Dual hesitant fuzzy information aggregation with Einstein t-conorm and t-norm

Dual hesitant fuzzy set (DHFS) is a new generalization of fuzzy set (FS) consisting of two parts (i.e., the membership hesitancy function and the non-membership hesitancy function), which confronts several different possible values indicating the epistemic degrees whether certainty or uncertainty. It encompasses fuzzy set (FS), intuitionistic fuzzy set (IFS), and hesitant fuzzy set (HFS) so that it can handle uncertain information more flexibly in the process of decision making. In this paper, we propose some new operations on dual hesitant fuzzy sets based on Einstein t-conorm and t-norm, study their properties and relationships and then give some dual hesitant fuzzy aggregation operators, which can be considered as the generalizations of some existing ones under fuzzy, intuitionistic fuzzy and hesitant fuzzy environments. Finally, a decision making algorithm under dual hesitant fuzzy environment is given based on the proposed aggregation operators and a numerical example is used to demonstrate the effectiveness of the method.     

Risk reduction in a project portfolio

The positive impacts of managing projects as a portfolio are quantified by comparing the value of the integrated risk of a project portfolio and the aggregation of single project risks implemented separately. Firstly, the integrated risk is defined by proposing risky events based on set theory. Secondly, as projects interact with each other in a project portfolio, the integrated risk is evaluated by using a Bayesian network structure learning algorithm to construct an interdependent network of risks. Finally, the integrated risk of a practical case is assessed using this method, and the results show that the proposed method is an effective tool for calculating the extent of risk reduction of implementing a project portfolio and identifying the most risky project, so as to assist companies in making comprehensive decisions in the phase of portfolio selection and portfolio controlling.     

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.     

Vertical handoff algorithm in heterogeneous networks to maximize system benefit

A new vertical handoff decision algorithm is proposed to maximize the system benefit in heterogeneous wireless networks which comprise cellular networks and wireless local area networks (WLANs). Firstly the block probability, the drop probability and the number of users in the heterogeneous networks are calculated in the channel-guard call admission method, and a function of the system benefit which is based on the new call arrival rate and the handoff call arrival rate is proposed. Then the optimal radius of WLAN is obtained by using simulation annealing (SA) method to maximize the benefit. All the nodes should handoff from cellular network to WLAN if they enter WLAN’s scope and handoff fromWLAN to cellular network if they leave the scope. Finally, the algorithm in different new call arrival rates and handoff call arrival rates is analyzed and results show that it can achieve good effects.     

Automatic patent document summarization for collaborative knowledge systems and services

Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R&D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R&D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification (IPC) codes B25C, B25D, B25F (categories for power hand tools) and B24B, C09G and H011 (categories for chemical mechanical polishing) are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.