This paper concerns with a multi-echelon inventory/distribution system considering one-warehouse and N-retailers. The retailers are replenished from the warehouse. We assume that the demand rate at each retailer is known. The problem consists of determining the optimal reorder policy which minimizes the overall cost, that is, the sum of the holding and replenishment costs. Shortages are not allowed and lead times are negligible. We study two situations: when the retailers make decisions independently and when the retailers are branches of the same firm. Solution methods to determine near-optimal policies in both cases are provided. Computational results on several randomly generated problems are reported.

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We deal with a multi-echelon inventory system in which one warehouse supplies an item to multiple retailers. Customer demand arrives at each retailer at a constant rate. The retailers replenish their inventories from the warehouse which in turn orders from an outside supplier. It is assumed that shortages are not allowed and lead times are negligible. These distribution/inventory systems are encountered frequently in practice and have been extensively analyzed in the literature. A survey of related works can be found in

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Academic performance during the first year of university is correlated with future academic success, and is considered to be a determining factor in the reduction of dropouts. In the present study, we describe a new academic performance indicator for the first term of first‐year science degrees students at La Laguna University in Spain. We are interested in identifying the most important previous academic factors for predicting the success of first‐year students at university. Specifically, multiple linear regression models were used to identify such predictors of academic success. We report that, for all of the analyzed science degrees, the best predictor of academic success is high school grade point average. In addition, we obtained predictive models for estimating the value of the new academic performance indicator. Using these models, we can predict future academic success, which may help identify students at risk of failure at the beginning of the course. This in turn would ensure early implementation of educational interventions or strategies to increase academic achievement of such students.