ASP环境下的离散生产车间调度系统

本文针对离散车间单件小批的生产特点,把基于规则的算法和启发式算法结合起来,给出了有效的优化算法。并以此算法为核心,设计了ASP(Application Service Provider)环境下的生产调度系统。该系统针对网络化协同生产的特点,在ASP模式下实现了对异地多个离散车间的作业计划编制和实时调度,监控生产进度。     

炼钢-连铸生产计划调度系统开发

针对炼钢-连铸生产以及钢厂现有信息化系统的特点,开发了面向钢厂MES的炼钢-连铸生产计划调度系统。系统通过与MES的数据接口收集生产作业计划、调度和生产运行实绩相关信息;设计和构建系统关系数据库,将通过MES接收的自ERP系统下达的生产批量计划和来自过程信息系统的实时生产状态信息分类集成并显示;设计基于生产过程信息的扰动自动识别和人工扰动自主设置的调度触发机制,按分类扰动处理机制进行重计划和重调度,调用智能优化算法编制生产作业计划,确定可行的生产调度方案,实现对调度方案的物流和工位的多种可视化表达方式;重计划和重调度信息可反馈至MES信息系统,为调度人员提供决策参考。通过在钢厂的在线测试表明,该系统可以满足实际生产需要,通过辅助调度决策可提高调度员决策的科学性和调度水平,提升和充分发挥MES的性能。     

半导体制造业MES中的生产计划调度研究

生产计划调度是制造执行系统的核心.基于半导体制造业生产特点,研究了半导体制造业MES中的生产计划调度问题,给出生产计划调度系统的基本构架.根据客户需求制定生产计划,再细化为车间层可操作的作业计划,确定每台设备上加工安排,通过拟实调度处理动态不确定问题.通过生产计划制定、作业计划生成和拟实调度可以实现半导体制造生产过程平稳进行.     

基于单件小批量生产的制造执行系统研究及其优化

作为连接企业上层生产计划管理系统和底层设备控制系统之间接口的制造执行系统,是当前管理信息系统研究和开发的热点与难点。本文探讨了适用于单件小批量生产的重型机械制造企业的制造执行系统的结构框架,详细地描述了其各个功能模块的作用,并给出了实现系统优化调度的遗传算法的问题编码,遗传操作和评价函数以及算法的整个过程。按照这种框架所构建的制造执行系统,能够实现生产作业计划的实时调度以及生产过程的实时控制。     

MES专题连载之二——MES的技术架构

MES在工厂综合自动化系统中起着中间层的作用——在MRP Ⅱ、ERP系统产生的长期计划的指导下,MES根据底层控制系统采集的与生产有关的实时数据,对短期生产作业的计划调度、监控、资源配置和生产过程进行优化。那么,MES依靠哪些技术、模块实现这一目标?尤其是与ERP、控制系统如何实现协作呢?     

MES，让制造与计划相统一

现在,以MRP Ⅱ、ERP为代表的管理信息系统,以及以数控加工、DNG单元和柔性制造为代表的自动化技术,在制造业行业已经大规模应用。尽管这两类系统的推广取得了一定效果,但却忽略了两者之间的有效配合,导致企业上层计划缺乏有效的实时信息支持、下层控制环节缺乏优化的调度与协调。计划层与车间执行层无法进行良好的双向信息流交互,企业就难以实时反应。 十多年前诞生的MES直指这一问题,目前在发达国家已经普遍推广。在我国,对MES的认识和应用还处于刚刚起步阶段。为此,我们特意邀请南京航空航天大学的专家做专题介绍,将从企业需求、技术模型、如何实施以及不同行业应用特点等角度系统地介绍MES这一概念。     

MES的技术架构

MES在工厂综合自动化系统中起着中间层的作用——在MRP Ⅱ．ERP系统产生的长期计划的指导下，MES根据底层控制系统采集的与生产有关的实时数据．对短期生产作业的计划调度、监控、资源配置和生产过程进行优化，那么，MES依靠哪些技术，模块实现这一目标?尤其是与ERP、控制系统如何实现协作呢?     

基于实时数据库技术的流程工业制造执行系统的设计

文章介绍了一种基于实时数据库技术的流程工业MES的设计方案,给出了流程工业MES体系结构及其功能模块,详细介绍了流程工业MES基于pSpace实时数据库的设计。实际应用表明,该MES实现了生产计划调度管理、质量管理、成本管理、绩效考核、设备管理和数据采集存储管理等功能,达到了企业生产过程和管理的一体化要求。     

基于单件小批量生产的制造执行系统研究及其优化

作为连接企业上层生产计划管理系统和底层设备控制系统之间接口的制造执行系统,是当前管理信息系统研究和开发的热点与难点.本文探讨了适用于单件小批量生产的重型机械制造企业的制造执行系统的结构框架,详细地描述了其各个功能模块的作用,并给出了实现系统优化调度的遗传算法的问题编码,遗传操作和评价函数以及算法的整个过程.按照这种框架所构建的制造执行系统,能够实现生产作业计划的实时调度以及生产过程的实时控制.     

