基于生物遗传算法的FMS生产调度算法

根据遗传算法,提出了一种FMS生产调度的新算法,该算法不仅适于FMS的静态调度问题,而且由于其计算复杂性低、计算量少的特点,同样也适于FMS的动态调度问题,有效地为解决自动化生产系统的生产调度问题提供了新方法.     

板材FMS计划调度方法的应用研究

针对板材FMS的特点,提出了一体化的计划调度方法,并开发出相应的控制与管理软件系统,应用于国产智能型板材FMS,系统的实际运行表明该计划调度方法具有较高的实用价值。     

板格FMS计划调度方法的应用研究

针对板材FMS的特点，提出了一体化的计划调度方法，并开发出相应的控制与管理软件系统，应用于国产智能型板板FMS，系统的实际运行表明该计划高度方法具有较高的实用价值。     

FMS刀具管理优化技术研究与实验系统开发

设计开发了基于嵌入式控制技术的FMS刀具控制管理实验系统,其结构紧凑、功能完善、配置灵活,能够模拟FMS实际生产加工中刀具控制过程,既可应用于相关理论与技术的实验验证,也可应用于实验教学和培训。并运用遗传算法在有限刀具资源约束下对FMS刀具调度优化问题进行了研究,采用静态和动态两级调度优化策略,对减少换刀次数,提高FMS生产效率具有积极的作用。     

基于EHLGEP-N的柔性制造系统的建模

柔性制造系统（FMS）是复杂离散事件动态系统.利用EHLGEP-N模型,对FMS建模,引入了全局库所G,使模型有了智能优化的思想,既提高了FMS优化调度的整体性能,又使调度具有较强的可观性.     

小型FMS实时动态调度专家系统的研究和开发

专家系统是人工智能领域的工具,它和FMS实时调度系统的结合有利于解决FMS实时调度中复杂的决策问题。 本文主要介绍一种基于规则的小型FMS实的动态调度专家系统,该系统中的知识描述采用产生式规则,推理机采用控制策略推理、原推理及目标推理三级正向推理方式、初步实践表明,该系统的原理和结构是合理可行的。     

基于Petri网的柔性制造系统建模的调度算法

针对柔性制造系统(FMS)建模中存在的问题,通过分析FMS的组成和特点,提出了基于赋时对象Petri网的建模方法.该方法集中了Petri网和面向对象技术的优点,利用赋时对象Petri网,建立了FMS的资源对象模型和动态行为模型.同时分析了传统调度算法的优缺点,在传统调度算法的基础上提出了时间-A搜索算法,该算法能有效地解决了FMS仿真时的调度问题,使得加工路径代价较低,同时避免了A搜索算法搜索节点多、搜索效率低的弊端.     

FMS调度与控制的统一建模与集成设计

分析了FMS调度与控制统一建模与集成设计的必要性，从FMS运行过程的IDEF0功能建模入手，建立了基于模块化彩色赋时Petri网（CTPN）的FMS调度与控制的统一模型。该模型不仅可描述FMS的调度问题，而且能表达FMS的运行控制逻辑与仿真，从而为FMS控制系统的集成设计奠定了基础。为简化FMS控制系统的设计，提出了虚拟工作站的概念与方法，将FMS控制器的部分控制功能下放到低层的工作站控制层。基于上述CTPN统一模型，开发了FMS调度与控制的集成设计原理和方法。     

一种基于改进遗传算法的多目标动态调度优化

静态调度问题一般是NP-hard问题,而动态调度问题的性能指标比静态调度的更为复杂,并且以多目标综合性能指标居多。在系统地研究了多目标动态作业车间调度问题的基础上,运用滚动窗口技术和改进的遗传算法来解决动态调度问题。滚动窗口技术将调度过程分成连续静态调度区间,在每个区间内用多目标遗传算法进行调度优化。最后开发了面向应用的动态调度原型系统,并对改进的Job-shop基准实例进行仿真试验,证明了提出策略的可行性和有效性。     

一种基于改进遗传算法的多目标动态调度优化

静态调度问题一般是NP-hard问题,而动态调度问题的性能指标比静态调度的更为复杂,并且以多目标综合性能指标居多.在系统地研究了多目标动态作业车间调度问题的基础上,运用滚动窗口技术和改进的遗传算法来解决动态调度问题.滚动窗口技术将调度过程分成连续静态调度区间,在每个区间内用多目标遗传算法进行调度优化.最后开发了面向应用的动态调度原型系统,并对改进的Job-shop基准实例进行仿真试验,证明了提出策略的可行性和有效性.     

