宝信制造业执行系统BM2中的高级计划排程

在中国钢铁行业ERP、MES已经基本普及的今天,日益激烈的竞争直接催生了高级计划排程.钢铁企业已经迫切需要APS帮助解决日益困扰他们的生产计划相关问题.就钢铁行业制定中短期计划所遇到的问题展开讨论,并在此基础上,全面介绍了宝信制造业执行系统BM2产品的APS解决方案的系统功能、技术特点以及实施方式.重点论述了中期计划理念;展示了国内钢铁行业在APS领域的最新成果.     

基于进化算法的铝加工行业生产计划高级排程系统设计

现有排程系统存在加工时间长、加工效率低等问题，为此设计基于进化算法的铝加工行业生产计划高级排程系统。首先，根据铝加工行业生产线分钟级别的生产排程及系统运行需要，设计高级排程系统框架结构；其次，利用进化算法，完成铝加工行业生产车间调度；最后，在动态环境下，实现生产计划高级排程。实验结果表明，新的排程系统在实际应用中可以在更短的时间内完成加工任务。     

MES合同计划排程的研究应用

针对钢铁行业中期计划排程所遇到的瓶颈工序、库存和准时交货等问题,提出了合同计划排程.合同计划排程是高级计划排程的一个分支,其采用ToC+TS相结合的中期计划理念.考虑到按照基于规则的排序方式很难得到优化的结果,所以引入了TS智能算法对合同计划进行建模优化.通过实际使用表明,合同计划排程系统有助于解决钢厂生产计划问题.     

基于RBF的离散制造业产量预测模型研究

离散制造业中产量预测系统是个动态复杂的系统,影响因素众多,面对这样一个复杂而庞大的系统很难将通用的系统直接应用到企业中,因此首先需要给出一个适合该企业的生产预测模型。在宁夏吴忠仪表厂的应用背景下,本文将RBF神经网络、广义回归神经网络等算法在企业应用中进行比较,找到最适合吴忠仪表厂的产量预测模型,对实验结果进行分析对比。并在企业原有的高级计划排程APS基础上将阀体、阀芯、阀座这三种常用零件产量预测模型应用到其中,在企业生产计划的实际安排中发挥了重大的作用。     

冷轧制造一体化自动排程系统设计及应用

以钢铁企业冷轧镀锡薄板生产一体化自动排程为案例,分析钢铁企业冷轧生产各工序的业务特点,设计并开发了冷轧生产自动排程系统。系统通过前后工序联合排程、计划生产时间实时更新、计划执行图形化展示、制造管理与生产过程控制纵向集成等关键技术,创新了生产排程和计划执行的工艺管控模式,降低了产品库存,缩短了合同交货期,提高了客户满意度。     

生产作业排程单道工序的模拟仿真算法研究

依据现实世界生产系统的实际运作情况,设计了一种单道工序作业排程的模拟仿真算法,相对于其他一些排程算法,该算法能比较准确真实的描绘生产系统机台作业的运行情况,准确得出作业的开始运行时间,结束时间及机台能力负荷状况。该算法运用于某实际ERP系统的生产实践表明:该算法能全面提高生产效率和机器设备使用率,为实际现场排程提供客观依据。     

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。     

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充分考虑生产的快速性、动态性和敏捷性等要素。本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。     

基于分布式数据缓存技术的Web-OLAP系统研究

为了解决在分布式环境下,Web-OLAP系统并发访问量急剧增加导致OLAP服务器负担过重的问题,提出一种基于分布式数据缓存技术的Web-OLAP系统。给出了该系统的总体框架和分布式缓存数据的表示,并设计了分布式缓存数据的管理算法。具体的应用实例表明,该方法可以有效地提高分布式环境下Web-OLAP系统的访问效率,较大缩短系统的响应时间。     

基于RR—DNS和MASK SORT的负载均衡策略实现

负载均衡技术是提高应用服务器承载能力和效率关键技术,通过对DNS轮序排程和网络掩码排序技术原理的深入研究,将两种技术进行了有效结合,给出了解决网络负载均衡问题的具体方法,该方法详细介绍了DNS轮序排程和网络掩码排序的实现过程及Windows Server 2003环境下的配置与测试步骤;系统测试结果表明,该方法实现了用户访问负载在各应用服务器间的有效均衡,提高了访问的响应速度,且系统工作稳定、易于实现.     

Optimizing distributed no-wait flow shop scheduling problem with setup times and maintenance operations via iterated greedy algorithm

Nowadays, distributed scheduling problem is a reality in many companies. Over the last years, an increasingly attention has been given to the distributed flow shop scheduling problem and the addition of constraints to the problem. This article introduces the distributed no-wait flow shop scheduling problem with sequence-dependent setup times and maintenance operations to minimize makespan. A mixed-integer linear programming (MILP) is to mathematically describe the problem and heuristic procedures to incorporate maintenance operations to job scheduling are proposed. An Iterated Greedy with Variable Search Neighborhood (VNS), named IG_NM, is proposed to solve small and large instances with size of 4,800 and 13,200 problems, respectively. Computational experiments were conducted to evaluate the performance of IG_NM in comparison with MILP and the most recent methods of literature of distributed flow shop scheduling problems. Statistical results show that in the trade-off between effectiveness and efficiency the proposed IG_NM outperformed other metaheuristics of the literature.     

