基于Petri网的JSP动态分类调度

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,通过构建Petri 网控制器使系统的运行满足期望的目标,同时利用混合遗传算法获得调度结果,用于解决作业车间的加工受到机床、操作工人等资源制约条件下的动态优化调度。为了保证生产的平稳性,最大限度地维持车间的生产能力,提出了针对不同的扰动进行分类处理的新方法,首先基于机床故障修复时间、工人离岗时间及取消订单包含任务的多少进行分类调度,然后根据机床故障修复后以及工人回岗后剩余任务的多少决定是否进行再一次的调度,最后对算法进行了仿真研究。     

求解柔性作业车间调度的GATOC混合方法

柔性作业车间调度问题是经典作业车间调度问题的扩展,它允许工序在可选加工机器集中任意一台上加工,加工时间随加工机器不同而不同。针对柔性作业车间调度问题的特点,提出一种基于约束理论的局部搜索方法,对关键路径上的机器的负荷率进行比较,寻找瓶颈机器,以保证各机器之间的负荷平衡。为了克服传统遗传算法早熟和收敛慢的缺点,设计多种变异操作,增加种群多样性。为了更好保留每代中的优良解,设计了基于海明距离的精英解保留策略。运用提出的算法求解基准测试问题,验证了算法的可行性和有效性。     

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.     

瓶颈指向的启发式算法求解混合流水车间调度问题

针对以最小化时间表长为目标的复杂混合流水车间调度问题,提出了一种将机器布局和工件加工时间特征紧密结合的启发式算法.首先,充分利用各阶段平均机器负荷一般不相等的特点确定瓶颈阶段,构建初始工件排序.其次,针对在瓶颈阶段前加工时间较短而瓶颈阶段后加工时间相对较长的工件,在第1阶段优先开始加工.同时,在瓶颈阶段前的每一个阶段,每当有工件等待加工或同时完工时,优先选择瓶颈阶段前剩余加工时间最短的工件加工;在瓶颈阶段以及瓶颈阶段之后,则优先选择这台机器后剩余加工时间最长的工件加工.最后,采用工件交换和插入操作改进初始调度.用Carlier和Neron的Benchmark算例测试提出的启发式算法.将计算结果与NEH启发式算法进行了比较,平均偏差降低了0.0555%,表明这个启发式算法是有效的.     

周期性和事件驱动的JobShop滚动调度策略

为了适应加工的连续性及环境的变化，借用了预测控制中的滚动优化思想提出了周期性和事件驱动的滚动调度策略。调度算法将遗传算法和分派规则相结合，以此来处理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。     

A distributed shifting bottleneck heuristic for complex job shops

In this paper, we consider distributed versions of a modified shifting bottleneck heuristic for complex job shops. The considered job shop environment contains parallel batching machines, machines with sequence-dependent setup times and reentrant process flows. Semiconductor wafer fabrication facilities are typical examples for manufacturing systems with these characteristics. The used performance measure is total weighted tardiness (TWT). We suggest a two-layer hierarchical approach in order to decompose the overall scheduling problem. The upper (or top) layer works on an aggregated model. Based on appropriately aggregated routes it determines start dates and planned due dates for the jobs within each single work area, where a work area is defined as a set of parallel machine groups. The lower (or base) layer uses the start dates and planned due dates in order to apply shifting bottleneck heuristic type solution approaches for the jobs in each single work area. We conduct simulation experiments in a dynamic job shop environment in order to assess the performance of the heuristic. It turns out that the suggested approach outperforms a pure First In First Out (FIFO) dispatching scheme and provides a similar solution quality as the original modified shifting bottleneck heuristic.     

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。     

Holonic job shop scheduling using a multiagent system

Manufacturing job shop scheduling is a notoriously difficult problem that lends itself to various approaches - from optimal algorithms to suboptimal heuristics. We combined popular heuristic job shop-scheduling approaches with emerging AI techniques to create a dynamic and responsive scheduler. We fashioned our job shop scheduler's architecture around recent holonic manufacturing systems architectures and implemented our system using multiagent systems. Our scheduling approach is based on evolutionary algorithms but differs from common approaches by evolving the scheduler rather than the schedule. A holonic, multiagent systems approach to manufacturing job shop scheduling evolves the schedule creation rules rather than the schedule itself. The authors test their approach using a benchmark agent-based scheduling problem and compare performance results with other heuristic-scheduling approaches.     

