基于遗传算法的滚动调度策略*

本文研究了动态加工环境下的一类Ｊｏｂ－Ｓｈｏｐ调度问题，提出了一种基于遗传算法的滚动调度策略，其要点是：１）借鉴预测控制的思想，采用ｔｉｍｅ－ｂａｓｅｄ和ｊｏｂ－ｂａｓｅｄ的滚动调度策略适应动态环境和要求的多变性。２）以遗传算法和分派规则相结合，处理考虑与操作序列有关的工件安装时间和工件到期时间约束的复杂调度问题。文中给出了在工件到期时间发生改变的动态环境中两种滚动调度算法的调度结果，并与静态调度     

一种基于粗集的广义控制滚动调度策略

提出了一种基于粗集理论和广义控制思想的滚动调度建模策略。在调度工件加工过程中,考虑到有:工件加工完成;工件到期时间改变;急加工工件和新加工工件到达等突发事件发生时,对滚动调度窗口中的工件进行再选择,对窗口中工件实施再调度。仿真结果显示,利用该方法,不仅能适应动态加工环境的变化,而且降低了调度问题求解的规模。     

动态零等待流水线调度问题的滚动策略及优化算法

针对工件动态到达的零等待流水线调度问题,提出一种基于工件的滚动策略.证明了在该策略下全局调度性能随着局部调度的逐步滚动可得到不断改善.将该策略与基于差分进化的混合算法有机结合,能有效处理动态零等待流水线调度问题.最后通过实验验证了所提出策略和算法的有效性.     

基于工件窗口的发动机装配车间滚动调度

针对航空发动机装配车间因订单变更,机器故障等随机扰动造成静态调度结果失效问题,建立了重入式混合车间FIow－shop动态调度模型,综合考虑了机器,班组能力等多种资源约束;提出了一种基于工件窗口的滚动调度方法,从滚动窗口的确定,滚动机制,滚动窗口内部算法三个方面提高动态调度性能,实验表明该方法可行有效。     

动态零等待流水线调度问题的滚动策略及优化算法

针对工件动态到达的零等待流水线调度问题,提出一种基于工件的滚动策略.证明了在该策略下全局调度性能随着局部调度的逐步滚动可得到不断改善.将该策略与基于差分进化的混合算法有机结合,能有效处理动态零动态调度,滚动策略,差分进化,全局罚函数.

     

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.     

基于滚动变时间窗的重组批处理机调度研究

针对具有等待时间限制和工件动态到达的重组批处理机调度问题,以拖延时间和最小为目标,提出基于滚动变时间窗的三层混合调度算法。该调度算法是应用滚动时域策略,将重组批处理机调度问题分解为许多变时间窗的子问题;每个子问题调度分三层执行：即产生触发并传递参数、重组批及排序、派工并更新参数。通过实时调度仿真平台和CPLEX平台进行实例验证,结果表明基于滚动变时间窗的三层混合调度算法能够在较短计算时间内获得满意优化解。     

一种监控与调度集成的滚动式算法

给出一种通过滚动调度将动态调度与监控集成的方法，分析了在实际调度中改变调度策略的必要性和可行性，对滚动调度中的滚动窗口进行了描述。仿真实例表明该算法是有效的。     

基于两步滚动的单机调度算法研究

提出了一种两步滚动的调度算法，一般情况下，与在线算法DELAY-SPT比较，滚动调度算法是比较优的。同时，也把两步滚动方法与一步滚动方法进行了比较，得出在一般情况下，两步滚动策略是较优的。     

有优先顺序约束的单件车间调度问题的一种启发式算法

本文研究单件车间调度优化问题，针对工件之间存在的加工顺序关系，给出了逆推算法对各工件的交货期进行预处理，将问题转化为一般单件车间调度问题，在此基础上运用合适的调度规则，构造了具有一定优度的启发式算法。     

A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates

Flexible job shop scheduling problem (FJSSP) is generalization of job shop scheduling problem (JSSP), in which an operation may be processed on more than one machine each of which has the same function. Most previous researches on FJSSP assumed that all jobs to be processed are available at the beginning of scheduling horizon. The assumption, however, is always violated in practical industries because jobs usually arrive over time and can not be predicted before their arrivals. In the paper, dynamic flexible job shop scheduling problem (DFJSSP) with job release dates is studied. A heuristic is proposed to implement reactive scheduling for the dynamic scheduling problem. An approach based on gene expression programming (GEP) is also proposed which automatically constructs reactive scheduling policies for the dynamic scheduling. In order to evaluate the performance of the reactive scheduling policies constructed by the proposed GEP-based approach under a variety of processing conditions three factors, such as the shop utilization, due date tightness, problem flexibility, are considered in the simulation experiments. The scheduling performance measure considered in the simulation is the minimization of makespan, mean flowtime and mean tardiness, respectively. The results show that GEP-based approach can construct more efficient reactive scheduling policies for DFJSSP with job release dates under a big range of processing conditions and performance measures in the comparison with previous approaches.     

