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.     

Machine gaming in Sydney clubs: characteristics of the supporting resident populations

This research provided background for surveys and interviews in later stages of a 3 part project. It aimed to identify, from secondary research, sociodemographic characteristics which tend to support registered clubs and their machine gaming activities in the Sydney Statistical Division. Using multiple methods including Pearson's Product Moment correlation, Principal Components factor analysis, and stepwise regression, the study profiled Sydney populations which spend highly on gaming machines. The most important sociodemographic predictors of Sydney statistical local areas where per capita gaming machine expenditure is high are large proportions of the adult resident population who were born in Malta, Greece, Lebanon, China, Italy, Vietnam, Yugoslavia, India or the Philippines; have no vocational or tertiary qualifications; or are unemployed.     

基于浊度检测的洗衣机废水自动回收装置的设计

为回收再利用洗衣机在洗涤过程中所排的废水，设计了洗衣机废水自动回收装置，装置由浊度传感器、液位开关、水用电磁阀、潜水泵、水箱等构成。该装置可自动识别废水污浊程度，从而决定是否回收储存起来。经简易处理的回收废水类似于q-水，可用于马桶及地面的冲洗，这对于水资源的节约利用、绿色住宅建筑设施设计具有一定的参考意义，同时也具有一定的经济效益。     

再生核支持向量机在非线性系统中的应用

为了提高非线性系统辨识的精度,提出用Walsh函数作为空间V0的尺度函数,构造出L2(R)空间的正交规范序列。结合小波多分辨分析,将Hilbert空间分为一系列子空间,并由可分Hilbert空间与L2(R)的等价性,利用内积同构的线性算子,可以把V0子空间的尺度函数折算为Hilbert空间的子空间V0的尺度函数,构造出新的Walsh序列再生核。通过仿真实验,与传统的RBF核函数、高斯核函数等比较,该尺度再生核函数具有更高的辨识精度,较少支持向量数目,充分体现了支持向量机较好的推广性能。     

浅谈机械制造工艺学之教学

机械制造工艺学课是机械类专业的专业基础课,具有较强的实践性。教师应根据这门课的特点和要求,钻研教材内容,合理安排教学,激发学生的学习积极性,提高教学质量。     

QA95—630型钢坯剪切机焊接机身的有限元分析

建立了ＱＡ９５－６３０型钢坯剪切机焊接机身的力学模型，利用有限元分析软件Ｓｕｐｅｒ－ＳＡＰ对机身进行网格划分和有限元分析，输出应力数表、位移数表、等应力线图和等位移线图等结果，以帮助设计人员对机身进行校核、改进或重新设计。     

免疫信息系统的数据安全与应用

随着社会的进步,居民生活水平的不断提高,疾病防护与儿童免疫全面信息化管理已经成为社会趋势。而对于该系统的数据安全问题就更显得十分重要,本采用双机热备份系统,对数据进行实时备份。     

基于RPi的面曝光快速成形机控制系统

为了简化面曝光快速成形机的控制系统,降低其成本,提出了一种基于树莓派( Raspberry Pi,RPi)卡片电脑的面 曝光快速成形机控制系统。系统以Raspberry Pi为控制核心,以Linux为操作系统,通过Pvthon编程控制,实现了对面曝 光快速成形机的掩模图形投影和工作台运动控制;利用接入以太网的Raspberry Pi,实现了对面曝光快速成形机的远程 登录、文件传输以及实时监控。控制系统大大减小了面曝光快速成形机的占用空间,降低了成本,且具备远程操作功能, 有利于面曝光快速成形机进入大众市场。     

胶辊数字中高机的设计

针对印刷机中普通胶辊由于胶辊扰度造成印制品厚薄不均的问题,以中高度法原理为依据,提出了将中高度曲 线方程解析成以时间为变量的函数,用单片机计算和控制中高机运动,设计了以单片机、交流伺服控制器、变频器和磨头 等构建的一个数字控制系统为核心的胶辊数字中高机。应用结果表明数字中高机可安装在普通车床上实现中高胶辊的 加工,且中高曲线和中高度可任意设置,设计简便实用,便于推广。