Dynamic ordering and pricing for a perishable goods supply chain

The paper studies the combined problem of pricing and ordering for a perishable product supply chain with one supplier and one retailer in a finite horizon. The lifetime of the product is two periods and demand in each period is random and price-sensitive. In each period, the supplier determines first a wholesale price and then the retailer decides an order quantity and retail prices. We show that the optimal pricing strategy for the non-fresh product depends only on its inventory, and the optimal pricing strategy and the optimal order quantity for the fresh product depend only on the wholesale price and they have a constant relation. Moreover, the game between the retailer and the supplier for finite horizon is equivalent to a one period game with only one order. Thus, the optimal policies are identical at each period. For the additive and multiplicative demands, we further obtain equations to compute the optimal strategies. All of above results are extended into the infinite horizon case and longer lifetime products. Finally, a numerical analysis is given.     

Multi-stage sequential uniform price auction mechanism for divisible goods

In this paper, we study the problem of auctioning a kind of continuous homogeneous divisible goods, and present a multi-stage sequential auction mechanism of divisible goods under uniform price. In this mechanism, the auction process is divided into multiple stages, all bidders satisfy the sequential rationality conditions, all bidders are asymmetric, every bidder is allowed to submit a nonincreasing continuous demand function in each round bidding, every bidder’s valuation to per unit of the goods is independent private information, and if a bidder does not obtain the quantity he wants in former stage auction, he still have another opportunity to obtain some quantity in the later stage auction. For this mechanism, we explore the formation process and composition of equilibrium points in each stage auction, and discuss the optimal strategy selection for bidders, then give the detail steps and an application example to show how to implement this multi-stage sequential auction mechanism. The conclusions can be drawn that our auction mechanism is an efficient incentive mechanism, and can effectively reduce many underpricing equilibrium and can yield higher expected income than a single uniform price auction for the auctioneer. It also can avoid the occurrence of failure auction (equilibrium does not exist), and effectively extend and improve the traditional single stage uniform price auction mechanism.     

Considering lost sale in inventory routing problems for perishable goods

This paper presents a mathematical model for an inventory routing problem (IRP). The model is especially designed for allocating the stock of perishable goods. It is assumed that the age of the perishable inventory has a negative impact on the demand of end customers and a percentage of the demand is considered as lost sale. The proposed model balances the transportation cost, the cost of inventory holding and lost sale. In addition to the usual inventory routing constraints, we consider the cost of lost sale as a linear or an exponential function of the inventory age. The proposed model is solved to optimality for small instances and is used to obtain lower bounds for larger instances. We have also devised an efficient meta-heuristic algorithm to find good solutions for this class of problems based on Simulated Annealing (SA) and Tabu Search (TS). Computational results indicate that, for small problems, the average optimality gaps are less than 10.9% and 13.4% using linear and exponential lost sale functions, respectively. Furthermore, we show that the optimality gaps found by CPLEX grow exponentially with the problem size while those obtained by the proposed meta-heuristic algorithm increase linearly.     

基于间隔型双边拍卖的网格资源预留算法

针对已有基于竞价拍卖的网格资源预留方法存在的问题，提出了一种基于间隔型双边拍卖的网格资源预留算法。根据网格资源预留的需求和拍卖机制的特点，给出了网格系统模型；基于间隔型双边拍卖的思想，设计了适合网格系统的资源拍卖机制，给出了详细的网格资源预留算法；算法的最优性分析表明：在资源拍卖过程，提供真实信息对用户方和资源方均是唯一占优策略，且算法能够保证中标资源供需平衡。 最后，模拟实验结果证明了算法的有效性和正确性。     

基于agent的连续双向拍卖人工股市建模研究

我国股票市场是连续双向拍卖的订单驱动市场,而在连续双向拍卖人工股市的仿真研究中,目前发现的仿真模型均是在Swarm中实现的[1],用Java实现的仿真模型尚未发现。对仿真得到的数据进行了分析,仿真数据表明,真实股票市场中的一些典型特征都可在本文模型的仿真过程出现。     

基于风险策略的多单元连续双向拍卖的网格资源分配

针对计算网格资源的特点,提出一种基于风险策略的多单元连续双向拍卖的网格资源分配机制,实现对网格资源灵活有效的管理。首先,介绍了基于多单元连续双拍卖的网格资源分配框架。其次,针对计算网格资源的有限性,提出了RB2-MCDA机制。RB2-MCDA机制是在多单元连续双向拍卖中,代理采用Risk-Based2策略进行资源交易。Risk-Based2策略是一种基于风险行为的代理策略。实验结果表明,在不同规模的有限资源的计算网格中采用RB2-MCDA机制能够实现较高的资源分配效率,当资源需求量接近供给量时,分配效率超过99%。     

