智能选择启发规则的FMS实时调度方法

介绍一种基于Ｐｅｔｒｉ网模型、面向系统特征智能选择启发式派遗规则的柔性制造系统动态调度方法。仿真结果表明，其调度性能优于使用单一发式派遗规则的方法，适合于解决柔性制造单元中随机、动态调度问题。     

一种柔性车间快速启发式调度算法

柔性作业车间调度问题比传统的Job-shop问题更复杂也更符合实际生产实际.为了快速有效地求解这类问题,设计出一种基于综合分派规则的快速启发式调度算法.基于综合分派规则的调度算法,以一批工件总完工时间最短为目标,在调度过程中通过动态调整工件的加工优先级并为每道工序分配最适合的机器进行加工,可迅速求得满意的较优解.与其他方法进行对比实验结果证实了算法的有效性,在实际调度系统的应用中也证明了算法的实用性.     

协作企业制造过程多Agent调度建模技术

构建了面向订单的基于WWW协作企业制造过程模型。通过车间调度的多Agent协作流程,建立车间调度多Agent实体的UML描述方式。采用合同网协议,把交货时间、生产成本和设备利用率等作为多Agent投标时的性能指标,形成分布式产品调度动态处理模型。最后通过一个实例来验证多Agent调度建模技术的可行性。     

基于Agent的分布式动态作业车间调度

Agent技术是分布式工业系统建模的一种重要方法．本文对Agent及多Agent技术进 行了简要总结,综述了Agent技术在制造作业车间调度中的应用研究概况,提出了一种基于 合同网协议投标机制的多Agent分布式动态作业车间调度方案．     

多Agent系统中基于改进合同网模型的任务分配研究

任务分配是多Agent系统中研究的热.奴,合同网模型是关于多Agent系统中任务分配的经典策略,但传统的合同网模型存在很多不足。在引入基于信任度的招标策略和基于自适应的投标策略的基础上,主要针对传统合同网模型中标阶段存在的问题,从投标Agent的负载、能力和信任度3个方面进行综合考虑和权衡,提出了一种基于多属性评价中标策略的动态任务分配算法,从而有效地提高了任务分配和执行的效率。最后通过仿真实验验证了基于多属性评价中标策略的正确性和合理性。     

基于Petri网与遗传算法的可重入生产系统调度

可重入生产系统调度问题属于NP难题,该文建立了系统的扩展Petri网模型,并且将遗传算法方法与调度规则结合起来用于解决可重入生产系统的调度问题。针对可重入生产系统生产过程的动态复杂性,首先建立了一类综合调度规则,然后提出了基于设备分组与分时段的综合规则组合的可重入生产系统调度策略,并采用遗传算法与基于Petri网模型的过程仿真相结合的方法对综合规则组合进行优化,仿真比较验证了该调度策略的有效性。     

扩展合同网方法在远程协同诊断中的应用

诊断任务分配是远程协同诊断的一个内容和难点;在经典合同网分配方法基础上,提出的扩展合同网分配方法依据诊断任务的相似范例推理和阈值规则确定投标诊断资源联盟,以满意效用并经过协商来签订和执行合同.该效用由多约束模糊系数法确定;给出了扩展合同网分配方法的算法描述和UML执行流程,通过一个FMS诊断任务分配过程验证了该方法是一个动态、有效的智能诊断任务分配方法;在结论中指出了进一步研究点。     

指挥调度系统在急诊科室的应用及其仿真分析

提出了一种基于指挥调度系统的医疗资源调度模型.调度模型综合考虑了内部和外部影响因素,构建了资源需求的优先顺序,并采用Petri网对对系统进行模块化建模,得出了一些符合要求的调度方案,根据采用指挥调度系统选择最优调度方案.仿真结果表明,提出的方法实现资源的动态管理,满足医院随时进行资源调度应急指挥的需求.     

基于多智能体的动态车间调度系统

在分析车间生产调度特点的基础上,提出了基于多智能体的动态车间生产调度模型。把车间生产调度系统分为调度代理、任务代理和资源代理等。代理之间采用了基于改进的合同网的关系网模型,为解决车间加工动态调度问题提供了一种新的方法。     

基于Petri网模型的柔性加工系统能耗动态优化调度方法

柔性加工系统加工过程中存在突发的动态事件,严重干扰已有调度计划的执行,难以维持较优的能耗指标。针对此问题,在建立柔性加工系统Petri网(flexible machining system etri net,FMSPN)模型的基础上,考虑新任务插单和机器故障与修复两类事件,提出一种面向能耗目标的动态优化调度方法。在动态事件发生时刻,重新建立FMSPN模型,同时融合系统内各设备不同状态下的能量消耗规律,得到扰动发生时刻至加工完成时刻的能耗目标模型。基于动态规划方法对该能耗模型进行重新优化,求解扰动发生时刻后的系统生产调度计划。最后实例仿真验证了FMSPN模型在优化调度流程中的可靠性,以及此方法在动态扰动下的可行性。     

