基于柔性维护的堕化单机系统调度研究

针对单机系统,在假设生产系统为堕化系统,且生产过程中作业的加工不可中断的情况下,对考虑柔性时间窗口[[u,v]]下进行长度为[w]的周期预防性维护的调度问题进行了研究。建立了综合考虑生产调度和设备维护的混合整数规划模型,并设计了一套基于贪婪的启发式算法对所研究问题进行优化求解。通过Cplex和启发式算法求解结果的对比证明了算法可以快速、有效地解决此类问题。     

集成预防性维护和流水线调度的鲁棒性优化研究

针对离散流水车间, 设备故障率函数服从威布尔分布, 在考虑维护策略的基础上, 以工件的最终完工时间期望值为质量鲁棒性指标、以所有工序的开始加工时间的延迟总和的期望值为解鲁棒性指标, 建立了不确定性环境下预防性维护(Preventive maintenance, PM)和生产调度的集成优化模型, 联合决策各工序的开始加工时间和预防性维护位置. 进一步, 设计了基于工件优先列表、有效代理指标、邻域搜索机制的三阶段启发式算法对模型进行求解. 最后, 数值实验与传统方法对比结果表明, 系统最优缓冲时间随着解鲁棒性权重的增大而逐渐增加, 且质量鲁棒性堕化速度远小于解鲁棒性提升的速度, 使得其与传统方法相比总体目标愈加优异.     

基于粒子群算法的流程工业生产调度研究

以优化流程工业生产为目标,研究了将基于惯性权重的粒子群算法应用到流程工业的生产调度问题。在对流程工业生产调度问题进行分析的基础上,建立了以总加工完成时间最短为优化目标的生产调度模型。调度算法采用动态惯性权重,使惯性权值在粒子群算法搜索过程中线性变化,以提高粒子群算法的优化性能。给出了粒子编码与解码实现方法,以及具体的算法实现过程。以某流程工业企业生产调度实例为例,利用建立的优化调度模型和设计的粒子群算法进行了实验仿真,结果表明,建立的调度模型和设计的算法是可行的,与蚁群系统方法相比较,有较好的调度性能,适用于解决流程工业实际生产调度问题。     

区间数可重入混合流水车间调度与预维护协同优化

针对加工时间不确定的可重入混合流水车间调度与预维护协同优化问题,构建以区间最大完工时间、区间总碳排放和区间总预维护费用为优化目标的集成调度模型.针对问题特性,通过设计改进的可能度计算方法,定义区间意义下解的Pareto占优关系.提出一种改进的离散鲸鱼群算法,通过同步调度与维护策略,实现制造与维护的联合优化;设计个体间距离计算策略,寻找“最近较优个体”;设计个体位置移动策略以及多邻域搜索策略,有效地平衡全局搜索和局部搜索,提高收敛精度.通过大量的仿真实验和结果对比分析,表明了所提出的算法对于求解区间数可重入混合流水车间调度和预维护协同优化问题的有效性和可行性.     

考虑多目标的柔性工艺与调度集成优化算法

针对工艺规划与车间调度集成优化问题,在考虑零件的加工工序柔性、工序次序柔性及加工机器柔性的基础上,以最大完工时间、总加工成本和总拖期时间为优化目标,对多目标柔性工艺与车间调度集成问题建模,提出一种基于改进人工蜂群算法的多目标柔性工艺与车间调度集成优化策略,并提出邻域变异操作以及全局交叉操作,对种群进行更新。引入Pareto方法,通过对适应度评价、贪婪准则、Pareto最优解集构造和保存以及解得多样性维护等方面进行改进,设计了一种基于Pareto方法的多目标人工蜂群算法。最后,通过采用基本人工蜂群算法及改进人工蜂群算法对六个工件、五台机床的柔性工艺与车间调度集成问题进行优化,验证了改进算法的有效性。     

双种群混合遗传算法求解具有预防性维护的分布式柔性作业车间调度问题

在实际生产过程中,生产调度和设备维护相互影响,因此两者应该统筹优化.为研究具有预防性维护的分布式柔性作业车间调度问题,以最小化最大完工时间为目标,提出一种双种群混合遗传算法.结合问题特性,设计三维编码以及对应的机器解码方案,采用不同的策略初始化种群以均衡一部分工厂负载,为双种群设计不同的交叉变异算子提高算法的多样性,并利用交换精英解的方法实现两个种群的协作优化,同时针对关键工厂和预防性维护操作设计相应的局部搜索.最后对比现有算法,在同构和异构工厂的算例上进行实验,使用正交试验法优化算法参数设置.实验结果验证了局部搜索以及种群协作的有效性和双种群混合遗传算法求解具有预防性维护的分布式柔性作业车间调度问题的优越性.     

