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.     

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.     

基于中国余数定理的目标距离估计算法

为了提高目标检测性能,脉冲多普勒(pulsed Doppler, PD)雷达常常采用高脉冲重复频率(pulsed repetition frequency, PRF)信号,以便在信号频域获得比较宽的无杂波区,但高的PRF往往引起目标的距离模糊。现有的解距离模糊算法大都面临计算量大的问题,该文紧密结合PD雷达体制的特点,在基于PD雷达参差重频模式下,提出一种最优余数的封闭式鲁棒中国余数定理(closed-form robust Chinese remainder theorem, CFRCRT)目标距离估计方法。该方法不仅可以在视在距离有误差的情况下精准地重构目标真实距离,而且具有封闭式的解析解,大大减小了运算量。理论分析和仿真实验都表明该文提出的方法在精度和实时性能上都具有一定的优越性。     

交互式多目标决策方法综述

本文在叙述了多目标决策问题的数学模型和基本概念的基础上,把交互式多目标决策方法分为四类,并对各类方法的研究情况及特点进行了讨论,最后对该领域的进一步研究提出了一些想法。     

基于Petri网下的哲学家就餐问题的模拟与验证

哲学家就餐问题可以看作当应用程序中包含并发线程的执行时,处理共享资源合作的一个有代表性的问题,该问题是评价同步方法的一个测试标准.利用Petri 网的C/E系统来模拟哲学家就餐问题具有很好的实用背景,因为C/E系统下的条件事件集的发生可以用开关或者门电路来实现.通过采用C/E系统来完善哲学家就餐问题的模型,并给出一个可以允许外部条件来加以有选择控制的模型.     

Clustering patents using non-exhaustive overlaps

Patent documents are unique external sources of information that reveal the core technology underlying new inventions.Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R&D investments.This research incorporates an ontology schema to extract and represent patent concepts.A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery.The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims,a feature that enables the grouping of patents that define related key innovations.Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation.The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification (RFID) patents and by analyzing the legal implications of automated discovery of patent infringement.     

社会责任与合作治污对企业治污效用影响

结合湖域初始排污权配置背景,从多利益相关者合作角度构建了企业治污效用凸博弈模型,给出基于减排率差异性的CSR-企业治污收益模型、基于治污技术差异性的同行业企业间合作治污的PCTC-企业治污成本分摊模型。进而以太湖流域氨氮初始排污权配置为例,在求解多主体Shapley值的基础上,结合江苏省张家港市10家纺织企业数据进行实证研究。结果表明:CSR同企业减排率之间具有显著拟线性关系与倍增响应关系,对企业收益具有正相关影响;PCTC可有效减少合作企业治污边际成本,对保障合作稳定性、促进多利益主体合作具有积极作用;推广CSR与PCTC有助于提高我国工业企业参与排污权交易积极性,对推进湖域排污权交易机制构建具有重要意义。     

基于数据融合的WLAN/MARG组合定位系统

接收信号强度（received signal strength, RSS）浮动和无线接入点缺失是制约无线局域网（wireless local area network, WLAN）定位精度的主要问题。利用智能终端已有的MARG（magnetic,angular rate,and gravity）传感器,设计了基于粒子滤波和卡尔曼滤波的数据融合算法,实现了一个低成本高精度的WLAN/MARG组合定位系统。该系统利用WLAN和MARG定位技术的互补特性,有效校正了由RSS浮动引起的定位误差和由传感器噪声引起的累积误差。室内WLAN环境下的实验结果表明,本文所提系统,相比WLAN和MARG定位系统,定位均方根误差分布减少了62%和91%,并且有效扩大了系统应用范围。