A Reinforcement Learning-based Approach to Dynamic Job-shop Scheduling

Production scheduling is critical to manufacturing system.Dispatching rules are usually applied dynamically to schedule (?)he job in a dynamic job-shop.Existing scheduling approaches sel- dom address machine selection in the scheduling process.Composite rules,considering both machine selection and job selection,are proposed in this paper.The dynamic system is trained to enhance its learning and adaptive capability by a reinforcement learning(RL)algorithm.We define the concep- tion of pressure to describe the system feature.Designing a reward function should be guided by the scheduling goal to accurately record the learning progress.Competitive results with the RL-based approach show that it can be used as real-time scheduling technology.     

一种基于强化学习的作业车间动态调度方法

Production scheduling is critical to manufacturing system. Dispatching rules are usually applied dynamically to schedule the job in a dynamic job-shop. Existing scheduling approaches sel- dom address machine selection in the scheduling process. Composite rules, considering both machine selection and job selection, are proposed in this paper. The dynamic system is trained to enhance its learning and adaptive capability by a reinforcement learning (RL) algorithm. We define the conception of pressure to describe the system feature. Designing a reward function should be guided by the scheduling goal to accurately record the learning progress. Competitive results with the RL-based approach show that it can be used as real-time scheduling technology.     

Study on shop floor control system in semiconductor fabrication by self-organizing map-based intelligent multi-controller

To confirm semiconductor wafer fabrication (FAB) operating characteristics, the scheduling decisions of shop floor control systems (SFCS) must develop a multiple scheduling rules (MSRs) approach in FABs. However, if a classical machine learning approach is used, an SFCS in FABs knowledge base (KB) can be developed by using the appropriate MSR strategy (this method is called an intelligent multi-controller in this study) as obtained from training examples. A classical machine learning approach main disadvantage is that the classes (scheduling decision variables) to which training examples are assigned must be pre-defined. This process becomes an intolerably time-consuming task. In addition, although the best decision rule can be determined for each scheduling decision variable, the combination of all the decision rules may not simultaneously satisfy the global objective function. To address these issues, this study proposes an intelligent multi-controller that incorporates three main mechanisms: (1) a simulation-based training example generation mechanism, (2) a data preprocessing mechanism, and (3) a self-organizing map (SOM)-based MSRs selection mechanism. These mechanisms can overcome the long training time problem of the classical machine learning approach in the training examples generation phase. Under various production performance criteria over a long period, the proposed intelligent multi-controller approach yields better system performance than fixed decision scheduling rules for each of the decision variables at the start of each production interval.     

Development of two-level decision tree-based real-time scheduling system under product mix variety environment

Most of the research on machine learning-based real-time scheduling (RTS) systems has been aimed toward product constant mix environments. However, in a product mix variety manufacturing environment, the scheduling knowledge base (KB) is dynamic; therefore, it would be interesting to develop a procedure that would automatically modify the scheduling knowledge when important changes occur in the manufacturing system. All of the machine learning-based RTS systems (including a KB refinement mechanism) proposed in earlier studies periodically require the addition of new training samples and regeneration of new KBs. Hence, previous approaches investigating machine learning-based RTS systems have been confronted with the training data overflow problem and an increase in the scheduling KB building time, which are unsuitable for RTS control. The objective of this paper is to develop a KB class selection mechanism that can be supported in various product mix ratio environments. Hence, the RTS KB is developed by a two-level decision tree (DT) learning approach. First, a suitable scheduling KB class is selected. Then, for each KB class, the best (proper) dispatching rule is selected for the next scheduling period. Here, the proposed two-level DT RTS system comprises five key components: (1) training samples generation mechanism, (2) GA/DT-based feature selection mechanism, (3) building a KB class label by a two-level self-organizing map, (4) DT-based KB class selection module, and (5) DT-based dynamic dispatching rule selection module. The proposed two-level DT-based KB RTS system yields better system performance than that by a one-level DT-based RTS system and heuristic individual dispatching rules in a flexible manufacturing system under various performance criteria over a long period.     

