马克思的机器思想及其对认识互联网的启示

马克思的《资本论》及其手稿中包含了丰富的机器思想,内容涉及机器的性质,机器对生产方式、资产阶级政权、家庭和社会的影响以及机器的发展前途.在产生和影响上,马克思所处时代的机器与今天的互联网具有高度的相似性,这决定了《资本论》中的机器思想对深刻认识互联网以及利用互联网发展生产力、化解网络政治经济问题、引导家庭和社会适应网络社会等方面具有重要的启示.     

Optimal Dynamic Order Scheduling under Capacity Constraints Given Demand‐Forecast Evolution

We consider a manufacturer without any frozen periods in production schedules so that it can dynamically update the schedules as the demand forecast evolves over time until the realization of actual demand. The manufacturer has a fixed production capacity in each production period, which impacts the time to start production as well as the production schedules. We develop a dynamic optimization model to analyze the optimal production schedules under capacity constraint and demand‐forecast updating. To model the evolution of demand forecasts, we use both additive and multiplicative versions of the martingale model of forecast evolution. We first derive expressions for the optimal base stock levels for a single‐product model. We find that manufacturers located near their market bases can realize most of their potential profits (i.e., profit made when the capacity is unlimited) by building a very limited amount of capacity. For moderate demand uncertainty, we also show that it is almost impossible for manufacturers to compensate for the increase in supply–demand mismatches resulting from long delivery lead times by increasing capacity, making lead‐time reduction a better alternative than capacity expansion. We then extend the model to a multi‐product case and derive expressions for the optimal production quantities for each product given a shared capacity constraint. Using a two‐product model, we show that the manufacturer should utilize its capacity more in earlier periods when the demand for both products is more positively correlated.     

虚拟机软件在中职网络教学和实训中的应用

随着全国中等职业学校计算机技能大赛的举办,中职学校网络实训环境发生了改变,虚拟机软件在网络教学中得到了广泛的应用.通过虚拟机软件可以建立虚拟化的实训环境,模拟真实的计算机系统.使用虚拟机软件既可以克服教学资源和设备不足的困难,又可以解决实训时长不足,完成真实环境难以实现的实训任务,保证网络教学和实训的顺利进行.     

Production scheduling in SMT electronic boards assembly

This paper tackles the problem of scheduling in the assembly of SMT electronic boards. In particular, it focuses on a new scheduling method developed within the framework of an industry-university joint research project. The scheduling system aims at minimizing the makespan; this goal is achieved by reducing setup times and idle times of the machines. As an example, the outcome of a test carried out on a production mix made up of 10 board types is presented and analyzed in a summarized form.     

采用汇文系统实现统编工作的探索

统一编目工作是促进图书馆业务工作自动化与标准化的重要技术,笔者从采用汇文系统进行统编工作的实践尝试中,探讨了统编工作中存在的一些实际问题,并提出了解决方案.     

聚酯聚合反应分层温度控制系统

介绍了聚合釜聚酯聚合反应温度过程控制系统及其设计方法。控制系统以高性能单片机系统为直控机（DDC）,PC机为上位机（MIS）,直控机负责聚合釜釜温及夹套温度采集、分析、控制,并将参数送上位机。上位机负责控制过程的时间及温度值设定、温度监测、工作过程监控。     

纸机传动控制系统的配置与控制原理

针对纸机日益高速化、大型化的发展趋势，设计了国内先进的全数字化通信网络传动控制系统。系统采用西门子全集成自动化解决方案，实现造纸生产过程的全线集成控制。高可靠的工业现场总线PROFIBUS—DP和工业以太网相结合的网络结构，有效地实现了数据共享，全面解决了整个工艺过程的控制。文章详细介绍了控制系统的配置和功能，重点阐述了速度链、负荷分配控制、张力控制等主要控制原理。     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

Batch-Processing Scheduling with Setup Times

The problem is to minimize the total weighted completion time on a single batch-processing machine with setup times. The machine can process a batch of at most B jobs at one time, and the processing time of a batch is given by the longest processing time among the jobs in the batch. The setup time of a batch is given by the largest setup time among the jobs in the batch. This batch-processing problem reduces to the ordinary uni-processor scheduling problem when B = 1. In this paper we focus on the extreme case of B = +, i.e. a batch can contain any number of jobs. We present in this paper a polynomial-time approximation algorithm for the problem with a performance guarantee of 2. We further show that a special case of the problem can be solved in polynomial time.