Line balancing in a just-in-time production environment: balancing multiple U-lines

The introduction of multiple, independent production lines has helped many firms to increase their production flexibility, provide for redundancy when equipment breaks down, reduce idle time and labor costs, and achieve many other benefits. This paper introduces and formalizes the multiple U-line balancing problem. Optimal solution methodologies are provided for Type I (minimize the number of stations for a given cycle time), Type II (minimize the cycle time for a given number of stations), and cost-minimization line-balancing problems. A branch-and-bound algorithm is also developed for the situation in which equipment requirements are dependent on the line configuration and the task assignment to stations. Computational results indicate that the greatest benefit of exploiting multiple lines occurs for smaller cycle-time problems that require higher output.     

Semi-automated user agents in distributed change management

Flexible integration of distributed design and manufacturing activities is one of the key issues in applying just-in-time principles along the logistics chain. The potential of AI based approaches in this field is clear, but the inherent asynchrony, concurrency, and inconsistency of distributed operations has reduced enthusiasm so far.This paper discusses coordination in the context of geographically distributed manufacturing of one-of-a-kind products. Cooperation of partners is based on semi-automated user agents which operate as an integrative layer between more dedicated tools and systems. The management of order changes in elevator manufacture is used as a case example.The work reported here is part of the DIMUN project (DIMUN, 1989) in the RACE program.     

即时定向新闻采集技术研究

互联网的迅速发展带动了信息量的爆炸性增加。如何更快地采集所需信息一直是国内外研究和开发的热点。近年来,不断增长的对特定信息(例如特定领域的新闻)的需求要求有针对性地从指定的网站即时采集相关信息。这些新闻一般具有不可预见性、更新频率较快、时效性强等特点。这要求我们必须能针对这些特点实现即时定向的采集。本文提出了一种有效抓取网页并进行分析的方法,实践表明取得了满意的效果。     

Implementing just-in-time philosophy to reverse logistics systems: a review

Just-in-time (JIT) and reverse logistics are two important philosophies. Coincidentally, both are related to reducing the impact on the environment. However, they are sometimes in conflict with each other. The former focuses on moving the materials smoothly which require a stable demand and supply, but the latter is weak in terms of predicting how many returned products will be processed. Therefore, there is a need to investigate the impact of JIT to reverse logistics systems. After an extensive literature review based on a model developed for this purpose, different functions, namely reverse logistics structure, process model, product life cycle, information system model, and JIT performance, have been identified that have great impact on integrating JIT with reverse logistics. In short, a process model with JIT can have a better control on cost and efficiency of reverse logistics activities, and product life cycle management with JIT helps to design the product that consider the reverse activities in various process models. An information system can support both process model and product life cycle management for JIT reverse logistics. Details are presented in this paper.     

iViewer:利用数据服务即时生成跨域数据视图

越来越多的企业和组织选择通过广域、开放的互联网作为其协作平台,业务决策者往往需要即时汇聚并综合分析来自多个部门的资源信息以进行临机决策。如何即时构建满足用户需求的跨组织数据视图,动态维护视图和数据源之间的一致性是需要求解的一个关键问题。提出了一种互联网环境下跨组织业务数据视图的动态生成方法iViewer,利用数据服务来封装自治、异构和动态变化的数据源;通过可视化和易用的数据服务组合操作来动态构建数据视图;提出了一种基于轮询的视图动态更新算法,维护数据源和数据视图的一致性,从而使得数据视图能够随数据源的变化而自主变化;详述了iViewer方法的原理和过程,并通过一个火灾应急处置场景中,面向指挥中心的跨部门火灾救援设备数据视图的动态生成过程例证了iViewer方法的效果。     

中小型钢铁企业生产物流分段管理模式及其应用

分析了中小型钢铁企业生产物流管理存在的普遍问题和生产物流的特点,建立以CPFR思想指导的生产物流管理规范和例外事件处理机制。应用JIT和MRPⅡ二种现代生产管理理论,构建了一套针对中小钢铁企业的生产物流推-拉分段管理模式:以MRPⅡ为主线,采用推式生产模式,进行连续生产阶段的生产计划的制定和生产物流控制;以JIT为主线,采用拉式生产模式,进行离散生产阶段的生产物流控制。X集团应用效果表明了该模式对生产物流相关指标改善的有效性。     

Rolling Kanban: a new visual tool to schedule family batch manufacturing processes with kanban

Rolling Kanban is the name of a kanban management methodology for batch processes manufacturing environment. Proposed in the early 2000s by FESTO Consulting, for two decades we have lost knowledge both from operative and scientific point of view. Basically, Rolling Kanban means a visual planning methodology based on the production of product-families and variants where: (i) set-up times are reduced between the products of the same family, and (ii) relevant times (dozens of minutes if not even hours) must be considered for changeover between products of different families. In addition, the cyclic production sequence between different product families cannot be maintained. Considering that very few technical information and documents are available about this approach, the main objective of this paper is to retrieve and present, for the first time to the scientific community, the Rolling Kanban methodology. Besides, a real industrial implementation concerning a manufacturer of domestic fittings is discussed as a case study. More specifically, two novel versions of the original Rolling Kanban technique are fully presented to effectively overcome certain limits and criticalities found during its operative use, such as the difficulty to realise a pull production, considering increased set-up time for changeover between products of different families.     

Just-in-time learning for the prediction of oil sands ore characteristics using GPS data in mining applications

For the mining based oilsands industry, it is desirable to determine the quality of the ore delivered to the extraction processes in real-time to make optimal operational decisions such as optimal ore blending to achieve maximal bitumen recovery. Currently, the industry determines the real-time ore characteristics for any given shovel Global Positioning System (GPS) location by first determining the shovel elevation from the topological mine map and then using the determined geological coordinates in the 3D geological block model. It should be noted that the block model is built based on the widely spaced core hole samples, and it is updated only on a yearly basis due to high cost of narrower core hole sampling. Thus, the block model predictions are often inaccurate in between the core hole spacing. On the other hand, mining operations data are available that contain accurate ore characteristics information in the already mined area. Therefore, in this work, we present a just-in-time based data-driven modelling strategy that utilizes the recently available mining operations data to obtain reliable ore characteristics given the GPS data. The prediction capability of ore characteristics using the proposed modelling strategy is validated at core hole locations. Further, the prediction of ore characteristics at non-core hole points demonstrate promising results.     

基于即时学习算法的铸坯质量预测方法

传统铸坯质量预测大多基于全局建模方法,存在自适应能力较差、预测精度不稳定等不足。为此,提出了一种基于即时学习算法的铸坯质量预测方法,主要特征为构建基于即时学习算法的局部模型,以取代传统全局模型,实现建模和预测同时在线进行,更适应于场景复杂、工况多变的连铸生产过程;根据连铸生产数据的时变性特点,在相似度计算中引入时间权重因子,强化样本数据与待测数据的相关性,更有利于提高铸坯质量预测模型精度。以国内某钢厂65号高碳钢铸坯三角区裂纹为例,具体说明即时学习算法在铸坯质量预测局部模型构建中的应用,并与铸坯质量预测全局模型的预测结果进行对比分析及评价。结果表明,基于即时学习算法的局部模型在评价指标上均优于全局模型,全局模型的预测准确率为65%,基于即时学习算法的局部模型准确率提升至90%,进一步阐明基于即时学习算法的局部模型用于铸坯质量预测的有效性。