最优化生产技术在模具企业车间作业计划中的应用研究

通过对作业计划编制方法和模具企业生产系统特点的简略分析与总结，提出在模具企业车间作业计划中运用最优化生产技术。研究了最优化生产技术应用于模具企业车间作业计划需要进一步改进的几个关键问题。针对模具企业生产过程的动态多变性，提出了一个闭环控制系统模型。     

基于模具企业的单件车间作业计划管理系统研究

模具企业作业计划是典型的单件车间调度问题,也是模具企业管理中的瓶颈所在.建立了模具车间作业计划问题的数学模型,基于遗传算法对作业计划问题进行了优化,并应用C#语言开发了基于模具企业的作业计划管理系统.最后通过实例得出经过算法优化后的甘特图,验证了算法的可行性和有效性.     

面向MES的模具CAPP中的工序相关性问题处理

实时监控与优化调度是解决模具企业生产管理问题的有效措施,而模具的加工艺是调度与控制车间生产过程的基础数据。常规的模具制造工艺规划系统只是从工件的角度编制工艺,忽略了工件加工过程中的工序相关性,从而影响了车间作业优化调度的可行性和合理性。本文从模具企业车间作业优化调度与控制的角度出发,面向车间MES研究了模具CAPP中工序相关性问题的处理方法。     

模具车间作业控制系统设计

车间作业控制是制造企业MRPⅡ／ERP系统的一个重要组成部分,是MRPⅡ／ERP计划的具体实施环节。以模具生产为例,阐述了一种车间作业控制系统的功能结构和设计方法。     

单件生产系统中动态车间作业计划与监控系统的集成研究

对于动态多变的单件生产系统来说，车间作业计划是最有效的生产管理措施之一；在编制这类生产系统的车间作业计划时，与监控系统有效集成又是必须考虑的最关键的问题。为此研究了单件生产系统中车间作业计划与监控系统的集成问题，又提出了集成的总体结构，基于这一结构，叉提出了日程作业计划的3种确定方法，较详细地分析了在进行车间作业计划滚动编制时，对各种监控反馈信息的处理措施。     

启发式算法在单件车间工序排序问题中的应用

本文探讨了车间作业计划的排序问题 ,深入分析和研究了启发式算法 ,结合模具车间的实际情况 ,采用基于无延迟作业计划的概率调度法 ,并进行适当的改进 ,实现对模具车间作业计划的排序 ,取得了较好的效果。     

模具企业车间作业计划中的关键路径及其求解算法

提出了模具企业车间作业计划中关键路径的概念，分析了关键路径的特点；设计了关键路径的逆推搜索算法，并通过算例对算法进行了详细说明和验证；对车间作业计划编制过程中的关键路径进行了讨论。     

浅谈制造执行系统

制造执行系统(MES)作为MRPⅡ系统的扩充,是企业资源计划系统(ERP)与车间作业控制系统(PAC)的纽带,可对工序进行精确计划和排序,对车间作业优化调度,获得工序作业计划的动态最优解,从而使企业的生产管理和控制更加科学化、实时化和规范化。     

APS技术在注塑机企业钣金车间的应用

针对某注塑机制造企业钣金件车间的生产计划和排程,分析原有直接采用企业管理解决方案(SAP)中物料需求计划(MRP)运算结果作为车间作业计划这一方式的不足之处,提出运用高级计划与排程(APS)技术,根据注塑机企业钣金件生产的特点,编程建立资源模型,集成SAP系统的输入数据,实现每台设备小时级别的精细工序排程,通过扫描派工单上的条码收集完工反馈信息,用于滚动重排车间作业计划,最后将APS排程结果用可交互的设备-工序甘特图形式,交付给钣金车间基层管理人员,用于人工干预。     

基于车间作业计划及调度监控的生产

概述了企业信息化的含义及内容,结合包装设备企业的实际情况,开发了以车间作业计划及调度监控为核心的生产管理系统.该生产管理系统主要模块包括:工艺管理子系统;设备管理子系统;车间作业计划及调度子系统;人员管理子系统;仃单管理子系统;产品生产成本估算子系统;报表及文档生成子系统,所开发的生产管理系统已在企业实施并取得良好的管理效果,提高了企业竞争力.     

