Online double auction mechanism for perishable goods

One-sided auctions are used for market clearing in the spot markets for perishable goods because production cost in spot markets is already “sunk.” Moreover, the promptness and simplicity of one-sided auctions are beneficial for trading in perishable goods. However, sellers cannot participate in the price-making process in these auctions. A standard double auction market collects bids from traders and matches the higher bids of buyers and lower bids of sellers to find the most efficient allocation, assuming that the value of unsold items remains unchanged. Nevertheless, in the market for perishable goods, sellers suffer a loss when they fail to sell their goods, because their salvage values are lost when the goods perish. To solve this problem, we investigate the suitable design of an online double auction for perishable goods, where bids arrive dynamically with their time limits. Our market mechanism aims at improving the profitability of traders by reducing trade failures in the face of uncertainty of incoming/departing bids. We develop a heuristic market mechanism with an allocation policy that prioritizes bids of traders based on their time-criticality, and evaluate its performance experimentally using multi-agent simulation. We find out that our market mechanism realizes efficient and fair allocations among traders with approximately truthful behavior in different market situations.     

Optimization of satellite searching strategy of the non-stationary antenna

The tiny searching step length and the satellite distribution density are the major factors to influence the efficiency of the satellite finder, so a scientific and reasonable method to calculate the tiny searching step length is proposed to optimize the satellite searching strategy. The pattern clustering and BP neural network are applied to optimize the tiny searching step length. The calculated tiny searching step length is approximately equal to the theoretic value for each satellite. In application, the satellite searching results will be dynamically added to the training samples to re-train the network to improve the generalizability and the precision. Experiments validate that the optimization of the tiny searching step length can avoid the error of locating target satellite and improve the searching efficiency.     

突发事件应急管理情景分析:宏观审视与微观解构

风险社会背景下，可预见和难以预见的风险因素明显增多，对我国应急管理体系和应急管理能力的现代化提出了更高要求。情景分析是对不确定性的未来进行研究的分析方法和实践工具，与突发事件应急管理中对不确定性因素的分析具有高度契合性。以突发事件应急管理情景分析为研究对象，运用CiteSpace软件绘制知识图谱，宏观审视应急管理情景分析研究的趋势与规律；微观上从理论修正与拓展、流程调整与规范、方法应用与实践三方面解构应急管理情景分析研究的内容与热点。结果表明:突发事件应急管理情景分析尚未形成清晰的研究主线，但具有较大潜力；研究内容呈现出多元化特征，但本土化研究相对较缺乏；当前研究不能满足实践需要，重大突发事件高度复杂性倒逼情景分析研究。     

小型漂浮式天线姿态的自抗扰解耦控制

研究了小型漂浮式天线姿态跟踪的自抗扰解耦控制。分析了4级风以下的海洋环境对天线姿态的影响,设计了三轴伺服执行机构校正天线指向目标卫星的姿态偏差,研究证实三轴伺服系统是一种复杂的耦合性较强的非线性系统;设计了由横滚伺服自抗扰控制器、俯仰伺服自抗扰控制器和方位自抗扰控制器组成的天线姿态自抗扰解耦控制器用于天线姿态控制;通过MATLAB仿真研究验证了自抗扰解耦控制器的性能优于PID控制器,可以较好地估计和补偿天线的内部和外部扰动,实现天线姿态的解耦控制;通过半实物仿真研究证实了自抗扰解耦控制器可以实现天线与卫星间的高质量通信。     

动态拣选系统下订单分批问题研究

对分区拣选系统研究在线订单分批策略.基于排队论角度,采用随机过程、随机服务系统的基本理论与方法,构建串行分区下订单不分批与订单固定时间窗分批拣选的随机服务系统模型,确定要研究对比的系统效率指标.在MAT-LAB R2019a软件环境下模拟在线订单到达情况,分析系统效率指标并找出分区拣选系统的最优分批策略.仿真验证结果表...     

Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data

Scientometrics - Understanding the complex patterns in research funding plays a fundamental role in comprehensively revealing funding preferences and informing ideas for future strategic...     

基于LS-SVM的模式判别技术 在边坡稳定性分析中的应用

由于边坡外部影响因素与内部力学性质的复杂性,边坡稳定性与各影响因素间具有高度的非线性关系,很难构建出一种普适性模型来描述边坡稳定性变化的过程。SVM（支持向量机）是一种智能化的经验学习算法,在处理非线性问题中表现出极大的优势。利用支持向量机对边坡各影响因素的数据进行分析,对边坡稳定性状态与影响因素的对应关系进行经验学习,根据学习的经验知识,利用模态判别方法对边坡的稳定性状态进行判断。试验结果表明：所提出的边坡稳定性判别方法具有很好的应用效果,可以满足工程应用的实际需求。     

Overhead reduction in a distributed path management system

CEAS (cross entropy ant system) is a distributed, robust and adaptive swarm intelligence system for path management in communication networks. This paper focuses on strategies for handling the overhead in terms of processing cycles, memory storage, and number of management packets (ants) generated by CEAS when the state of the network changes.Pheromone sharing is introduced such that virtual connections with common sub-paths are sharing information and cooperate in the path finding when the paths have the same destination and the same objective function. The sharing of information reduces the required memory in each node significantly on the expense of an increase in the size of the management packets. However, the packets are still rather small. The cooperation also leads to an improvement in convergence rates which again results in reduced transmission overhead.A rate adjustment scheme is also proposed. The scheme is self-tuned and detects state changes implicitly and sets packet rates accordingly by monitoring parameter values in the management system. Rate adaptation can be done both in the network nodes and at the end-points of a virtual path. Compared to a fixed rate strategy the self-tuned strategies show a significant reduction in the number of packets generated, while maintaining the same data packet delay and service availability level. The self-tuned rate adjustment in the network nodes provides fast restoration with short path detection times, which ensures high service availability. The self-tuned ant rate in the end-points avoids flooding the network with management packets when these are not required.The performance and overhead of CEAS are compared to those of the link state routing currently in use in today’s networks. The results show that CEAS outperforms link state routing both with respect to performance and overhead when the network experiences transient link failures, while the opposite is the case with long lived failures.     

Coordinating a three-level supply chain with delay in payments and a discounted interest rate

Both researchers and practitioners recognize the importance of the interactions between financial and inventory decisions in the development of cost effective supply chains. Moreover, achieving effective coordination among the supply chain players has become a pertinent research issue. This paper considers a three-level supply chain, consisting of a capital-constrained supplier, a retailer, and a financial intermediary (bank), coordinating their decisions to minimize the total supply chain costs. Specifically, we consider a retailer managing its cash through the supplier’s bank, in return for permissible delay in payments from the supplier. The bank, benefiting from increasing its cash holdings with the retailer’s cash deposits, offers the supplier a discount on its borrowing rate. We show that the proposed coordination mechanism achieves significant cost reduction, by up to 26.2%, when compared to the non-coordinated model. We also find that, with coordination, the retailer orders in larger quantities than its economic order quantity, and that a higher return on cash for the retailer leads to a higher order quantity. Furthermore, we empirically validate our proposed coordination mechanism, by showing that banks, retailers, and suppliers have much to gain through collaboration. Thus, using COMPUSTAT datasets for the years 1950 through 2012, we determine the most important factors that affect the behavior of the retailers and suppliers in granting and receiving trade credit. Our results indicate that engaging into such a coordination mechanism is a win–win situation to all parties involved.