基于预防维护的单机调度问题

在两种维护约束下,研究完工时间之和最小化的单机调度问题.第1种维护约束是,固定周期预防维护;第2种维护约束是,机器工作期间可连续加工的最大工件个数受限.对于这种带有约束的调度问题,根据问题的规模,采用4种方法进行求解.针对小规模问题,建立一个二值整数规划模型,并根据最优解的特性制定剪枝规则,进而给出分支定界算法.针对中...     

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.     

基于灾变机制的预防性维护和生产调度集成优化方法

为了解决生产车间中多品种任务的生产调度与预防性维护集成优化问题,综合考虑其加工顺序、生产批量及预防性维护策略等要素,在订单充足的前提下,以总制造成本和加工时间最小化为联合优化目标,建立了生产调度与预防性维护集成优化模型。针对模型特点,在非支配排序遗传算法框架的基础上,基于灾变机制和荣誉空间,引入截断和拼接操作算子,提出一种变长度染色体单亲遗传算法对模型进行求解,并在不同参数条件和问题规模下,通过仿真实验验证了该算法解决复杂生产任务调度和预防性维护集成优化问题的有效性。     

基于时间戳的实化视图多版本维护

实化视图的一致性维护是数据仓库联机维护的关键问题.本文分析了基于三层数据仓库体系结构的3VWQ算法中潜在不一致性的原因,给出了三层结构的改进方法并引入时间戳作为中间版本实化视图的一致性维护标记;提出一种基于时间戳的补偿算法,实现了增量查询维护过程中不一致更新的本地补偿,从而在保证较好性能的同时解决了维护查询过程中的一致性问题.     

特征柔性模型的可视维护

本文简要介绍在ＶＰＳＦ系统中通过对特征图形的可视化交互操作来维护特征柔性模型。     

GA/LP算法求解带组换装时间的单机调度问题

以包头某钢铁线材企业实际调度问题为背景,针对带组换装时间的单机提前/拖期调度问题,建立了混合整数线性规划模型.根据模型的特点,将该问题的求解分为最优排序变量的确定和最优开工时间的确定两个层次.由于该问题是NP难的,提出了一种基于遗传算法和线性规划相结合的GA/LP算法.在算法中,利用GA在外层搜索遍历二元变量空间来优化工件排序;在内层则利用LP来确定满足约束条件的最优连续解.为了提高算法寻优性能,降低算法运行时间,提出了一种基于问题特征的种群初始化方法.最后,针对三种交叉算子在随机问题上进行了测试,实验结果表明,该算法能够有效求解问题.     

具有恶化效应和学习效应的单机成组调度问题

讨论了一类具有恶化效应和学习效应的单机成组调度问题, 其中工件的加工时间为开工时间和组内工序的函数. 通过对问题性质的分析以及多项式时间算法的描述, 得出如下结论: 在单机成组调度问题中, 即便工件的加工时间同时受恶化效应和学习效应的制约, 极小化完工时间问题以及极小化总资源消耗的问题仍是多项式时间可解的.     

单机UNIX核心的并行化技术

本文主要阐述UNIX源程序从单处理机到共享存储器多处理机系统的移植技术.主要从三个方面介绍:基本移植技术,提高效率技术以及增加新概念.我们在国外研究成果和经验的基础上提出我们对该问题的看法.     

神经网络在柔性生产线动态生产调度中的应用研究

把多条柔性生产线、多种产品的动态生产调度(MMDPS)问题归结为一混合非线性整数规则问题.基于神经网络优化计算原理,设计了求解MMDPS问题的连续时间动态神经网络模型,并给出了仿真结果.     

Integrated production and preventive maintenance scheduling for a single machine with failure uncertainty

This paper addresses the problem of finding a robust and stable schedule for a single machine with availability constraints. The machine suffers unexpected breakdowns and follows the Weibull failure function. A joint model for integrating run-based preventive maintenance (PM) into the production scheduling problem is proposed, in which the sequence of jobs, the PM times and the planned completion times of jobs are proactively determined simultaneously. Aiming at optimizing the bi-objective of system robustness and stability, a genetic algorithm based on the properties of the optimal schedule is proposed. The experimental results demonstrate that the proposed algorithm is efficient and effective under practical problem sizes. In addition, the impact of degree of uncertainty on the performance and the tradeoff between robustness and stability are explored in detail.     

Integrated scheduling and flexible maintenance in deteriorating multi-state single machine system using a reinforcement learning approach

This paper studies an integrated optimization problem of production scheduling and flexible preventive maintenance (PM) in a multi-state single machine system with deteriorating effects. A flexible PM strategy is proposed to proactively cope with machine failures while ensuring relatively regular PM intervals, which is composed of time-based PM (TBPM) and condition-based PM (CBPM). TBPM is conducted within every flexible time window and CBPM is implemented immediately after the most deteriorated yet still functional state. An illustrative case is presented using the enumeration approach to demonstrate the integration of production scheduling and machine maintenance. Then, Q-learning-based solution framework (QLSF) is further designed with proper state and action sets and reward functions to facilitate the determination of appropriate production scheduling rule under the constraint of the flexible maintenance. Numerical experiments show that the proposed QLSF outperforms the other four state-of-the-art scheduling rules in different scenarios. Moreover, the performance of the proposed flexible PM strategy is also examined and validated in comparison with three candidate maintenance strategies, i.e., run-to-failure corrective maintenance (CM), combination of TBPM and CM, and CBPM. The proposed flexible maintenance and solution approach can enrich the relevant academic knowledge base, and provide managerial insights and guidance in practical production systems.     