A rolling horizon job shop rescheduling strategy in the dynamic environment

In this paper, the job shop scheduling problem in a dynamic environment is studied. Jobs arrive continuously, machines breakdown, machines are repaired and due dates of jobs may change during processing. Inspired by the rolling horizon optimisation method from predictive control technology, a periodic and event-driven rolling horizon scheduling strategy is presented and adapted to continuous processing in a changing environment. The scheduling algorithm is a hybrid of genetic algorithms and dispatching rules for solving the job shop scheduling problem with sequence-dependent set-up time and due date constraints. Simulation results show that the proposed strategy is more suitable for a dynamic job shop environment than the static scheduling strategy.     

考虑工序相关性的动态Job shop调度问题启发式算法

提出一类考虑工序相关性的、工件批量到达的动态Job shop 调度问题,在对工序相关性进行了定义和数学描述的基础上,进一步建立了动态Job shop 调度问题的优化模型。设计了一种组合式调度规则RAN(FCFS,ODD),并提出了基于规则的启发式算法以及该类动态Job shop 调度问题的算例生成方法。为验证算法和比较评估调度规则的性能,对算例采用文献提出的7种调度规则和RAN(FCFS,ODD)进行了仿真调度,对调度结果的分析表明了算法的有效性和RAN(FCFS,ODD)调度规则求解所提出的动态Job Shop 调度问题的优越性能。     

基于Agent的芯片制造生产线动态调度方法研究

基于智能体技术,提出了芯片制造生产线动态调度新方法,实现了投料调度、工件调度与设备维护调度的集成。首先,给出了基于智能体的动态调度模型,该模型中包括管理智能体、投料智能体、工件智能体、设备智能体、运输智能体与人员智能体。投料智能体用于实现投料控制,工件调度通过工件智能体与设备智能体之间的协商实现,设备维护调度由设备智能体实现。通过智能体间的合作,能够实现投料控制、工件调度与设备维护调度的协同进行。然后给出了工件智能体与设备智能体问协商使用的协商协议（即带有时间约束的单步协商协议）,以及智能体决策中使用的调度算法（包括投料调度算法,工件调度算法与设备维护调度算法）。最后,通过实例,给出了方法的使用过程,并进行了模拟仿真。仿真结果表明,集成的调度方法能更好地优化模型的生产率、加工周期与设备利用率,最终提高准时交货率。     

Dynamic dispatching for interbay material handling by using modified Hungarian algorithm and fuzzy-logic-based control

Semiconductor Wafer Fabrication System (SWFS) is one of the most complicate discrete processing systems in the world. As the wafer size grows from 200 to 300 mm and then to 450 mm in recent years, the interbay automated material handling system (AMHS) has been widely adopted. How to improve the overall efficiency of interbay material handling has therefore become a critical and urgent problem to be solved. However, the large-scale, dynamic and stochastic production environment significantly substantiates the complexity of the scheduling problem. Aiming to meet the demands of adaptive adjusting, efficient scheduling and multiple-objective optimization, a dynamic dispatching method based on modified Hungarian algorithm is proposed. The system parameters, including cassette due date, cassette waiting time, and system load are simultaneously considered, and furthermore the multi-parameters’ weight coefficients are adjusted dynamically by using the fuzzy-logic-based control. Discrete event simulation models are constructed with the eM-Plant software to evaluate the proposed approach. Experimental results show that the proposed dynamic dispatching method improves the system efficiency in terms of mean delivery time, mean waiting time and so on. Meanwhile, the proposed dispatching method has a better comprehensive performance such as the robustness compared to conventional vehicle dispatching approaches.     

A mixed dispatching rule approach in FMS scheduling

Short-term scheduling in flexible manufacturing systems (FMSs) is a difficult problem because of the complexities and dynamic behavior of FMSs. To solve this problem, a dispatching rule approach is widely used. In this approach, however, a single dispatching rule is usually assigned for all machines in a system during a given scheduling interval. In this paper, a mixed dispatching rule which can assign a different dispatching rule for each machine is proposed. A search algorithm which selects an appropriate mixed dispatching rule using predictions based on discrete event simulation is developed for this approach. The search algorithm for the mixed dispatching rule is described in detail. The effectiveness (in meeting performance criteria) of the mixed dispatching rule and the efficiency of the search algorithm relative to exhaustive search (complete enumeration) is demonstrated on an FMS model. The mixed dispatching rule approach performs up to 15.9% better than the conventional approach, and is 4% better on average. The statistical significance of the results is dicussed.     

