In this paper, a newsboy model is developed under uniformly distributed lead-time and demand that is an appropriate assumption in obtaining optimal relief inventory of humanitarian disasters. It is noteworthy that limited historical data are in hand on relief operations. Hence, analytical and approximate solutions for optimal relief order quan­tity were derived. The effect of lead-time uncertai...

In view of the limitations traditional statistical methods in dealing with multifactor and nonlinear data inadequacy classical machine learning algorithms predicting high dimensions large sample sizes, this paper proposes an operational risk prediction model based on automatic encoder convolutional neural networks. First, we use to extract features motion factors obtain feature components that ...

• Established a network equilibrium model with ride-sourcing services under travel time variability. Captured the interaction between variability, pricing strategy, and demand competitive modes. The company prefers more reliable that would produce higher profit for company. Proposed two strategies explores optimal prices services. Theoretically proved can achieve same maximum case one single OD...

Convolutional Neural Network (CNN) is a biologically inspired trainable architecture that can learn invariant features for a number of applications. In general, CNNs consist of alternating convolutional layers, non-linearity layers and feature pooling layers. In this work, a novel feature pooling method, named as mixed pooling, is proposed to regularize CNNs, which replaces the deterministic po...

Motion (change) detection is a basic preprocessing step in video processing, which has many application scenarios. One challenge that deep learning-based methods require high computation power to improve their accuracy. In this paper, we introduce novel semantic segmentation and lightweight-based network for motion detection, called Real-time Detection Network Based on Single Linear Bottleneck ...

Recurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variablelength text. RNN can utilize distributed representations of words by first converting the tokens comprising each text into vectors, which form a matrix. And this matrix includes two dimensions: the time-step ...

Pooling is one of the key elements in a convolutional neural network. It reduces feature map size, thereby enabling training with limited amount computation. The most common pooling methods are average pooling, max and stride pooling. methods, however, have disadvantage that they can perform only specified fixed functions thus expressive power. In this paper, we propose new method named univers...

This thesis studies two operations management topics in online auctions, and is di-vided into two parts.Motivated by the increasing use of ShopBots to scan Internet auctions, the firstpart of the thesis analytically examines whether or not two competing auctioneersselling the same commodity should share, or pool, some or all of their bidders. Un-der pooling, the bidding popu...