基于调度规则和免疫算法的作业车间多目标调度

利用动态在线调度方法对动态环境下的作业车间进行研究,采用优先级调度规则对大量调度案例进行求解,针对7个调度目标,从备选调度规则集中选出了单个目标下性能最优的调度规则;为实现调度规则的动态选择以适应多目标调度,基于免疫系统中的独特型网络理论,设计了一种免疫调度算法．根据算法,定义了有效的抗体和抗原结构,并通过抗体间亲和力计算、抗体浓度计算、抗体选择等关键步骤,实现对调度规则的动态控制．仿真测试数据表明,所设计的免疫调度算法能根据不同的车间情况,快速选出不同的调度规则满足多个调度目标,有效解决了作业车间多目标调度问题．     

基于上下文赌博机的自适应实时车间调度

传统的多Agent车间调度方法使用单一调度规则, 忽略了生产环境变化对调度规则适用性的影响, 导致调度结果欠佳. 本文针对该问题提出一种自适应实时车间调度方法, 通过上下文赌博机对工件调度过程进行类比建模. 经过若干回合学习的上下文赌博机模型能够依据生产环境制定调度决策, 获得优异的调度结果. 最后, 通过仿真实验验证了提出方法的有效性.     

数字车间生产调度原型系统及调度仿真

文章介绍所开发的一个基于Agent的数字车间的生产调度原型系统,包括它的组成、结构、平台选择和软件运行环境等方面的内容;并结合一个实际的生产任务,对车间调度系统对生产任务的招投标过程进行了仿真。     

基于Agent的混合流水车间动态调度系统

针对敏捷制造调度环境的不确定性、动态性以及混合流水车间（HFS）调度问题的特点,设计了一种基于多Agent的混合流水车间动态调度系统,系统由管理Agent、策略Agent、工件Agent和机器Agent构成。首先提出一种针对混合流水车间环境的插值排序（HIS）算法并集成于策略Agent中,该算法适用于静态调度和多种动态事件下的动态调度。然后,设计了各类Agent间的协调机制,在生产过程中所有Agent根据各自的行为逻辑独立工作并互相协调。在发生动态事件时,策略Agent调用HIS算法根据当前车间状态产生工件序列,随后各Agent根据生成的序列继续进行协调直到完成生产。最后进行了发生机器故障、订单插入情况下的重调度以及在线调度等动态调度的实例仿真,结果表明对于这些问题,HIS算法的求解效果均优于调度规则,特别是在故障重调度中,HIS算法重调度前后的Makespan一致度达97.6%,说明系统能够灵活和有效地处理混合流水车间动态调度问题。     

基于规则的工厂仿真调度环境

本文针对ＦＭＳ车间调度问题，分析了用仿真技术解决这一问题的可行性，介绍了基于离散事件仿真的调度软件－工厂仿真调度环境，并对其中基于规则的仿真决策机制做了详细的分析，最后，说明了决策规则库的设计方法。     

基于人机协同的动态车间生产调试问题的研究与实现

针对传统的Job-Shop型车间生产调度研究只能解决静态调度问题的现状,在分析实际生产调度过程中可能发生的动态事件的基础上,深入研究了三类典型动态调度事件的动态响应机制以及动态调度过程中的几个关键算法,开发了基于以上研究的面向精密加工生产的车间调度系统,较好地解决了实际车间生产调度中出现的动态调度问题。     

基于强化学习的智能车间调度策略研究综述

智能制造是我国制造业发展的必然趋势,而智能车间调度是制造业升级和深化“两化融合”的关键技术。主要研究强化学习算法在车间调度问题中的应用,为后续的研究奠定基础。其中车间调度主要包括静态调度和动态调度;强化学习算法主要包括基于值函数和AC（Actor-Critic）网络。首先,从总体上阐述了强化学习方法在作业车间调度和流水车间调度这两大问题上的研究现状;其次,对车间调度问题的数学模型以及强化学习算法中最关键的马尔可夫模型建立规则进行分类讨论;最后,根据研究现状和当前工业数字化转型需求,对智能车间调度技术的未来研究方向进行了展望。     

基于Flexsim的车间调度优化

基于三维动画仿真软件Flexsim,文中对航空附件加工车间这种多品种、小批量生产的作业车间（Job-Shop）进行了调度优化研究。介绍了Flexsim连接数据库的技术与遗传算法求解生产调度的方法;在Flexsim中建立虚拟生产车间模型,并且在Flexsim虚拟车间模型内部嵌入C＋＋数据库操纵程序,将仿真模型与生产管理数据库连接,使模型可以实时采集生产数据;最后通过实例说明Flexsim仿真与调度优化相结合的方法可以有效地提高航空附件加工车间的效益,证明了方法的有效性。     

