一体化生产过程中的动态调度问题,以保证冶铸轧一体化生产稳定顺行。首先,通过分析炼钢生产过程中的扰动,提出了基于扰动处理的冶铸轧一体化生产下动态调度策略。之后,将动态调度策略归结为:计划延迟和LF炉处理策略;钢种改判、钢水回炉以及产品问题处理策略;设备替换处理策略。基于三类处理策略分别介绍了启发式规则和机器冲突消解模型、计划优化模型和智能优化算法以及设备替换算法等处理方法。最后对实际生产中常见的"追加计划"进行了仿真,仿真系统分别对炼钢-连铸计划以及热轧和加热炉计划进行了动态调度,结果表明系统能够依据扰动,动态调整生产计划,保证生产稳定顺行。     

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。     

基于改进??学习的知识化制造自适应动态调度策略

针对知识化制造系统生产环境的不确定性, 构建一个基于多Agent 的知识化动态调度仿真系统. 为了保证设备Agent 能够根据当前的系统状态选择合适的中标作业, 提出一种基于聚类-动态搜索的改进??学习算法, 以指导不确定生产环境下动态调度策略的自适应选择, 并给出算法的复杂性分析. 所提出的动态调度策略采用顺序聚类以降低系统状态维数, 根据状态差异度和动态贪婪搜索策略进行学习. 通过仿真实验验证了所提出动态调度策略的适应性和有效性.

     

Multi-objective scheduling of dynamic job shop using variable neighborhood search

Dynamic job shop scheduling that considers random job arrivals and machine breakdowns is studied in this paper. Considering an event driven policy rescheduling, is triggered in response to dynamic events by variable neighborhood search (VNS). A trained artificial neural network (ANN) updates parameters of VNS at any rescheduling point. Also, a multi-objective performance measure is applied as objective function that consists of makespan and tardiness. The proposed method is compared with some common dispatching rules that have widely used in the literature for dynamic job shop scheduling problem. Results illustrate the high effectiveness and efficiency of the proposed method in a variety of shop floor conditions.     

An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time

In this article the scheduling problem of dynamic hybrid flow shop with uncertain processing time is investigated and an ant colony algorithm based rescheduling approach is proposed. In order to reduce the rescheduling frequency the concept of due date deviation is introduced, according to which a rolling horizon driven strategy is specially designed. Considering the importance of computational efficiency in the dynamic environment, the traditional ant colony optimization is improved. On the one hand, a strategy of available routes compression to restrict ants’ movement is proposed so that the ants’ searching cycle for new solutions could be shorten. On the other hand, illuminating function in state transfer possibility is improved to facilitate the exploration of low pheromone trail. Performance of rolling horizon procedure and rescheduling algorithm are evaluated respectively through simulations, the results show the best parameters of rolling horizon procedure and demonstrate the feasibility and efficiency of rescheduling algorithm. An example from the practical production is addressed to verify the effectiveness of the proposed approach.     

The role of algorithm and result comprehensibility of automated scheduling on complacency

Several studies have stressed that even expert operators who are aware of a machine's limits could adopt its proposals without questioning them (i.e., the complacency phenomenon). In production scheduling for manufacturing, this is a significant problem, as it is often suggested that the machine be allowed to build the production schedule, confining the human role to that of rescheduling. This article evaluates the characteristics of scheduling algorithms on human rescheduling performance, the quality of which was related to complacency. It is suggested that scheduling algorithms be characterized as having result comprehensibility (the result respects the scheduler's expectations in terms of the discourse rules of the information display) or algorithm comprehensibility (the complexity of the algorithm hides some important constraints). The findings stress, on the one hand, that result comprehensibility is necessary to achieve good production performance and to limit complacency. On the other hand, algorithm comprehensibility leads to poor performance due to the very high cost of understanding the algorithm. © 2008 Wiley Periodicals, Inc.     

Greedy randomized adaptive search for dynamic flexible job-shop scheduling

Dynamic flexible job shop scheduling problem is studied under the events such as new order arrivals, changes in due dates, machine breakdowns, order cancellations, and appearance of urgent orders. This paper presents a constructive algorithm which can solve FJSP and DFJSP with machine capacity constraints and sequence-dependent setup times, and employs greedy randomized adaptive search procedure (GRASP). Besides, Order Review Release (ORR) mechanism and order acceptance/rejection decisions are also incorporated into the proposed method in order to adjust capacity execution considering customer due date requirements. The lexicographic method is utilized to assess the objectives: schedule instability, makespan, mean tardiness and mean flow time. A group of experiments is also carried out in order to verify the suitability of the GRASP in solving the flexible job shop scheduling problem. Benchmark problems are formed for different problem scales with dynamic events. The event-driven rescheduling strategy is also compared with periodical rescheduling strategy. Results of the extensive computational experiment presents that proposed approach is very effective and can provide reasonable schedules under event-driven and periodic scheduling scenarios.     

Filtered-beam-search-based algorithm for dynamic rescheduling in FMS

In the practical production process of a flexible manufacturing system (FMS), unexpected disturbances such as rush orders arrival and machine breakdown may inevitably render the existing schedule infeasible. This makes dynamic rescheduling necessary to respond to the disturbances and to improve the efficiency of the disturbed FMS. Compared with the static scheduling, the dynamic rescheduling relies on more effective and robust search approaches for its critical requirement of real-time optimal response. In this paper, a filtered-beam-search (FBS) -based heuristic algorithm is proposed to solve the dynamic rescheduling problem in a large and complicated job shop FMS environment with realistic disturbances. To enhance its performance, the proposed algorithm makes improvement in the local/global evaluation functions and the generation procedure of branches. With respect to a due date-based objective (weighted quadratic tardiness), computational experiments are studied to evaluate the performance of the proposed algorithm in comparison with those of other popular methods. The results show that the proposed FBS-based algorithm performs very well for dynamic rescheduling in terms of computational efficiency and solution quality.     

Fuzzy neural network-based rescheduling decision mechanism for semiconductor manufacturing

Most semiconductor manufacturing systems (SMS) operate in a highly dynamic and unpredictable environment. The production rescheduling strategy addresses uncertainty and improves SMS performance. The rescheduling framework of SMS is presented as layered scheduling strategies with an optimization rescheduling decision mechanism. A fuzzy neural network (FNN) based rescheduling decision model is implemented which can rapidly choose an optimized rescheduling strategy to schedule the semiconductor wafer fabrication lines according to current system disturbances. The mapping between the input of FNN, such as disturbances, system state parameters, and the output of FNN, optimal rescheduling strategies, is constructed. An example of a semiconductor fabrication line in Shanghai is given. The experimental results demonstrate the effectiveness of proposed FNN-based rescheduling decision mechanism approach over the alternatives such as back-propagation neural network (BPNN) and multivariate regression (MR).