The Multi-Location Transshipment Problem with Positive Replenishment Lead Times

Transshipments, monitored movements of material at the same echelon of a supply chain, represent an effective pooling mechanism. With a single exception, research on transshipments overlooks replenishment lead times. The only approach for two-location inventory systems with non-negligible lead times could not be generalized to a multi-location setting, and the proposed heuristic method cannot guarantee to provide optimal solutions. This paper uses simulation optimization by combining an LP/network flow formulation with infinitesimal perturbation analysis to examine the multi-location transshipment problem with positive replenishment lead times, and demonstrates the computation of the optimal base stock quantities through sample path optimization. From a methodological perspective, this paper deploys an elegant duality-based gradient computation method to improve computational efficiency. In test problems, our algorithm was also able to achieve better objective values than an existing algorithm.Transshipment;Infinitesimal Perturbation Analysis (IPA);Simulation Optimization

Compression of Deep Neural Networks on the Fly

Thanks to their state-of-the-art performance, deep neural networks are increasingly used for object recognition. To achieve these results, they use millions of parameters to be trained. However, when targeting embedded applications the size of these models becomes problematic. As a consequence, their usage on smartphones or other resource limited devices is prohibited. In this paper we introduce a novel compression method for deep neural networks that is performed during the learning phase. It consists in adding an extra regularization term to the cost function of fully-connected layers. We combine this method with Product Quantization (PQ) of the trained weights for higher savings in storage consumption. We evaluate our method on two data sets (MNIST and CIFAR10), on which we achieve significantly larger compression rates than state-of-the-art methods

Constraints on a new alternative model to dark energy

The recent type Ia supernova data suggest that the universe is accelerating now and decelerated in recent past. This may provide the evidence that the standard Friedmann equation needs to be modified. We analyze in detail a new model in the context of modified Friedmann equation using the supernova data published by the High-$z$ Supernova Search Team and the Supernova Cosmology Project. The new model explains recent acceleration and past deceleration. Furthermore, the new model also gives a decelerated universe in the future.Comment: 12 pages, 5 figures, use ws-ijmpd, minor changes made. In the new version, a detailed derivation of the model is give

Transductive Multi-View Zero-Shot Learning

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