Joint Initial Stocking and Transshipment——Asymptotics and Bounds

该论文针对许多公司在供应链中面临的各分销中心周期性期初订货、期间可通过转运来再匹配各分销中心的供需不平衡问题，通过随机动态规划建模，刻画最优订货和转运策略，并证明最优订货和转运策略对总利润的贡献分别为T 和根号T 的量级，该结论的管理启示是期初订货决策的贡献比再分配重要得多，特别是对大批量、快消品行业。【Abstract】A common problem faced by many firms in their supply chains can be abstracted as follows. Periodically, or at the beginning of some selling season, the firm needs to distribute finished goods to a set of stocking locations, which, in turn, supply customer demands. Over the selling season, if and when there is a supply-demand mismatch somewhere, a re-distribution or transshipment will be needed. Hence, there are two decisions involved: the one-time stocking decision at the beginning of the season and the supply/transshipment decision throughout the season. Applying a stochastic dynamic programming formulation to a two-location model with compound Poisson demand processes, we identify the optimal supply/transshipment policy and show that the optimal initial stocking quantities can be obtained via maximizing a concave function whereas the contribution of transshipment is of order square-root-of T. Hence, in the context of high-volume, fast-moving products, the initial stocking quantity decision is a much more important contributor to the overall profit. The bounds also lead to a heuristic policy, which exhibits excellent performance in our numerical study; and we further prove both the bounds and the heuristic policy are asymptotically optimal when T approaches infinity. Extension to multiple locations is also discussed

A Comparison of Personality Prediction Classifiers for Personnel Selection in Organizations Based on Industry 4.0

Nowadays, the internet has an astonishing amount of useful material for personality mining; nevertheless, many companies fail to exploit the information and screen job candidates using personality tests that fail to grasp the very information they are trying to gather. This research aims to highlight and compare the different machine learning classifiers that can be used to predict the personality of a Spanish-speaking job applicant based on the written content posted on their social networks. The authors conduct experiments considering the most critical measures (such as accuracy, precision, and recall) to evaluate the classification performance. The results show that the random-forest classifier outperforms the other classifiers. It is of utmost importance to correctly assess the resumes to determine the most qualified people in a smart manufacturing position