基于数据的复杂制造过程调度

现代制造企业规模庞大、过程复杂等特征给制造过程的调度决策带来了极大的挑战. 一方面, 使用传统方法建立指导生产过程调度的精确数学模型变得越来越困难; 另一方面, 因缺乏准确、及时的模型参数而往往导致低下的模型使用效果. 在此情况下, 基于数据--信息--知识--决策的信息提炼轨迹, 有必要探寻新的基于数据的复杂制造过程的调度理论与方法. 在综述国内外相关研究的基础上, 提出了由数据层与模型层构成的基于数据的复杂制造过程调度架构, 并对该结构框架下的相关理论、方法及实施技术进行了探讨.     

间歇过程生产调度建模的研究

生产调度是为实现某一目的而对共同使用的资源进行时间上的分配.调度中存在大量的模糊因素,将模糊的思想运用到调度领域可以帮助决策者进行有效决策.本文提出一种基于模糊规划的间歇过程生产调度建模方法及其模糊优化的新算法.应用模糊集合论的方法,在已有模糊规划模型的基础上,针对间歇生产过程,分析调度中存在的模糊信息以及出现的形式和方式,将确定型生产调度模型的约束条件和目标方程中的参数模糊化,采用非精确的量化形式,以隶属函数来表示,建立基于模糊参数的生产调度模糊线性规划模型MIFCLP.通过对一个调度问题实例进行了仿真,仿真结果证明,采用模糊模型更容易得到可行解,采用模糊线性规划解决间歇生产调度问题是一种有效方法.     

生产过程智能调度方法研究进展

生产过程调度是现代化生产管理的一个重要环节,也是生产管理中长期未解决的一个难题,是一个重要的研究领域。本文在对生产过程调度问题及其传统调度方法进行简要综述的基础上,重点分析了各类生产过程智能调度方法及研究现状,并对智能调度方法进一步的研究内容作了展望。     

基于收益的软件过程资源调度优化方法

软件项目管理人员须对软件过程中的各种资源进行优化调度,但依靠主观判断和个人经验的资源调度方法具有不稳定性和不可靠性,需要提供客观可靠的软件过程资源调度方法和工具。基于收益的资源调度优化方法通过对软件过程的资源调度进行建模,描述和定义投入资源产生的收益,分析软件过程中活动、资源和收益的各种约束关系,采用基于动态规划的优化算法以较高效率完成资源调度,使资源在软件过程中有效利用。     

基于数据的流程工业生产过程指标预测方法综述

生产过程关键指标的预测对于流程工业生产调度,安全生产和节能环保有着重要作用.目前,已有多种基于工业生产数据提出的生产过程指标预测方法,主要涉及特征（变量）选择,预测模型构建及其模型参数优化这三方面.本文分别针对以上三方面论述了基于数据的工业生产过程指标预测国内外研究现状,分析了各种方法的优缺点.最后,指出了流程工业生产过程指标预测方法在工业大数据及知识自动化等方面的未来研究方向和前景.     

基于数据的决策方法综述

现代的决策问题与传统环境相比具有两个特点, 首先是系统自动化水平的提高带来的大量原始数据, 另外则是由于现实决策问题的复杂性和不确定性导致的机理模型无法准确建立. 面对这样的特点, 传统的基于机理模型的决策方法无法得到有效应用, 于是, 大量的研究工作围绕基于数据的决策方法展开. 本文根据决策问题的性质从三个方面综述了当前被普遍关注和应用的基于数据的决策方法: 分类方法、决策分析方法和优化方法, 针对各种具体方法, 总结了该方法的特征、发展过程以及前景.     

生产调度理论和方法研究综述

生产调度理论与方法研究是非常困难的课题,但是对企业或行业提高生产效率和效益是至关重要的,特别是在当前市场经济时代,生产调度理论和方法的研究已经有50多年的历史,但是经典调度理论和实际调度问题之间仍然存在着鸿沟。针对这一特点,指出了当前实际生产调度存在的一些问题和需要考虑的各种因素,例举了主要的生产调度方法和典型应用,指出了各种方法的优缺点。最后,总结并提出了生产调度理论和方法的未来研究方向和今后工作的建议。     

基于COM的发酵过程调度优化软件开发

在生物制药多罐并行发酵过程中，关键变量在线检测困难，发酵过程的优化调度决策难度高。本文采用了基于COM的组件化程序设计方法，设计了多罐并行发酵过程的调度优化软件系统。利用该软件，可以实现发酵过程关键变量的超前预报和罐批的在线分类。并给出了以经济效益最优为目标的停罐调度策略。     

基于连续过程特性的炼油生产调度优化研究

针对炼油生产调度问题,基于连续过程特性建立了广义析取规划调度模型。提出了连续性、长期性等连续过程特性定义及其性能指标,基于析取范式和逻辑命题对调度初期、中期和末期的连续过程特性进行了描述,并对连续过程特性性能进行了量化表达和评价,以实现生产利润和连续过程特性性能的综合利益最大化。以某炼油过程生产调度为例,对比有无连续过程特性表达的优化调度仿真结果表明：基于连续过程特性调度模型的调度方案能够提高连续过程特性性能,且更可行、实用,验证了模型的可行性和有效性。     

基于数据和知识的工业过程监视及故障诊断综述

从复杂工业过程所可能具有的过程特性及数据存取过程中引入的数据特性分析出发,综述了具有复杂数据特性的工业过程的多元统计监视方法,并分别讨论了基于数据和基于知识方法进行故障诊断的优势、进展、适用范围及二者结合的可能.最后探讨了这一领域中值得进一步研究的问题和可能的发展方向.     