基于动态多处理节点的分布式系统任务调度

针对固定处理节点分布式系统动态调控能力弱的问题,给出一种分布式系统任务调度模型,讨论单处理节点任务调度问题,提出平均处理强度指标,用于更准确地刻画处理节点的承载能力。推导出动态多处理节点的任务分配方法,优化分布式系统中任务处理的时间响应特性。模拟实验证明,该算法有较好的动态调控能力,能根据需要降低处理器负载、改善任务处理延时并更合理地利用系统资源。     

分布式系统下的DAG任务调度研究综述

近年来随着网格、云计算工作流等分布式计算技术的发展,关于DAG(有向无环图)模型任务在分布式系统环境下的调度问题逐渐成为备受关注的研究热点。根据最新研究进展,对分布式系统下的DAG任务调度问题和有关技术进行了研究与讨论,主要包括四个方面:系统地描述了分布式系统和异构分布式系统的有关概念,异构分布式系统下的DAG任务调度问题、调度模型及其典型应用;对现有分布式系统下DAG任务调度的研究按照不同的方式进行了分类;探讨了多DAG共享异构分布式资源调度的研究现状;讨论了目前多DAG共享异构分布式资源调度研究存在的问题和未来可能的研究方向。     

一种用于异构环境中任务调度的高效算法

在异构计算环境中,有效的任务调度对于获得高性能是十分重要的。现在虽然已经有许多异构处理器调度算法,但它们或者不具有良好的效果,或者算法代价太高。提出了一种新的基于表的调度算法APS。APS利用有向无环图来计算任务优先级,并采用基于调度的策略分配任务到不同处理器,以获得任务最少完工时间。将APS和LMT,HEFT,CPOP算法做比较之后得出：在大多数情况下APS算法都能获得更好性能。     

Time windows and constraint programming to deal with strong restriction in the due date of productive systems

The APS (Advanced Planning and Scheduling) systems are widely used by companies; however, the traditional APS systems cannot deal with problems whose the due date is a strong restriction. The problem derives from the way companies use their scheduling heuristics. This paper addresses this problem by using the concept of time windows with the constraint programming mechanism. A procedure is shown to generate the time windows and how they can be used for the APS systems. The APS heuristic approach that uses the concepts of time windows with constraint programming is introduced to solve problems for which the due date is a strong restriction. These heuristics, with tasks allocation either at the beginning or at the end of the task time window, eliminate the need for a priority scheme. To illustrate the advantage of the proposal, some examples are presented.     

基于自适应与主动消息的任务调度策略研究与实现

在并行分布计算中，任务调度策略是影响并行分布计算性能的重要因素，结合现有任务调度法存在的问题，运用有效聚合与充分释放的思想，提出并实现了一种具有自适应和主动消息的任务调度策略，有效地提高了系统的整体性能。     

Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations

Agent-based distributed simulations are confronted with load imbalance problem, which significantly affects simulation performance. Dynamic load balancing can be effective in decreasing simulation execution time and improving simulation performance. The characteristics of multi-agent systems and time synchronization mechanisms make the traditional dynamic load balancing approaches not suitable for dynamic load balancing in agent-based distributed simulations. In this paper, an adaptive dynamic load balancing model in agent-based distributed simulations is proposed. Due to the complexity and huge time consuming for solving the model, a distributed approximate optimized scheduling algorithm with partial information (DAOSAPI) is proposed. It integrates the distributed mode, approximate optimization and agent set scheduling approach. Finally, experiments are conducted to verify the efficiency of the proposed algorithm and the simulation performance under dynamic agent scheduling. The experiments indicate that DAOSPI has the advantage of short execution time in large-scale agent scheduling, and the distributed simulation performance under this dynamic agent scheduling outperforms that under static random agent distribution.     

Multi-agent Pareto appointment exchanging in hospital patient scheduling

We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment exchanging algorithm: MPAEX. It respects the decentralization of scheduling authorities and continuously improves patient schedules in response to the dynamic environment. We present models of the hospital patient scheduling problem in terms of the health care cycle where a doctor repeatedly orders sets of activities to diagnose and/or treat a patient. We introduce the Theil index to the health care domain to characterize different hospital patient scheduling problems in terms of the degree of relative workload inequality between required resources. In experiments that simulate a broad range of hospital patient scheduling problems, we extensively compare the performance of MPAEX to a set of scheduling benchmarks. The distributed and dynamic MPAEX performs almost as good as the best centralized and static scheduling heuristic, and is robust for variations in the model settings. A preliminary version of this work has appeared as [1].     

A holonic approach to dynamic manufacturing scheduling

Manufacturing scheduling is a complex combinatorial problem, particularly in distributed and dynamic environments. This paper presents a holonic approach to manufacturing scheduling, where the scheduling functions are distributed by several entities, combining their calculation power and local optimization capability. In this scheduling and control approach, the objective is to achieve fast and dynamic re-scheduling using a scheduling mechanism that evolves dynamically to combine centralized and distributed strategies, improving its responsiveness to emergence, instead of the complex and optimized scheduling algorithms found in traditional approaches.     

基于混合粒子群算法的网格任务调度

减少分布式程序的执行时间是网格调度系统需要解决的重要问题。因分布式程序常建模为DAG图,故该问题又称异构DAG调度问题。在研究网格环境下的任务调度的基础上,提出了一种用于解决DAG任务调度问题的通用混合粒子群优化算法(Common Hybrid Particle Swarm Optimization),简称为CHPSO。该算法将问题的解(粒子)表示为任务的调度优先权向量,采用混合粒子群优化算法探索解空间。实验结果表明,在求解不含孤立点的单个DAG调度问题时,该算法所得解的调度长度仅为HEFT的90%～92%,求解质量与PSGA相当;在多张DAG图(含孤立节点)并发执行的网格环境中,该算法的调度性能明显优于PSGA及文中列出的其它演化计算方法。