Reactive scheduling in a job shop where jobs arrive over time

The paper considers the dynamic job shop scheduling problem (DJSSP) with job release dates which arises widely in practical production systems. The principle characteristic of DJSSP considered in the paper is that the jobs arrive continuously in time and the attributes of the jobs, such as the release dates, routings and processing times are not known in advance, whereas in the classical job shop scheduling problem (CJSSP), it is assumed that all jobs to be processed are available at the beginning of the scheduling process. Reactive scheduling approach is one of the effective approaches for DJSSP. In the paper, a heuristic is proposed to implement the reactive scheduling of the jobs in the dynamic production environment. The proposed heuristic decomposes the original scheduling problem into a number of sub problems. Each sub problem, in fact, is a dynamic single machine scheduling problem with job release dates. The scheduling technique applied in theproposed heuristic is priority scheduling, which determines the next state of the system based on priority values of certain system elements. The system elements are prioritized with the help of scheduling rules (SRs). An approach based on gene expression programming (GEP) is also proposed in the paper to construct efficient SRs for DJSSP. The rules constructed by GEP are evaluated in the comparison of the rules constructed by GP and several prominent human made rules selected from literatures on extensive problem sets with respect to various measures of performance.     

A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches.     

A three-fold approach for job shop problems: A divide-and-integrate strategy with immune algorithm

This paper presents a novel divide-and-integrate strategy based approach for solving large scale job-shop scheduling problems. The proposed approach works in three phases. First, in contrast to traditional job-shop scheduling approaches where optimization algorithms are used directly regardless of problem size, priority rules are deployed to decrease problem scale. These priority rules are developed with slack due dates and mean processing time of jobs. Thereafter, immune algorithm is applied to solve each small individual scheduling module. In last phase, integration scheme is employed to amalgamate the small modules to get gross schedule with minimum makespan. This integration is carried out in dynamic fashion by continuously checking the preceding module's machine ideal time and feasible slots (satisfying all the constraint). In this way, the proposed approach will increase the machine utilization and decrease the makespan of gross schedule. Efficacy of the proposed approach has been tested with extremely hard standard test instances of job-shop scheduling problems. Implementation results clearly show effectiveness of the proposed approach.     

动态车间作业调度问题中调度规则算法研究综述

调度规则是解决实际生产中的动态车间作业调度问题的有效方法,但它一般只在特定调度环境下性能较好,当环境发生变化时,就需要进行实时选择和评价。对调度规则的实时选择和评价方法进行综述,以研究实际生产中动态车间的实时调度问题。对调度规则的发展、分类以及特点进行了概述,并对调度规则的选择和评价方法进行了总结。详细介绍了调度规则的选择方法,包括使用较多的稳态仿真方法和表现较好的人工智能方法,并给出了仿真方法、专家系统、机器学习方法以及人工神经网络方法,用于调度规则的选择时所取得的研究成果和结论。此外,还介绍了调度规则的评价指标及评价方法。最后针对调度规则存在的不足,指出了未来的研究方向。     

Using multi-agent architecture in FMS for dynamic scheduling

The proposed scheduling strategy is based on a multi-agent architecture. Each agent of this architecture is dedicated to a work centre (i.e. a set of resources of the manufacturing system); it selects locally and dynamically the most suitable dispatching rules. Depending on local and global considerations, a new selection is carried out each time a predefined event occurs (for example, a machine becomes available, or a machine breaks down). The selection depends on: (1) primary and secondary performance objectives, (2) the operating conditions, and (3) an analysis of the system state, which aims to detect particular symptoms from the values of certain system variables. We explain how the scheduling strategy is shared out between agents, how each agent performs a local dynamic scheduling by selecting an adequate dispatching rule, and how agents can coordinate their actions to perform a global dynamic scheduling of the manufacturing system. Each agent can be implemented through object-oriented formalisms. The selection method is improved through the optimization of the numerical thresholds used in the detection of symptoms. This approach is compared with the use of SPT, SIX, MOD, CEXSPT and CR/SPT on a jobshop problem, already used in other research works. The results indicate significant improvements.     

Learning-based multi-pass adaptive scheduling for a dynamic manufacturing cell environment

Because the essential attributes are uncertain in a dynamic manufacturing cell environment, to select a near-optimal subset of manufacturing attributes to enhance the generalization ability of knowledge bases remains a critical, unresolved issue for classical artificial neural network-based (ANN-based) multi-pass adaptive scheduling (MPAS). To resolve this problem, this study develops a hybrid genetic /artificial neural network (GA/ANN) approach for ANN-based MPAS systems. The hybrid GA/ANN approach is used to evolve an optimal subset of system attributes from a large set of candidate manufacturing system attributes and, simultaneously, to determine configuration and learning parameters of the ANN according to various performance measures. In the GA/ANN-based MPAS approach, for a given feature subset and the corresponding topology and learning parameters of an ANN decoded by a GA, an ANN was applied to evaluate the fitness in the GA process and to generate the MPAS knowledge base used for adaptive scheduling control mechanisms. The results demonstrate that the proposed GA/ANN-based MPAS approach has, according to various performance criteria, a better system performance over a long period of time than those obtained with classical machine learning-based MPAS approaches and the heuristic individual dispatching rules.