An integrated scheduler for manufacturing planning and control (INSCH)

The most basic problem in a manufacturing process is to create valid scheduling system which determines the sequence of jobs to be processed at each of the series of machine centers. An integrated scheduler (INSCH) is developed for small job shop manufacturing systems while considering high machine utilization, low work-in-process, and reduced job lateness.

As an and to understand the interaction of live jobs with the shop, sequence scheduler is developed to complement production scheduler such as Gantt bar chart. INSCH can achieve better performance than simple static models as shown in an example. It is desirable for INSCH to be applied to small job shop manufacturing companies using micro personal computer with relevant modifications discussed.     

基于人工蜂群算法的柔性工艺与车间调度集成优化

为实现柔性工艺与车间调度集成优化,在考虑工件特征的加工工艺、次序及加工机器的柔性基础上,以最小化最大完工时间为优化目标,提出一种基于交叉变异的人工蜂群算法。该算法针对柔性工艺与车间调度集成问题的离散性特征,对工艺路线进行序列编码,工件调度采用基于工序的编码方式。通过工艺种群与调度种群的交叉变异操作,分别使采蜜蜂及观察蜂进行局部寻优,侦查蜂进行全局寻优,以此提高算法性能。在此基础上用两部分测试实例分别验证了集成研究的必要性及改进算法的有效性。     

Parallel-identical-machine job-shop scheduling with different stage-dependent buffering requirements

The neglect of buffering requirements in a classical job shop scheduling system often results in inapplicability in many complex real-world applications. To overcome this inapplicability, a new and more generalised scheduling problem is proposed under different stage-dependent buffering requirements and parallel use of identical-function machine units at each processing stage in job shop environments. The problem is formulated as a mixed integer programming model that can be exactly solved by ILOG-CPEX for small-size instances. Moreover, a hybrid metaheuristic algorithm embedded with a state-of-the-art constructive algorithm is developed. The computational experiment shows that the proposed metaheuristic can efficiently solve large-size instances. The result analysis indicates that the proposed approach can provide better configuration of real-world scheduling systems. The proposed DBPMJSS methodology has a potential to analyse, model and solve many industrial systems with the requirements of buffering conditions, particularly for manufacturing, railway, healthcare and mining industries.     

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。     

具有空闲时间的云制造作业车间调度方法

针对云制造企业的剩余能力利用问题,研究具有空闲时间的车间调度方法.构建云制造作业车间的调度框架,以最小化工期为目标,考虑设备空闲时间,提出工序加工时间序列的确定方法和空闲时间段的更新策略,采用基于工件的精简编码和改进的二阶粒子群算法求解工件最优调度顺序.仿真结果验证了所提出调度方法的可行性.与已有算法的对比分析表明,改进算法具有良好的搜索性能.     

A two-stage hybrid memetic algorithm for multiobjective job shop scheduling

In this paper we address multiobjective job shop scheduling problems. After several decades of research in scheduling problems, a variety of heuristics have been developed. The proposed algorithm is a hybrid of three frequently applied ones: the dispatching rule, the shifting bottleneck procedure, and the evolutionary algorithm. It is a two-stage algorithm, which integrates a rule-based memetic algorithm in the first stage and a re-optimization procedure of shifting bottleneck in the second. We conduct experiments using benchmark instances found in the literature to assess the performance of the proposed method. The experimental results show that the proposed method is effective and efficient for multiobjective scheduling problems.     

基于多Agent协商的柔性车间调度系统

针对柔性作业车间调度的特点,提出了一种基于多agent协商的柔性作业车间调度系统。系统由工件agent,机器agent和工序agent组成。Agent之间通过相互发送消息和响应消息进行交互,并且通过消息相应函数按照各agent局部的信息、同时兼顾系统的性能进行决策。工件agent通过招标的方式,选择合适的机器完成加工任务,机器agent通过竞争来获得工件的加工权。最后用Java语言在Eclipse平台上进行程序设计,对柔性作业车间调度的平均滞后问题进行仿真实验,并与传统的分派规则比较,结果显示所提方法的优越性。     

Improving the performance of cellular manufacturing by a dynamic part assignment approach

In this paper, a simple dynamic part assignment (DPA) procedure is introduced and its effects on the performance of cellular manufacturing systems is evaluated. In this procedure rerouting of parts among machine cells is allowed for better machine utilization. Three data sets are used to compare the performance of manufacturing systems under job shop and cellular manufacturing when DPA is employed. The results of simulation runs indicate that the proposed DPA method improves the performance of cellular manufacturing significantly by reducing the congestion in machine cells with long waiting lines.     