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.     

一种基于分解交货期的Job Shop启发式调度算法

针对以拖期加权和为目标的Job shop调度问题,提出一种基于分解交货期的启发式调度方法,首先根据工件的允许流比率确定每道工序的初始交货期,然后在活动调度框架下应用改进的MOD规则确定工件在机器上的加工顺序．在迭代优化过程中不断调整关键工序的交货期以改善调度的质量,并考虑了工件之间的相互影响．算例仿真研究表明,该算法可以在较短计算时间内得到较好解。可以满足实际Jobshop系统对调度质量和计算效率的要求。     

Scheduling with due date assignment under special conditions on job processing

We review the results on scheduling with due date assignment under such conditions on job processing as given precedence constraints, maintenance activity or various scenarios of processing time changing. The due date assignment and scheduling problems arise in production planning when the management is faced with setting realistic due dates for a number of jobs. Most research on scheduling with due date assignment is focused on optimal sequencing of independent jobs. However, it is often found in practice that some products are manufactured in a certain order implied, for example, by technological, marketing or assembly requirements and this can be modeled by imposing precedence constraints on the set of jobs. In classical deterministic scheduling models, the processing conditions, including job processing times, are usually viewed as given constants. In many real-life situations, however, the processing conditions may vary over time, thereby affecting actual durations of jobs. In the models with controllable processing times, the scheduler can speed up job execution times by allocating some additional resources to the jobs. In the models with deterioration or learning, the actual processing time can depend either on the position or on the start time of a job in the schedule. In scheduling with deterioration, the later a job starts, the longer it takes to process, while in scheduling with learning, the actual processing time of a job gets shorter, provided that the job is scheduled later. We consider also scheduling models with optional maintenance activity. In manufacturing processing, production scheduling with preventive maintenance planning is one of the most significant methods in preventing the machinery from failure or wear.     

模糊Job Shop调度交货满意度模型研究

针对生产环境中调度参数的不确定性,研究含模糊加工时间和模糊交货期的Job Shop调度问题,用6点模糊数表示加工时间梯形模糊数表示交货期。基于隶属度定义工件交货满意度,以最大化平均工件交货满意度作为优化目标建立模糊调度数学模型。基于模糊截集概念设计稳定性指标评价优化方案的稳定性,最后通过仿真结果证明所设计模型能够获得稳定性较好的满意调度方案。     

相关工件车间调度问题的拓扑算法

针对加工装配型离散制造企业实际生产的特点,提出了一类用于表示工序之间偏序关系的相关工件车间调度问题。为了利用已有的求解表示工序之间的线序关系的传统车间调度算法求解相关工件车间调度问题,设计了一种拓扑算法,该算法能够将工序之间的偏序关系转化为线序关系,将相关工件车间调度问题转化为传统的车间调度问题,通过实证研究,结果表明了拓扑算法是可行和高效的。     

A hybrid local search algorithm for scheduling real-world job shops with batch-wise pending due dates

This paper aims at solving a real-world job shop scheduling problem with two characteristics, i.e., the existence of pending due dates and job batches. Due date quotation is an important decision process for contemporary companies that adopt the MTO (make to order) strategy. Although the assignment of due dates is usually performed separately with production scheduling, there exist strong interactions between the two tasks. Therefore, we integrate these two decisions into one optimization model. Meanwhile, each order placed by the customer defines a batch of jobs, for which the same due date should be set. Thus, the completion times of these jobs should be close to one another in order to reduce waiting time and cost. For this purpose, we propose a dispatching rule to synchronize their manufacturing progresses. A two-stage local search algorithm based on the PMBGA (probabilistic model-building genetic algorithm) and parameter perturbation is proposed to solve the integrated scheduling problem and its superiority is revealed by the applications to a real-world mechanical factory.     

Digital twin-based job shop anomaly detection and dynamic scheduling

Scheduling scheme is one of the critical factors affecting the production efficiency. In the actual production, anomalies will lead to scheduling deviation and influence scheme execution, which makes the traditional job shop scheduling methods are not sufficient to meet the needs of real-time and accuracy. By introducing digital twin (DT), further convergence between physical and virtual space can be achieved, which enormously reinforces real-time performance of job shop scheduling. For flexible job shop, an anomaly detection and dynamic scheduling framework based on DT is proposed in this paper. Previously, a multi-level production process monitoring model is proposed to detect anomaly. Then, a real-time optimization strategy of scheduling scheme based on rolling window mechanism is explored to enforce dynamic scheduling optimization. Finally, the improved grey wolf optimization algorithm is introduced to solve the scheduling problem. Under this framework, it is possible to monitor the deviation between the actual processing state and the planned processing state in real time and effectively reduce the deviation. An equipment manufacturing job shop is taken as a case study to illustrate the effectiveness and advantages of the proposed framework.