基于Flexsim的车间调度优化

基于三维动画仿真软件Flexsim,文中对航空附件加工车间这种多品种、小批量生产的作业车间（Job-Shop）进行了调度优化研究。介绍了Flexsim连接数据库的技术与遗传算法求解生产调度的方法;在Flexsim中建立虚拟生产车间模型,并且在Flexsim虚拟车间模型内部嵌入C＋＋数据库操纵程序,将仿真模型与生产管理数据库连接,使模型可以实时采集生产数据;最后通过实例说明Flexsim仿真与调度优化相结合的方法可以有效地提高航空附件加工车间的效益,证明了方法的有效性。     

基于合同网协议交互投标的动态调度方法研究

柔性制造系统使生产加工路径有很多可选性，所以调度系统必须考虑机器调度问题。分配规则调度是一种最基本、最具影响力的动态调度方法。然而，分配规则调度方法很少考虑机器顺序选择。兼顾工件选择和机器选择两方面，本文运用交互投标过程，构建基于合同网协议调度的协商规则。研究作业车间动态调度问题，提出并构建了5种合同网规则调度方法。通过实验分析结果表明，基于合同网交互投标模式的规则调度能够大大改善调度系统性能，提高设备的利用率和设备负荷平衡指标。     

An AIS-based hybrid algorithm with PDRs for multi-objective dynamic online job shop scheduling problem

The dynamic online job shop scheduling problem (JSSP) is formulated based on the classical combinatorial optimization problem – JSSP with the assumption that new jobs continuously arrive at the job shop in a stochastic manner with the existence of unpredictable disturbances during the scheduling process. This problem is hard to solve due to its inherent uncertainty and complexity. This paper models this class of problem as a multi-objective problem and solves it by hybridizing the artificial intelligence method of artificial immune systems (AIS) and priority dispatching rules (PDRs). The immune network theory of AIS is applied to establish the idiotypic network model for priority dispatching rules to dynamically control the dispatching rule selection process for each operation under the dynamic environment. Based on the defined job shop situations, the dispatching rules that perform best under specific environment conditions are selected as antibodies, which are the key elements to construct the idiotypic network. Experiments are designed to demonstrate the efficiency and competitiveness of this model.     

A neural network model and algorithm for the hybrid flow shop scheduling problem in a dynamic environment

A hybrid flow shop (HFS) is a generalized flow shop with multiple machines in some stages. HFS is fairly common in flexible manufacturing and in process industry. Because manufacturing systems often operate in a stochastic and dynamic environment, dynamic hybrid flow shop scheduling is frequently encountered in practice. This paper proposes a neural network model and algorithm to solve the dynamic hybrid flow shop scheduling problem. In order to obtain training examples for the neural network, we first study, through simulation, the performance of some dispatching rules that have demonstrated effectiveness in the previous related research. The results are then transformed into training examples. The training process is optimized by the delta-bar-delta (DBD) method that can speed up training convergence. The most commonly used dispatching rules are used as benchmarks. Simulation results show that the performance of the neural network approach is much better than that of the traditional dispatching rules.This revised version was published in June 2005 with corrected page numbers.     

Distributed Reinforcement Learning Control for Batch Sequencing and Sizing in Just-In-Time Manufacturing Systems

This paper presents an approach that is suitable for Just-In-Time (JIT) production for multi-objective scheduling problem in dynamically changing shop floor environment. The proposed distributed learning and control (DLC) approach integrates part-driven distributed arrival time control (DATC) and machine-driven distributed reinforcement learning based control. With DATC, part controllers adjust their associated parts' arrival time to minimize due-date deviation. Within the restricted pattern of arrivals, machine controllers are concurrently searching for optimal dispatching policies. The machine control problem is modeled as Semi Markov Decision Process (SMDP) and solved using Q-learning. The DLC algorithms are evaluated using simulation for two types of manufacturing systems: family scheduling and dynamic batch sizing. Results show that DLC algorithms achieve significant performance improvement over usual dispatching rules in complex real-time shop floor control problems for JIT production.     

Study of stochastic sequence-dependent flexible flow shop via developing a dispatching rule and a hybrid GA

A flexible flow shop is a generalized flow shop with multiple machines in some stages. This system is fairly common in flexible manufacturing and in process industry. In most practical environments, scheduling is an ongoing reactive process where the presence of real time information continually forces reconsideration of pre-established schedules. This paper studies a flexible flow shop system considering non-deterministic and dynamic arrival of jobs and also sequence dependent setup times. The problem objective is to determine a schedule that minimizes average tardiness of jobs. Since the problem class is NP-hard, a novel dispatching rule and hybrid genetic algorithm have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the problem is developed for the purpose of experimentation. The most commonly used dispatching rules from the literature and two new methods presented in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization, setup time level and number of stages. The results indicate that methods proposed in this study are much better than the traditional dispatching rules.     

