Multi-echelon inventory optimization for fresh produce

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 69).For fresh produce, the product freshness is a key value to end consumers. Retailers try to maximize product freshness at retail stores while maintaining high product availability. Fresh produce that is close to the end of its life cycle will either be scrapped or be sold at a much lower price. With an increasing demand volatility and complication of supply chain network, obsolescence cost from these spoilages has been increasing recently. Our research focuses on the study of multi-echelon inventory optimization for fresh produce. We investigated the impacts of an additional fulfillment center in a supply chain to justify an improvement in product freshness. We analyzed three relevant factors: transit time, inventory dwell time and safety time, which affect the time products spend in a supply chain from the suppliers to the retail stores. Our objective was to create a predictive model that could determine whether product freshness could be improved when those products are shipped through a supply chain network with an additional fulfillment center. While a fulfillment center increases the total transit time by adding more "touches" of the inventory, it can provide benefits by reducing demand variability through the risk pooling effect. When an fulfillment center aggregates demand from several grocery distribution centers, it pools the demand volatility across various locations, thus reducing the demand volatility and the safety stock. Our model demonstrated that, with a fulfillment center, six product categories (Berries, Watermelons, Cherries, Mixed melons, Stone fruit, and Strawberries) had a decrease in the safety time that is more than the increase in total transit time, resulting in the improved product freshness at retail stores. Further, we defined a term "Enhance Coefficient of Variation (ECV)" to quantify the demand volatility. Finally, we determined a set of minimum ECV ratios in order to make an fulfillment center benefits the product freshness under different replenishment frequencies. Retailers can use this ECV ratio as an indicator to make channeling decisions.by Saran Limvorasak and Zhiheng Xu.M.Eng.in Logistic

Identity based proxy re-encryption scheme (IBPRE+) for secure cloud data sharing

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In proxy re-encryption (PRE), a proxy with re-encryption keys can transfer aciphertext computed under Alice's public key into a new one, which can be decrypted by Bob only with his secret key. Recently, Wang et al. introduced the concept of PRE plus (PRE+) scheme, which can be seen as the dual of PRE, and is almost the same as PRE scheme except that the re-encryption keys are generated by the encrypter. Compared to PRE, PRE+ scheme can easily achieve two important properties: first, the message-level based fine-grained delegation and, second, the non-transferable property. In this paper, we extend the concept of PRE+ to the identity based setting. We propose a concrete IBPRE+ scheme based on 3-linear map and roughly discuss its properties. We also demonstrate potential application of this new primitive to secure cloud data sharing.Peer ReviewedPostprint (author's final draft

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, we apply the m-RNN model to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval. The project page of this work is: www.stat.ucla.edu/~junhua.mao/m-RNN.html .Comment: Add a simple strategy to boost the performance of image captioning task significantly. More details are shown in Section 8 of the paper. The code and related data are available at https://github.com/mjhucla/mRNN-CR ;. arXiv admin note: substantial text overlap with arXiv:1410.109

Automatic Generation of Hierarchical Contracts for Resilience in Cyber-Physical Systems

With the growing scale of Cyber-Physical Systems (CPSs), it is challenging to maintain their stability under all operating conditions. How to reduce the downtime and locate the failures becomes a core issue in system design. In this paper, we employ a hierarchical contract-based resilience framework to guarantee the stability of CPS. In this framework, we use Assume Guarantee (A-G) contracts to monitor the non-functional properties of individual components (e.g., power and latency), and hierarchically compose such contracts to deduce information about faults at the system level. The hierarchical contracts enable rapid fault detection in large-scale CPS. However, due to the vast number of components in CPS, manually designing numerous contracts and the hierarchy becomes challenging. To address this issue, we propose a technique to automatically decompose a root contract into multiple lower-level contracts depending on I/O dependencies between components. We then formulate a multi-objective optimization problem to search the optimal parameters of each lower-level contract. This enables automatic contract refinement taking into consideration the communication overhead between components. Finally, we use a case study from the manufacturing domain to experimentally demonstrate the benefits of the proposed framework.Comment: \copyright 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work