Detecting online auction shilling frauds using supervised learning

Online auction sites are a target for fraud due to their anonymity, number of potential targets and low likelihood of identification. Researchers have developed methods for identifying fraud. However, these methods must be individually tailored for each type of fraud, since each differs in the characteristics important for their identification. Using supervised learning methods, it is possible to produce classifiers for specific types of fraud by providing a dataset where instances with behaviours of interest are assigned to a separate class. However this requires multiple labelled datasets: one for each fraud type of interest. It is difficult to use real-world datasets for this purpose since they are difficult to label, often limited in size, and contain zero or multiple suspicious behaviours that may or may not be under investigation.The aims of this work are to: (1) demonstrate the approach of using supervised learning together with a validated synthetic data generator to create fraud detection models that are experimentally more accurate than existing methods and that is effective over real data, and (2) to evaluate a set of features for use in general fraud detection is shown to further improve the performance of the created detection models.The approach is as follows: the data generator is an agent-based simulation modelled on users in commercial online auction data. The simulation is extended using fraud agents which model a known type of online auction fraud called competitive shilling. These agents are added to the simulation to produce the synthetic datasets. Features extracted from this data are used as training data for supervised learning. Using this approach, we optimise an existing fraud detection algorithm, and produce classifiers capable of detecting shilling fraud.Experimental results with synthetic data show the new models have significant improvements in detection accuracy. Results with commercial data show the models identify users with suspicious behaviour.     

面向资源最优分配的深度学习双边拍卖算法

针对拍卖过程中计算效率低和利益分配不合理等问题, 本文提出了一种基于深度学习的迭代双边拍卖算法. 该算法通过买卖双方的初始报价数据训练基于神经网络的资源最优分配模型, 调用训练好的模型对实时报价数据快速响应, 直接求解经纪人最优分配问题(BAP)以实现计算资源分配, 显著地减小了计算代价, 提高了算法的计算效率. 进一步, 针对利益分配不合理等问题, 在迭代双边拍卖框架的支出规则和收入规则中引入调节因子用于调节买卖双方的利益, 解决已有算法在实现社会福利最大化过程中利益分配不合理的问题. 实验结果验证了该算法的有效性和优越性, 在运行时间、社会福利、买家利益、卖家利益和经纪人利益等多项指标均明显优于已有的迭代双边拍卖算法     

On the effects of competition between agent-based double auction markets

Real market institutions, stock and commodity exchanges for example, do not occur in isolation. The same stocks and commodities may be listed on multiple exchanges, and traders who want to deal in those goods have a choice of markets in which to trade. While there has been extensive research into agent-based trading in individual markets, there is little work on this kind of multiple market scenario. Our work seeks to address this imbalance in the context of double auction markets. This paper examines how standard economic measurements, like allocative efficiency, are affected by the presence of multiple markets for the same goods, especially when the markets are competing for traders. We find that while dividing traders between several small markets typically leads to lower efficiency and worse convergence than grouping them into one large market, competition between markets for traders, can reduce these losses.     

基于双向拍卖的流媒体资源交易模型

为有效降低视频服务商成本开销和提高服务质量,研究当前业界流行的资源交易模型,构建一种适用于流媒体资源交易的拍卖模型(SMRTM).由于模型中资源组合交易问题为NP-hard问题,计算复杂度高,通过把多种资源加权为综合满意度,将资源组合交易问题转化为图论中求解二分图最大权匹配问题,基于二分图设计一种资源组合交易算法(RP...     

一种基于双边拍卖的复制优化策略

提出了基于连续双边拍卖协议的复制优化策略,解决数据网格中复制优化策略难以适应数据高度自治性和动态性的问题。首先给出了双边拍卖机制的实现模型,接着给出了分布式的连续双边拍卖协议算法和基于零智慧增强学习的投标策略,最后使用Optorsim对模型和复制优化策略进行仿真。实验结果表明,该复制优化策略与基于单边拍卖的优化策略相比,具有较低的任务运行时间和网络有效占用率,较好的存储和计算资源利用率。     

Retracted: A novel viewpoint on information and interface design for auction web sites

For the reason of convenience, auction sites are one of the most popular and quickly developing of Internet shopping sites. There have been many problems, however, arising in relation to online auction commerce; these problems have been dealt with one by one. In addition to the important issue of security, some customers have faced problems such as complicated processes, insufficient product information, and bad interface design. Based on the consumer behavior model that we have built on auction sites, the purpose of this study is to evaluate the information and interface design for current domestic and foreign auction sites. The results indicated that some auction Web sites provided insufficient information and inconvenient interface design during some shopping steps. Foreign auction Web sites provided sufficient information and a more convenient interface design than did the domestic auction Web sites. The results, hopefully, can be used as the reference for online auction Web sites to provide customers more convenient shopping environments. © 2011 Wiley Periodicals, Inc.     