Intelligent scheduling with tabu search: An application to jobs with linear delay penalties and sequence-dependent setup costs and times

In this article we study thetabu search (TS) method in an application for solving an important class of scheduling problems. Tabu search is characterized by integrating artificial intelligence and optimization principles, with particular emphasis on exploiting flexible memory structures, to yield a highly effective solution procedure. We first discuss the problem of minimizing the sum of the setup costs and linear delay penalties when N jobs, arriving at time zero, are to be scheduled for sequential processing on a continuously available machine. A prototype TS method is developed for this problem using the common approach of exchanging the position of two jobs to transform one schedule into another. A more powerful method is then developed that employs insert moves in combination with swap moves to search the solution space. This method and the best parameters found for it during the preliminary experimentation with the prototype procedure are used to obtain solutions to a more complex problem that considers setup times in addition to setup costs. In this case, our procedure succeeded in finding optimal solutions to all problems for which these solutions are known and a better solution to a larger problem for which optimizing procedures exceeded a specified time limit (branch and bound) or reached a memory overflow (branch and bound/dynamic programming) before normal termination. These experiments confirm not only the effectiveness but also the robustness of the TS method, in terms of the solution quality obtained with a common set of parameter choices for two related but different problems.     

柔性车间生产排产调度优化方法

为满足柔性制造企业在车间生产中合理安排生产排产调度的需要,提出柔性车间生产排产调度优化方法。首先,通过分析车间生产排产问题的特点,制定满足车间应用需求和各种资源限制的生产排产总体流程,从而设计基于约束条件的生产对象关系模型;其次,提出一种动态策略差分进化算法,根据个体之间的拥挤度动态选择变异策略,设计基于工序位置的编解码方案,其能快速有效地进行求解,从而得到最佳调度方案,提高设备运行效率,实现资源利用的最大化;最后,通过6个标准测试函数、FT6-6测试问题及生产调度应用实例验证了算法的有效性。     

An efficient search method for multi-objective flexible job shop scheduling problems

Flexible job shop scheduling is very important in both fields of production management and combinatorial optimization. Owing to the high computational complexity, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches. Motivated by some empirical knowledge, we propose an efficient search method for the multi-objective flexible job shop scheduling problems in this paper. Through the work presented in this work, we hope to move a step closer to the ultimate vision of an automated system for generating optimal or near-optimal production schedules. The final experimental results have shown that the proposed algorithm is a feasible and effective approach for the multi-objective flexible job shop scheduling problems.     

钢铁生产中的加热炉优化调度算法研究

钢铁生产中的加热炉调度问题属于组合优化中的NP—hard问题．对此．建立了加热炉调度问题的数学规划模型,并提出一种用于求解该问题的超启发式算法——遗传局部搜索算法．基于生产实际数据的仿真实验表明.所提出的方法适用于生产实际．效果优于目前现场使用的人工调度方法．     

Heuristic factory planning algorithm for advanced planning and scheduling

This study focuses on solving the factory planning (FP) problem for product structures with multiple final products. In situations in which the capacity of the work center is limited and multiple job stages are sequentially dependent, the algorithm proposed in this study is able to plan all the jobs, while minimizing delay time, cycle time, and advance time. Though mixed integer programming (MIP) is a popular way to solve supply chain factory planning problems, the MIP model becomes insolvable for complex FP problems, due to the time and computer resources required. For this reason, this study proposes a heuristic algorithm, called the heuristic factory planning algorithm (HFPA), to solve the supply chain factory planning problem efficiently and effectively. HFPA first identifies the bottleneck work center and sorts the work centers according to workload, placing the work center with the heaviest workload ahead of the others. HFPA then groups and sorts jobs according to various criteria, for example, dependency on the bottleneck work center, the workload at the bottleneck work center, and the due date. HFPA plans jobs individually in three iterations. First, it plans jobs without preempting, advancing, and/or delaying. Jobs that cannot be scheduled under these conditions are scheduled in the second iteration, which allows preemption. In the final iteration, which allows jobs to be preempted, advanced, and delayed, all the remaining jobs are scheduled. A prototype was constructed and tested to show HFPA's effectiveness and efficiency. This algorithm's power was demonstrated using computational and complexity analysis.     

全球化背景下的制造业生产调度体系探讨

面对客户随机需求,制造业生产调度系统应促进生产的快速性、动态性和敏捷性。论文以制造业生产调度体系为研究对象,从生产车间调度的目标、方法、手段、资源等基本要素入手,总结了传统生产调度系统在实际生产中的问题,并概括了系统集成视角下的生产调度方法及其改进措施,介绍了不确定条件下生产调度策略及评价指标。运用系统集成的思想,探讨了供需链环境下的制造业生产调度系统模式,最终提出了生产调度系统的集成化、动态化、高效智能化、柔性化和排程可视化等发展方向。     

Lessons learned for building agile and flexible scheduling tool for turbulent environments in the extended enterprise

This paper presents results of a 2.5-year, multidisciplinary, university–industry collaborative effort investigating design of “Internet-based Scheduler for material Optimisation and agile ProducTion In MUlti-Site enterprises in agile manufacturing” (IS-OPTIMUS), a four nation collaborative project aimed at improvements in turbulent manufacturing environments. The focus of this paper is specifically on the content of the work carried out, along with the main benefits and results. Key to achieving the goals is following a complete project-life-cycle path from the initial stages where the industrial users requirements were identified and the system specification took place to the development and tuning of the final system. Design choices for software must strike a balance between the user flexibility required and the representing environment constraints, i.e. finite capacity scheduling, which takes production requirements from existing production planning systems, to schedule production resources like plant workers, critical tools and machines. The system consists of a material optimiser working closely with the finite capacity scheduler and a dynamic scheduler providing automatic reaction to real-time exceptions, thus deriving in solutions of higher performance.