Feature network-driven quadrant mapping for summarizing customer reviews

With the rapid growth of e-commerce, customers increasingly write online reviews of the product they purchase. These customer reviews are one of the most valuable sources of information affecting selection of products or services. Summarizing these customer reviews is becoming an interesting area of research, inspiring researchers to develop a more condensed, concise summarization for users. However, most of the current efforts at summarization are based on general product features without feature’s relationship. As a result, these summaries either ignore feedback from customers or do a poor job of reflecting the opinions expressed in customer reviews. To remedy this summarization shortcoming, we propose a feature network-driven quadrant mapping that captures and incorporates opinions from customer reviews. Our focus is on construction of a feature network, which is based on co-occurrence and sematic similarities, and a quadrant display showing the opinions polarity of feature groups. Moreover, the proposed approach involves clustering similar product features, and thus, it is different from standard text summarization based on abstraction and extraction. The summarized results can help customers better understand the overall opinions about a product.     

Optimal bundling and pricing decisions for complementary products in a two-layer supply chain

This article explores bundling and pricing decisions for two complementary products in a two-layer supply chain consisting of a multi-product manufacturer and a retailer. We establish four different pricing models under cases of decentralized decision, while considering different portfolio-bundling strategies of the manufacturer and the retailer. A game-theoretical method is used to characterize the corresponding equilibrium outcomes in each scenario. By further analyzing and comparing the maximum profits of all four possible scenarios, optimal bundling and pricing decisions for the manufacturer and the retailer are obtained, respectively. Model extensions and numerical examples are enriched to highlight the factors affecting optimal decision-making. Finally, valuable and interesting managerial insights are summarized. Results show that the upstream manufacturer always profits more when he sells complementary products separately. However, the optimal bundling decision of the downstream retailer is jointly determined by product complementarity (as a major factor) and the difference of product profitability (as a secondary factor). Market power cannot exert an influence on both optimal bundling decisions, but it can partly affect pricing decisions.     

Which is the use of systems theory in literature analysis?: The discovery of the place in La Mancha in Don Quixote

The use of systems theory to attempt to determine which ''place in La Mancha'' was the one whose name Cervantes could not quite recall in his universal novel, appears to be ordained to change certain attitudes towards science held in Cervantine literature. The reason: after four centuries of literary analysis, systemic methodology has proven able to identify the famous ''place'' with acceptable accuracy. Nonetheless, certain reasonable doubts persist around the suitability of a strictly scientific analysis of literature. In light of those doubts, the present article aims primarily to facilitate critique both of the systemic approach adopted and its outcome.     

A partial robust optimization approach to inventory management for the offline-to-online problem under different selling prices

This study examines an optimal inventory strategy when a retailer markets a product at different selling prices through a dual-channel supply chain, comprising an online channel and an offline channel. Using the operating pattern of the offline-to-online (O2O) business model, we develop a partial robust optimization (PRO) model. Then, we provide a closed-form solution when only the mean and standard deviation of the online channel demand distribution is known and the offline channel demand follows a uniform distribution (partial robust). Specifically, owing to the good structural properties of the solution, we obtain a heuristic ordering formula for the general distribution case (i.e., the offline channel demand follows a general distribution). In addition, a series of numerical experiments prove the rationality of our conjecture. Moreover, after comparing our solution with other possible policies, we conclude that the PRO approach improves the performance of incorporating the internet into an existing supply chain and, thus, is able to adjust the level of conservativeness of the solution. Finally, in a degenerated situation, we compare our PRO approach with a combination of information approach. The results show that the PRO approach has more “robust” performance. As a result, a reasonable trade-off between robustness and performance is achieved.     

Knowledge-based bridge detection from SAR images

Automatic bridge detection is an important application of SAR images. Differed from the classical CFAR method, a new knowledge-based bridge detection approach is proposed. The method not only uses the backscattering intensity difference between targets and background but also applies the contextual information and spatial relationship between objects. According to bridges’ special characteristics and scattering properties in SAR images, the new knowledge-based method includes three processes: river segmentation, potential bridge areas detection and bridge discrimination. The application to AIRSAR data shows that the new method is not sensitive to rivers’ shape. Moreover, this method can detect bridges successfully when river segmentation is not very exact and is more robust than the radius projection method.     

GROUP SIZE AND PARALLELISM EFFECTS IN TASKS WITHHETEROGENEOUS LEVELS OF DIFFICULTY：A STOCHASTIC ORDER APPROACH

We consider tasks where in order to perform them it is sufficient that one member of a group will know how to do it. We are interested in the effect of task difficulty, and variability of that difficulty, on group performance, and in particular on the marginal contribution of an additional number to the performance of groups of different size. We explore the implications of various stochastic orders over task difficulty and variability. Some intuitive conjectures are shown to be false.     

GROUP-BUYING ONLINE AUCTION AND OPTIMAL INVENTORY POLICY IN UNCERTAIN MARKET

In this paper we consider a group-buying online auction (GBA) model for a monopolistic manufacturer selling novel products in the uncertain market. Firstly, we introduce the bidder's dominant strategy, after which we optimize the GBA price curve and the production volume together. Finally, we compare the GBA with the traditional posted pricing mechanism and find that the GBA is highly probable to be advantageous over the posted pricing mechanism in some appropriate market environments.     

AUTOMATIC DECISION-MAKING： NEURAL FIRING AND RESPONSE

1. Introduction This is the fourth exposition on theAnalytic Hierarchy Process. It is true thatmany decisions we make subconsciously ariseout of our physical and biological needs andderive from the pressure to survive physically.We also make subconsciou…     

THE PERIODIC CAPACITATED ARC ROUTING PROBLEM LINEAR PROGRAMMING MODEL, METAHEURISTIC AND LOWER BOUNDS

1. Introduction The Capacitated Arc Routing Problem(CARP) is defined on an undirected network inwhich a fleet of identical vehicles with limitedcapacity is based at a depot node. Each edge hasa non-negative traversal cost and can betraversed any number…