基于组合双向拍卖的云资源调度方法

针对跨数据中心的资源调度问题，提出了一种基于组合双向拍卖（PCDA）的资源调度方案。首先，将云资源拍卖分为三个部分：云用户代理报价、云资源提供商要价、拍卖代理组织拍卖；其次，在定义用户的优先级及任务紧迫度的基础上，在拍卖过程中估算每一个工作发生的服务等级协议（SLA）违规并以此计算云提供商的收益，同时每轮竞拍允许成交多项交易；最终达到根据用户等级合理分配云资源调度的效果。仿真实验结果表明该算法保证了竞拍成功率，与传统一次拍卖成交一项的组合双向拍卖方案相比，PCDA在竞拍时间段产生的能耗降低了35.00%，拍卖云提供商的利润提高了约38.84%。     

基于在线双边拍卖的分层联邦学习激励机制

在分层联邦学习中,能量受限的移动设备参与模型训练会消耗自身资源.为了降低移动设备的能耗,文中在不超过分层联邦学习的最大容忍时间下,提出了移动设备能耗之和最小化问题.不同训练轮次的边缘服务器能够选择不同的移动设备,移动设备也能够为不同的边缘服务器并发训练模型,因此文中基于在线双边拍卖机制提出了ODAM-DS算法.基于最优...     

基于多智能体的网络社区负面口碑信息传播研究

随着Web2.0的发展,网络社区成为企业负面口碑信息传播的一种便利媒介,网络社区负面口碑信息的传播能够直接或间接影响企业的经济利益以及客户忠诚度。从信息传播特征的视角,以病毒传播模型为基础,结合多主体建模方法,运用Netlogo仿真平台对网络社区中的负面口碑信息传播进行仿真实验。仿真实验主要从网络社区的参与、负面口碑信息价值对负面口碑信息传播过程的影响进行研究。结果表明,网络社区的参与以及较高的负面口碑信息价值对负面口碑信息传播过程具有明显的促进作用。基于实验结果提出了应对措施,即企业应对网络社区所扮演的角色进行控制 并 重点关注 高信息价值的负面口碑,从而可以在较短时间内以较低成本处理海量负面口碑信息,进而降低经济损失并提升客户忠诚度。     

基于拍卖和效益的双层次Agents协作机制

多Agent系统中的成员既有合作性又有自利性,只有将二者有机结合才能保证任务高效地完成.结合拍卖机制和能力模型,提出了双层次Agent合作机制,分析了联邦之间拍卖过程中的招标选择策略和投标策略,讨论了联邦内部基于效益的任务分配策略,介绍了熟人目标搜索和替换的方法.     

易腐生鲜货品车辆路径问题的改进混合蝙蝠算法

针对配送易腐生鲜货品的车辆其配送路径的选择不仅受货品类型、制冷环境变化、车辆容量限制、交货时间等多种因素的影响,而且需要达到一定的目标（如：费用最少、客户满意度最高）,构建了易腐生鲜货品车辆路径问题（VRP）的多目标模型,并提出了求解该模型的改进混合蝙蝠算法。首先,采用时间窗模糊化处理方法定义客户满意度函数,细分易腐生鲜货品类型并定义制冷成本,建立了最优路径选择的多目标模型;然后,在分析蝙蝠算法求解离散问题易陷入局部最优、过早收敛等问题的基础上,精简经典蝙蝠算法的速度更新公式,并对混合蝙蝠算法的单多点变异设定选择机制,提高算法性能;最后,对改进混合蝙蝠算法进行性能测试。实验结果表明,与基本蝙蝠算法和已有混合蝙蝠算法相比,所提算法在求解VRP时能够提高客户满意度1.6%~4.2%,且减小平均总成本0.68%~2.91%。该算法具有计算效率高、计算性能好和较高的稳定性等优势。     

云计算环境下兼顾买卖双方利益的双向拍卖资源分配算法

针对当前云计算环境下的资源分配算法不能充分考虑买卖双方利益的问题,本文提出了一种适用于云计算环境的组合双向拍卖资源分配模型。首先,初始化云经纪人列表和供应商报价列表,拍卖人通知拍卖参与者拍卖开始;然后,根据属性值按升序排序云经纪人请求和云服务供应商报价列表,从而确定投标获胜者;最后,获胜的云经纪人向相关云服务供应商发送任务并支付费用,云服务商执行任务。仿真实验使用CloudSim原型化,在基于Java的仿真云环境中从经济角度进行了效率评估。仿真结果表明,本文模型适用于云环境中的资源分配,在经济上非常有效。相比其他的现有模型,本文模型更能鼓励参与者在买卖双方公平公正的前提下根据真实估值竞购资源。