Dynamic scheduling of manufacturing job shops using genetic algorithms

Most job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule permutation approach that systematically permutes an initial pool of randomly generated schedules to return the best schedule found to date.A dynamic scheduling problem was designed to closely reflect a real job shop scheduling environment. Two performance measures, namely mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. To span a varied job shop environment, three factors were identified and varied between two levels each. The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.     

智能制造系统基于数据驱动的车间实时调度

智能制造系统采用大量先进的信息技术,为车间实时调度提供技术基础.各类信息技术在生产制造过程中的广泛应用使得制造系统积累了大量与生产调度相关的数据,因此,通过利用历史生产调度数据和智能装备收集到的实时生产数据,建立基于数据驱动的生产实时调度方法成为新型制造环境下实现高效调度的新思路.针对智能制造环境下的混合流水车间实时调度问题,提出基于BP神经网络的数据驱动的实时调度方法,从历史近优的调度方案中提取用于调度知识挖掘的样本数据,通过BP神经网络训练学习获取生产系统状态与调度规则的映射关系网络,并将其应用于生产在线实时调度.数值实验表明,所提出的方法优于固定单一调度规则,在不同的调度性能指标下其效果均稳定且良好.     

基于ABC-SA混合算法的群控电梯优化调度

为解决电梯群控系统（Elevator group control system,EGCS）时间和能耗性能不理想的问题,提出一种基于改进人工蜂群的电梯群控多目标优化调度算法。首先,针对EGCS控制目标复杂性,建立具有多评价指标的群控电梯调度模型,依据该模型的适应度值进行合理派梯选择;其次,引入模拟退火准则优化基本人工蜂群算法结构以解决算法易陷入局部最优解的问题,使用混合改进的人工蜂群算法进行多目标优化调度。仿真结果表明,所提算法在侯梯时间、乘梯时间和停靠次数三个性能指标上对比基本人工蜂群算法均有所提高,有效说明该方法在求解柔性多目标群控电梯优化调度时具有一定的优越性。     

Scheduling rules for dynamic shops that manufacture multi-level jobs

The problem of scheduling in dynamic conventional jobshops has been extensively investigated over many years. However, the problem of scheduling in assembly jobshops (i.e. shops that manufacture multi-level jobs with components and subassemblies) has been relatively less investigated in spite of the fact that assembly jobshops are frequently encountered in real life. A survey of literature on dynamic assembly jobshop scheduling has revealed that the TWKR-RRP rule is the best one for minimizing the mean flowtime and staging delay, and the job due-date (JDD) rule is the best for minimizing the mean tardiness of jobs. However, the objectives of minimizing the maximum flowtime (and maximum staging delay) and standard deviation of flowtime (and standard deviation of staging delay) are as important as the minimization of mean flowtime and mean staging delay. Likewise, the objectives of minimizing the maximum tardiness and standard deviation of tardiness are also as important as the minimization of mean tardiness. The reason is that the maximum and standard deviation values of a performance measure indicate the worst-case performance of a dispatching rule. The present study seeks to develop efficient dispatching rules to minimize the maximum and standard deviation of flowtime and staging delay, and the maximum and the standard deviation of conditional tardiness of jobs. The dispatching rules are based on the computation of the earliest completion time of a job and consequently determining the latest finish time of operations on components/subassemblies of a job. An extensive simulation-based investigation of the performance evaluation of the existing dispatching rules and the proposed dispatching rules has been carried out by randomly generating jobs with different structures and different shop utilization levels. It has been found from the simulation study that the proposed rules are quite effective in minimizing the maximum and standard deviation of flowtime and staging delay, and the maximum conditional tardiness and standard deviation of conditional tardiness.     

Estimating order lead times in hybrid flowshops with different scheduling rules

In this study, we consider a problem of estimating order lead time in hybrid flowshops, where orders arrive dynamically. When an order arrives at the shop, the time duration between the arrival and completion (i.e., lead time) of the order is to be estimated. For good estimation, not only the order specification and the inventory status but also the scheduling method used in the shop should be taken into account. In this study, we consider three most common dispatching rules, i.e., FCFS, EDD, and SPT, for the shop scheduling, and develop three distinct estimation methods for the corresponding dispatching rules. A series of computational experiments were carried out and the results show that the proposed methods outperform the existing benchmarks in terms of two accuracy measures.