考虑机器速度的低碳柔性作业车间调度问题研究

根据具有低碳需求的制造企业的实际情况,建立了考虑机器速度的低碳柔性作业车间调度问题模型,该模型考虑机器加工速度,增加了工件的装夹和卸载时间及机器在不同状态下的碳排放参数,使问题更具现实性。为了实现低碳生产的目标,本文在实现最大完工时间最小的前提下提出了一种非关键工序调整法,通过对非关键工序的调整,降低机器的总碳排放量,提高机器利用率。最后通过求解实际案例,实验结果证实模型的可行性和非关键工序调整法的有效性。     

基于正态云模型的状态转移算法求解多目标柔性作业车间调度问题

为了求解具有多目标多约束的柔性作业车间调度问题,提出一种基于正态云模型的状态转移算法.构建以最小化最大完工时间、机器总负荷及瓶颈机器负荷为目标的多目标柔性作业车间调度问题的数学模型;针对灰熵关联度适应度分配策略在Pareto解比较序列与参考序列之间的差值相等时不能引导算法进化的情况,提出一种改进灰熵关联度的适应度值分配策略;同时引入兼具模糊性和随机性的云模型进化策略以改进状态转移算法,可有效避免算法早熟并增加候选解的多样性.仿真结果表明:基于正态云模型的状态转移算法能够有效解决多目标柔性作业车间调度问题;与其他算法相比,所提出算法求解问题的收敛精度更高、收敛速度更快.     

A genetic algorithm with sub-indexed partitioning genes and its application to production scheduling of parallel machines

Production scheduling seeks optimal combination of short manufacturing time, stable inventory, balanced human and machine utilization rate, and short average customer waiting time. Since the problem in general has been proven as NP-hard, we focus on suboptimal scheduling solutions for parallel flow shop machines where jobs are queued in a bottleneck stage. A Genetic Algorithm with Sub-indexed Partitioning genes (GASP) is proposed to allow more flexible job assignments to machines. Our fitness function considers tardiness, earliness, and utilization rate related variable costs to reflect real requirements. A premature convergence bounce is added to traditional genetic algorithms to increase permutation diversity. Finally, a production scheduling system for an electronic plant based on GASP is implemented and illustrated through real production data. The proposed GASP has demonstrated the following advantages: (1) the solutions from GASP are better and with smaller deviations than those from heuristic rules and genetic algorithms with identical partitioning genes; (2) the added premature convergence bounce helps obtain better solutions with smaller deviations; and (3) the consideration of variable costs in the fitness function helps achieve better performance indicators.     

Integration of deep reinforcement learning and multi-agent system for dynamic scheduling of re-entrant hybrid flow shop considering worker fatigue and skill levels

In real-life manufacturing systems, production management is often affected by urgent demands and unexpected interruptions, such as new job insertions, machine breakdowns and operator unavailability. In this context, agent-based techniques are useful and able to respond quickly to dynamic disturbances. The ability of agents to recognize their environment and make decisions can be further enhanced by deep reinforcement learning (DRL). This paper investigates a novel dynamic re-entrant hybrid flow shop scheduling problem (DRHFSP) considering worker fatigue and skill levels to minimize the total tardiness of all production tasks. An integrated architecture of DRL and MAS (DRL-MAS) is proposed for real-time scheduling in dynamic environments. Two DRL models are proposed for different sub-decisions, where a reward-shaping technique combining long-term and short-term returns is proposed for the job sequence and machine selection sub-decisions, and an attention-based network is proposed for the worker assignment sub-decision for efficient feature extraction and decision making. Numerical experiments and case studies demonstrate the superior performance of the proposed DRL models compared with existing scheduling strategies.