An adaptive approach to dynamic scheduling in knowledgeable manufacturing cell

To overcome deficiency in the global capacity of a single dispatching rule, it is vital to select a dispatching rule in real time for dynamic scheduling. Among the studies addressing the method for selecting dispatching rules, few have no requirements for domain knowledge or accurate training example, which is hard to acquire from the real production system. In this paper, a new learning algorithm, along with the presentation of an adaptive scheduling control policy, is proposed to obtain the dynamic scheduling knowledge effectively, and different dispatching rules are selected to schedule the jobs in the machine buffer according to the current transient state of the system. Case studies are given to illustrate the validity of the scheduling control policy.     

Information-Based Dynamic Manufacturing System Scheduling

Information about the state of the system is of paramount importance in determining the dynamics underlying manufacturing systems. In this paper, we present an adaptive scheduling policy for dynamic manufacturing system scheduling using information obtained from snapshots of the system at various points in time. Specifically, the framework presented allows for information-based dynamic scheduling where information collected about the system is used to (1) adjust appropriate parameters in the system and (2) search or optimize using genetic algorithms. The main feature of this policy is that it tailors the dispatching rule to be used at a given point in time to the prevailing state of the system. Experimental studies indicate the superiority of the suggested approach over the alternative approach involving the repeated application of a single dispatching rule for randomly generated test problems as well as a real system. In pa ticular, its relative performance improves further when there are frequent disruptions and when disruptions are caused by the introduction of tight due date jobs and machine breakdown—two of the most common sources of disruption in most manufacturing systems. From an operational perspective, the most important characteristics of the pattern-directed scheduling approach are its ability to incorporate the idiosyncratic characteristics of the given system into the dispatching rule selection process and its ability to refine itself incrementally on a continual basis by taking new system parameters into account.     

A nonlinear scheduling rule incorporating fuzzy-neural remaining cycle time estimator for scheduling a semiconductor manufacturing factory—a simulation study

Scheduling of a semiconductor wafer fabrication system (SWFS) is complicated due to its re-entrant product flow, high uncertainties in operations, and rapidly changing products and technologies; thus dispatching rules have been widely used for real-time scheduling because they can provide a very quick and pretty good solution. However, deciding how to select appropriate rules is very difficult and seldom tackled. This paper describes an approach into the evaluation and optimization of dispatching rules by integrating the simulation and response surface methodology (RSM). In order to implement the proposed approach, a dynamic bottleneck dispatching (DBD) policy is designed, in which bottlenecks are detected in a timely way and adaptive dispatching decisions are made according to the real-time conditions. In addition, two case studies are carried out to demonstrate the approach. One case compares DBD to regular rules, such as CR + FIFO, EDD, SRPT, SPT, SPNB and Justice, a bottleneck dispatching method. Simulation results show that the DBD policy is superior to the other six methods. In another case study, the parameters of DBD are optimized by RSM and desirability function, and the result proves that the optimized DBD method can get even better performance.     

Performance evaluation of priority dispatching rules in multi-level assembly job shops with jobs having weights for flowtime and tardiness

The problem of scheduling in dynamic shops is an important operational problem in view of its complexity and significance in terms of associated costs of scheduling. While a number of research studies have investigated the problem of scheduling in flow shops and job shops, only some attempts have been done to study the problem of scheduling in assembly job shops that manufacture multi-level jobs. The problem of scheduling in dynamic assembly job shops with jobs having weights for holding and tardiness of jobs deserves due attention. In this study an attempt has been made to propose new priority dispatching rules that minimize the performance measures related to weighted flowtime and weighted tardiness of jobs. The existing unweighted dispatching rules have been modified in view of the consideration of weights for flowtime and tardiness of jobs. The performances of the (modified) existing dispatching rules and the proposed dispatching rules are compared through exhaustive simulation experiments with the consideration of a number of different experimental settings involving due-date setting, utilization levels and types of job structures. The proposed dispatching rules are found to perform better than the existing ones in most experimental settings and with respect to a number of measures of performance.     

基于强化学习的模式驱动调度系统研究

目前,还没有一种调度规则能够根据系统环境状态的改变来进行自适应调整.对此,提出一种基于智能体的模式驱动调度系统,由智能体和仿真环境两个主要部分构成.其中,智能体将利用强化学习(Q学习算法)进行训练,以提高其动态选择合适调度规则的能力.仿真结果表明,这种模式驱动调度系统能够很好地根据系统环境状态的改变选择出对应的最优调度规则,且其调度性能优于单一调度规则,适合于系统环境不断变化的动态调度.