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。     

加工与装配车间集成调度的多目标优化模型

为解决一类具有多品种混流加工作业车间和流水装配车间的两阶段集成调度优化问题,以加工线最大完工时间和产品总生产完工时间最小为目标,并考虑通过对零部件加工提前完工和装配线等待施加惩罚系数,以保证缓冲区在制品库存和装配过程均匀连续生产,建立加工与装配车间集成调度的多目标优化模型,充分利用加工和装配工序之间存在的并行性,合理确定零部件加工顺序和装配排序,以缩短产品生产周期,降低生产成本,提高生产设备利用率;同时针对所建立的模型,设计遗传算法进行求解,采用零件加工和产品装配的两段实数编码,以稳态复制对群体进行选择,对交叉和变异算子进行设计,以保证新个体满足工序先后约束的可行性,避免了非可行解的混入影响优化结果;最后通过实例验证所建调度模型的可行性和算法的有效性。     

异构负载下单机调度与预测性维修的集成建模

在生产调度的过程中,设备常常因加工不同作业而承受不同负载即异构负载,设备受异构负载的影响导致其加工每项作业过程中的退化速率不同,从而影响生产调度与维修计划的排程,进而带来资源闲置和时间成本增加的问题.为了解决该问题,在考虑异构负载影响下,提出单机调度与预测性维修的联合策略,以最小总加权期望完成时间为目标构建相应的集成模型.对单机调度过程中受异构负载影响的设备,建立基于维纳过程的退化模型,根据其退化规律,推导相应设备剩余寿命的累积分布函数.通过数值实验,分别针对异构负载与平均负载的情况比较相应集成模型的优化结果,结果表明了在集成模型中考虑异构负载的必要性,并通过参数灵敏度分析验证了所建集成模型的有效性.     

基于Petri网的JSP动态优化调度

提出一种在柔性制造系统动态优化调度中处理紧急定单的方法。以带有控制器的 Petri 网为建模工具对柔性生产调度中的离散事件建模,对系统的设备维护、各种优先级等特性进行描述,利用遗传算法和模拟退火算法获得调度结果,用于解决作业车间的加工受到机床、操作工人等双资源制约条件下的动态优化调度。当有紧急定单需要加工时,该方法把剩余任务和紧急任务作为两个独立的任务分别处理,然后进行集成,在紧急任务为最优调度的基础上选取剩余任务的最优调度,找到兼顾整体和局部的最优解。仿真结果说明了算法的有效性和鲁棒性。     

An approximate nondominated sorting genetic algorithm to integrate optimization of production scheduling and accurate maintenance based on reliability intervals

With the development of intelligent manufacturing, production scheduling and preventive maintenance are widely applied in industry to enhance production efficiency and machine reliability. Therefore, according to the different processing states and the physical degradation phenomena of the machine, this paper proposes an accurate maintenance (AM) model based on reliability intervals, which have different maintenance activities in diverse intervals and overcome the shortcoming of the single reliability threshold maintenance model used in the past. Combining the flexible job-shop scheduling problem (FJSP), an integrated multiobjective optimization model is established with production scheduling and accurate maintenance. To strengthen the ability of the evolutionary algorithm to solve the presented model/problem, we propose a novel genetic algorithm, named the approximate nondominated sorting genetic algorithm III (ANSGA-III), which is inspired by NSGA-III. To improve the performance of the Pareto dominance principle, the local search, the elite storage for the original algorithm, the approximate dominance principle, the variable neighborhood search, and the elite preservation strategy are proposed. Then, we employ a scheduling example to verify and evaluate the availability of the above three improved operations and the proposed algorithm. Next, we compare ANSGA-III against five recently proposed algorithms, representing the state-of-the-art on similar problems. Finally, we apply ANSGA-III to solve the integrated optimization model, and the results reveal that the machine can maintain higher availability and reliability when compared to other models in our experiments. Consequently, the superiority of the proposed model based on accurate maintenance of reliability intervals is demonstrated, and the optimal reliability threshold between the yellow and red areas is found to be 0.82.     

