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. 相似文献
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. 相似文献