Dynamic production planning model: a dynamic programming approach

Production planning is one of the most important issues in manufacturing. The nature of this problem is complex and therefore researchers have studied it under several and different assumptions. In this paper, applied production planning problem is studied in a general manner and it is assumed that there exists an optimal control problem that its production planning strategy is a digital controller and must be optimized. Since this is a random problem because of stochastic values of sales in future, it is modeled as a stochastic dynamic programming and then it is transformed to a linear programming model using successive approximations. Then, it is proved that these two models are equivalent. The main objective of the proposed model is achieving optimal decisions using forecasting sales which can be applied in master production schedule, manufacturing resource planning, capacity requirements planning, and job shop/shop floor scheduling.     

任务工时不确定的模具车间前摄性调度研究

由于模具生产属于非重复性生产模式,各工序的工时具有很强的随机不确定性,这给模具车间制定合理可行的作业计划带来了一定的困难。针对这一实际问题,提出了一种考虑任务工时不确定性的前摄性车间调度算法。首先,分析了模具精加工环节的两道关键工序对制造系统稳定性的影响,并基于工序的工时不确定特性,建立了任务工时不确定的离散概率模型;然后,以调度方案的稳定度作为优化目标,构建了两阶段流水车间前摄性调度模型,针对该模型,提出了一种变宽集束搜索求解算法;最后,将该算法与定宽集束搜索算法进行对比分析,结果表明该算法能很好地兼顾求解质量和计算时间。     

分段式车间作业调度算法

车间作业调度问题是制造系统运筹技术、管理技术与优化技术发展的核心。本文对离散作业型 (Job Shop)车间中的作业调度问题做了探讨 ,并根据离散作业调度的阶段性提出了基于作业状态空间的逐段式车间作业调度算法。通过对一个实际车间作业调度仿真比较 ,此算法运算速度比最短加工时间 (SPT)和最少工作量剩余 (L WR)算法快 ,其调度结果具有可执行性     

A clustering-based modified variable neighborhood search algorithm for a dynamic job shop scheduling problem

The dynamic job shop scheduling (DJSS) problem occurs when some real-time events are taken into account in the ordinary job shop scheduling problem. Most researches about the DJSS problem have focused on methods in which the problem’s input data structure and their probable relationship are not considered in the optimization process while some useful information can be extracted from such data. In this paper, the variable neighborhood search (VNS) combined with the k-means algorithm as a modified VNS (MVNS) algorithm is proposed to address the DJSS problem. The k-means algorithm as a cluster analysis algorithm is used to place similar jobs according to their processing time into the same clusters. Jobs from different clusters are considered to have greater probability to be selected when an adjacent for a solution is made in an optimization process using the MVNS algorithm. To deal with the dynamic nature of the problem, an event-driven policy is also selected. Computational results obtained using the proposed method in comparison with VNS and other common algorithms illustrate better performance in a variety of shop floor conditions.     

GA based heuristic for the open job shop scheduling problem

Open job shop scheduling is a kind of job shop scheduling in which operations can be performed in any order. In this paper an attempt is made to develop a heuristic for the open job shop scheduling problem using genetic algorithm to minimize makespan. Genetic algorithm operators are suitably modified to maintain feasibility. The results are statistically compared and found to be significantly better than the earlier reported results.     

面向IC生产模式的Job Shop调度问题的研究

针对即时定制生产模式的车间调度的特点,提出基于粒子群算法(PSO)的车间调度问题的解决方案.利用粒子群算法本身的优越性解决复杂的车间作业排序问题,克服了传统调度算法存在寻优效率低或全局寻优能力差的弱点.对粒子群的编码及寻优操作进行研究,确定了更适合车间调度问题的编码和操作方式,并将算法进行编程,应用到系统的车间调度部分.仿真结果表明,通过设置适当的参数,可以快速地得到理想的排序结果,能够适用于IC生产模式的车间调度问题.     

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.     

Application of chance-constrained programming for stochastic group shop scheduling problem

In this paper, we study a group shop scheduling (GSS) problem subject to uncertain release dates and processing times. The GSS problem is a general formulation including the other shop scheduling problems such as the flow shop, the job shop, and the open shop scheduling problems. The objective is to find a job schedule which minimizes the total weighted completion time. We solve this problem based on the chance-constrained programming. First, the problem is formulated in a form of stochastic programming and then prepared in a form of deterministic mixed binary integer linear programming such that it can be solved by a linear programming solver. To solve the problem efficiently, we develop an efficient hybrid method. Exploiting a heuristic algorithm in order to satisfy the constraints, an ant colony optimization algorithm is applied to construct high-quality solutions to the problem. The proposed approach is tested on instances where the random variables are normally, uniformly, or exponentially distributed.