基于单件小批量生产的制造执行系统研究及其优化

作为连接企业上层生产计划管理系统和底层设备控制系统之间接口的制造执行系统,是当前管理信息系统研究和开发的热点与难点.本文探讨了适用于单件小批量生产的重型机械制造企业的制造执行系统的结构框架,详细地描述了其各个功能模块的作用,并给出了实现系统优化调度的遗传算法的问题编码,遗传操作和评价函数以及算法的整个过程.按照这种框架所构建的制造执行系统,能够实现生产作业计划的实时调度以及生产过程的实时控制.……     

A batch splitting heuristic for dynamic job shop scheduling problem

The job shop scheduling problem has been a major target for many researchers. Unfortunately, though, most of the past studies assumed that a job consists of only a single part. If we assume that a job consists of a batch as in many real manufacturing environment, then we can obtain an improved schedule. However, then, the size of the scheduling problem would become too large to be solved in practical time limit. So, we proposed an algorithm to get an improved schedule by splitting the original batch into smaller batches, and thereby can meet the due date requirement, and adapt to unexpected dynamic events such as machine failure, rush order and expediting.     

Customer order management in service oriented holonic manufacturing

One of the most important problems when considering the design of manufacturing systems based on SOA paradigms is the integration of shop floor devices in the business processes at the enterprise level. This paper presents the design and implementation of the Customer Order Management (COM) module based on SOA architecture in the context of holonic manufacturing systems. The COM module is integrating with SOA enabled shop floor devices using industry standards. The implementation leverages a multi agent system suited for industrial applications integrated in a SOA environment capable of dynamic BPEL workflow generation and execution. The prototype consists in a SCA application for core COM module functionality and an extension for NetLogo MAS platform for SOA integration. The COM module interacts with the MES layer using real time events handled by the BPEL process implementation in the execution stage. A web based portal frontend for the COM module has been developed to allow real time tracking of customer orders, providing data about product batch execution and individual progress of each product on the production line.     

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.     

A Case Study on Integrated Production Planning and Scheduling in a Three-Stage Manufacturing System

This paper presents an integrated optimization model of production planning and scheduling for a three-stage manufacturing system, which is composed of a forward chain of three kinds of workshops: a job shop, a parallel flow shop consisting of parallel production lines, and a single machine shop. As the products at the second stage are assembled from the parts produced in its upstream workshop, a complicated production process is involved. On the basis of the analysis of the batch production, a dynamic batch splitting and amalgamating algorithm is proposed. Then, a heuristic algorithm based on a genetic algorithm (known as the integrated optimization algorithm) is proposed for solving the problem. Note to Practitioners-This paper presents a method for integrated production planning and scheduling in a three-stage manufacturing system consisting of a forward chain of three kinds of workshops, which is common in such enterprises as producers of automobiles and household electric appliances, as in the case of an autobody plant usually with the stamping workshop, the welding and assembling workshop, and the painting workshop. Herein, the production planning and scheduling problems are simultaneously addressed in the way that a feasible production plan can be obtained and the inventory reduced. A batch splitting and amalgamating algorithm is proposed for balancing the production time of the production lines. And a case study of the integrated planning and scheduling problem in a real autobody plant verifies the effectiveness of our method     

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。     

一种改进的求解柔性作业车间调度问题的灰狼算法

柔性作业车间调度问题是智能制造领域的一类典型调度问题,它是制造流程规划和管理中最关键的环节之一,有效的求解方法对提高生产效率具有重要的现实意义。本文基于经典灰狼算法进行改进,以优化最大完工时间为目标,提出一种改进的灰狼算法来求解柔性作业车间调度问题。算法首先采用基于权值的编码形式,实现对经典狼群算法中连续性编码的离散化;其次在迭代优化过程中加入随机游走策略,以增强局部搜索能力;然后在种群更新过程中加入尾部淘汰策略,在避免局部优化的同时增加种群多样性,合理扩大算法的广度搜索范围。在标准算例上的仿真实验结果表明,改进的灰狼算法在求解FJSP时比经典灰狼算法在寻优能力方面具有明显的优势,相比其它智能优化算法,本文所提算法在每种算例上均具有更好的优化性能。     

A Pareto archive particle swarm optimization for multi-objective job shop scheduling

In this paper, we present a particle swarm optimization for multi-objective job shop scheduling problem. The objective is to simultaneously minimize makespan and total tardiness of jobs. By constructing the corresponding relation between real vector and the chromosome obtained by using priority rule-based representation method, job shop scheduling is converted into a continuous optimization problem. We then design a Pareto archive particle swarm optimization, in which the global best position selection is combined with the crowding measure-based archive maintenance. The proposed algorithm is evaluated on a set of benchmark problems and the computational results show that the proposed particle swarm optimization is capable of producing a number of high-quality Pareto optimal scheduling plans.     

Recent developments in evolutionary computation for manufacturingoptimization: problems, solutions, and comparisons

The use of intelligent techniques in the manufacturing field has been growing the last decades due to the fact that most manufacturing optimization problems are combinatorial and NP hard. This paper examines recent developments in the field of evolutionary computation for manufacturing optimization. Significant papers in various areas are highlighted, and comparisons of results are given wherever data are available. A wide range of problems is covered, from job shop and flow shop scheduling, to process planning and assembly line balancing     

A survey of problems,solution techniques,and future challenges in scheduling semiconductor manufacturing operations

In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems found in semiconductor manufacturing systems. We describe batch scheduling problems, parallel machine scheduling problems, job shop scheduling problems, scheduling problems with auxiliary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some of the challenges in scheduling semiconductor manufacturing operations.     

遗传算法在烟草企业排产中的应用研究

卷烟制造行业的生产管理要求十分严格,生产计划必须严格满足工艺流程的要求,对生产计划时间安排必须特别准确,对生产过程控制和质量管理要求很高。文章研究了遗传算法在烟草企业排产中的应用,设计了一种基于遗传算法的排产算法模型,该模型被实际应用到常德卷烟厂MES系统的设计实现中,运行效果证明了其有效性。