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.     

考虑柔性机器检修的钢管热轧批量调度方法

本文从无缝钢管生产管理中提取并定义了周期性机器柔性检修环境下的钢管热轧批量调度问题,针对无缝钢管热轧阶段的生产特点,将其抽象为一类考虑序列相关设置成本和机器柔性检修的单机调度问题,建立了以最小化机器闲置时间和机器调整时间为优化目标的数学模型。分析闲置时间和检修时点的关系,证明了闲置时间最小化性质,结合问题特征设计了两阶段启发式算法。算法第一阶段采用最小轧机调整时间规则获取具有最小机器调整时间的初始批量轧制序列,第二阶段对初始轧制序列进行全局寻优搜索。基于实际生产数据设计了多种问题规模的对比实验,实验结果表明模型和算法对求解该类问题具有较好效果。     

Multi-agent based approach for single machine scheduling with sequence-dependent setup times and machine maintenance

Scheduling of single machine in manufacturing systems is especially complex when the order arrivals are dynamic. The complexity of the problem increases by considering the sequence-dependent setup times and machine maintenance in dynamic manufacturing environment. Computational experiments in literature showed that even solving the static single machine scheduling problem without considering regular maintenance activities is NP-hard. Multi-agent systems, a branch of artificial intelligence provide a new alternative way for solving dynamic and complex problems. In this paper a collaborative multi-agent based optimization method is proposed for single machine scheduling problem with sequence-dependent setup times and maintenance constraints. The problem is solved under the condition of both regular and irregular maintenance activities. The solutions of multi-agent based approach are compared with some static single machine scheduling problem sets which are available in the literature. The method is also tested under real-time manufacturing environment where computational time plays a critical role during decision making process.     

Minimizing total completion time on a single machine with a flexible maintenance activity

A problem of jointly scheduling multiple jobs and a single maintenance activity on a single machine with the objective of minimizing total completion time is considered in this paper. It is assumed that the machine should be stopped for maintenance which takes a constant duration within a predefined period. The problem is generalized from the one with a fixed maintenance in that it relaxes the starting time of the maintenance from a fixed time point to a predefined period. Both resumable and nonresumable cases are studied. First, three properties of an optimal solution to each of the two cases are identified. Then it is shown that the proposed shortest processing time (SPT) algorithm is optimal for the resumable case. As for the nonresumable case, the conditions under which the SPT algorithm is optimal are also specified. Furthermore, it is shown that relaxing the starting time of the maintenance cannot improve the relative error bound of the SPT algorithm. The focus of the paper is presented afterwards, which is to develop a dynamic programming algorithm and a branch-and-bound algorithm to generate an optimal solution for this case. Experimental results show that these algorithms are effective and complementary in dealing with different instances of the problem.     

An economic production quantity model for deteriorating items with preventive maintenance policy and random machine breakdown

In recent years, many researches on economic production quantity (EPQ) models with machine breakdown and preventive maintenance have been developed, but few of them have developed integrated models for deteriorating items. In this study, we develop EPQ models for deteriorating items with preventive maintenance, random machine breakdown and immediate corrective action. Corrective and preventive maintenance times are assumed to be stochastic and the unfulfilled demands are lost sales. Two EPQ models of uniform distribution and exponential distribution of corrective and maintenance times are developed. An example and sensitivity analysis is given to illustrate the models. For the exponential distribution model, it is shown that the corrective time parameter is one of the most sensitive parameters to the optimal total cost.     

考虑设备随机故障的装备维修作业调度方法

装备维修过程中,维修设备随机故障这一不确定因素易对正常的维修计划造成影响。为了优化制定维修设备随机故障条件下的维修作业调度决策方案,依据维修设备的故障特点,提出一种基于组合调度策略的维修作业调度方法。调度方法兼顾完成时间与鲁棒性两方面目标,通过调度优化模型生成调度决策方案,并采用一种组合调度策略进行方案调整以改善决策方案的鲁棒性。通过随机故障事件模拟实验与分析,验证了调度方法能有效地减少设备随机故障造成的工序延误。     

一种面向柔性生产的动态调度模型*

为解决柔性生产中两个生产单元与多作业对象的优化调度问题,提出一种基于能力平衡的动态调度模型。将生产单元模型化为一种容器,把生产能力视为容积,并划分为设定能力和机动能力。通过建立两单元的机动能力连通模型,根据流体力学原理提出了一组动态调度规则,并开发了可用于现场调度的高效的启发式优化算法。在滚装运输企业进行的案例研究表明,该模型及其算法在实际生产运作中具有切实的可行性和有效性。