Two-machine flow shop scheduling integrated with preventive maintenance planning

This paper investigates an integrated optimisation problem of production scheduling and preventive maintenance (PM) in a two-machine flow shop with time to failure of each machine subject to a Weibull probability distribution. The objective is to find the optimal job sequence and the optimal PM decisions before each job such that the expected makespan is minimised. To investigate the value of integrated scheduling solution, computational experiments on small-scale problems with different configurations are conducted with total enumeration method, and the results are compared with those of scheduling without maintenance but with machine degradation, and individual job scheduling combined with independent PM planning. Then, for large-scale problems, four genetic algorithm (GA) based heuristics are proposed. The numerical results with several large problem sizes and different configurations indicate the potential benefits of integrated scheduling solution and the results also show that proposed GA-based heuristics are efficient for the integrated problem.     

考虑劣化状态的单机调度与维修决策集成模型

大多数研究单机调度与维修决策集成问题的文献采用基于役龄的维修策略. 然而, 设备劣化状态与加工对象、加工环境和加工时间等诸多因素相关. 鉴于此, 针对设备状态可检测的系统, 采用非完美预防性视情维修、小修与故障更换相结合的维修策略, 建立一种以加工作业次序和预防维修阈值为决策变量, 加工作业的总加权期望完成时间最小为优化目标的随机期望值集成模型. 实验结果表明, 所提出的模型能更有效地避免过维修或欠维修, 并且能够降低生产持有成本.

     

Hybrid flexible flowshops with sequence-dependent setup times and machine availability constraints

One of the common assumptions in the field of scheduling is that machines are always available in the planning horizon. This may not be true in realistic problems since machines might be busy processing some jobs left from previous production horizon, breakdowns or preventive maintenance activities. Another common assumption is the consideration of setup times as a part of processing times, while in some industries, such as printed circuit board and automobile manufacturing, not only setups are an important factor but also setup magnitude of a job depends on its immediately preceding job on the same machine, known as sequence-dependent setup times. In this paper, we consider hybrid flexible flowshops with sequence-dependent setup times and machine availability constraints caused by preventive maintenance. The optimization criterion is the minimization of makespan. Since this problem is NP-hard in the strong sense, we propose three heuristics, based on SPT, LPT and Johnson rule and two metaheuristics based on genetic algorithm and simulated annealing. Computational experiments are performed to evaluate the efficiencies of the algorithms.     

维修时点预测的动态车间调度问题

针对制造业中生产计划的不确定问题,提出一种维修时点预测与自适应的遗传模拟退火算法相结合的优化调度方法。该方法首先利用差分自回归移动平均模型预测设备未来的故障率,然后借助电气设备的威布尔（Weibull）分布模型逆向求出设备未来故障发生时刻,最后将此作为约束条件,利用自适应的遗传模拟退火算法解决传统的生产调度问题。结合工厂实际情况,主要分析了设备有无维修的随机调度问题,以最小化最大完工时间为目标,获取每一个任务的调度计划以及每一台设备的维修时点,确定出最佳调度方案。实验表明自适应的遗传模拟退火算法的性能较好。在河北某工厂的生产车间中,设备在运行调度方法后三个月的平均故障率比运行前相对降低了3.46%。     

基于最小延时启发式搜索的TTA代码优化

针对传输触发架构下代码生成中指令调度的流水线冲突、调度死锁、资源冲突等问题,给出一种基于最小延时的遗传搜索算法模型,将软件旁路优化和资源动态分配优化整合到该模型中。实验结果表明,该算法能产生较高质量的并行代码,90%以上测试用例的指令级并行度高于表调度算法获得的结果。     

A methodology for joint optimization for maintenance planning, process quality and production scheduling

Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.     

A Case Study on Integrated Production Planning and Scheduling in a Three-Stage Manufacturing System

This paper presents an integrated optimization model of production planning and scheduling for a three-stage manufacturing system, which is composed of a forward chain of three kinds of workshops: a job shop, a parallel flow shop consisting of parallel production lines, and a single machine shop. As the products at the second stage are assembled from the parts produced in its upstream workshop, a complicated production process is involved. On the basis of the analysis of the batch production, a dynamic batch splitting and amalgamating algorithm is proposed. Then, a heuristic algorithm based on a genetic algorithm (known as the integrated optimization algorithm) is proposed for solving the problem. Note to Practitioners-This paper presents a method for integrated production planning and scheduling in a three-stage manufacturing system consisting of a forward chain of three kinds of workshops, which is common in such enterprises as producers of automobiles and household electric appliances, as in the case of an autobody plant usually with the stamping workshop, the welding and assembling workshop, and the painting workshop. Herein, the production planning and scheduling problems are simultaneously addressed in the way that a feasible production plan can be obtained and the inventory reduced. A batch splitting and amalgamating algorithm is proposed for balancing the production time of the production lines. And a case study of the integrated planning and scheduling problem in a real autobody plant verifies the effectiveness of our method