基于前景理论和模糊理论的在线多属性采购拍卖供应商选择决策

在线多属性采购拍卖已在工程招标、政府采购等诸多领域有着广泛应用.由于在实际的采购拍卖活动中,往往采购方对采购商品的相关属性指标存在不同程度的不确定性,采购商在对采购品属性的描述及相应权重的确定上都存在困难,进而导致采购商在面临供应商选择决策上难度增大.为了解决这些问题,从采购方利益角度出发,考虑风险态度对决策者行为的影响,引入前景理论,研究4种模糊描述方式并存情况下的在线多属性采购拍卖的供应商选择决策问题;在综合考虑主客观因素确定属性权重的情况下,提出一种新的在线多属性采购拍卖供应商选择决策方法;最后通过相关的算例分析与比较说明所提出方法的有效性.     

属性权重不确定条件下的区间直觉模糊多属性决策

在区间直觉模糊集(Interval-valued intuitionistic fuzzy set, IVIFS)的框架内,重点研究了属性权重在一定约束条件下和属性权重完全未知的 多属性群决策问题.首先利用区间直觉模糊集成算子获得方案在属性上的综合区间直觉模糊决策矩阵,进一步依据逼近理想解排序法(Technique for order preference by similarity to an ideal solution, TOPSIS) 的思想计算候选方案和理想方案的加权距离,最后确定方案排序.其中针对属性权重在一定约束条件下的决策问题,提出了基于 区间直觉模糊集精确度函数的线性规划方法,用以解决属性权重求解问题.针对属性权重完全未知的决策问题,首先定义了区间直觉 模糊熵,其次通过熵衡量每一属性所含的信息量来求解属性权重.实验结果验证了决策方法的有效性和可行性.     

基于蜂群算法的多属性反向拍卖中供应商投标策略模型

为了确定多因素多组合投标环境下当前供应商的最佳投标策略,给出一种多属性反向拍卖中供应商投标策略模型。该模型分析了竞争供应商的投标策略和获胜者确定规则,着重将人工蜂群算法的优化流程与当前供应商的决策过程相结合,根据竞争供应商的投标策略计算其成本函数,利用人工蜂群算法进行探索,得到当前投标组合下一轮最优投标策略。仿真实验表明,该模型在动态变化的投标过程中能快速高效求得当前供应商每一个投标组合的最优投标策略。     

对方案有偏好的最小最大偏差多属性决策方法

针对部分属性权重信息且对方案有偏好的多属性决策问题,提出了一种新的决策方法。该方法通过求解最小化最大的主、客观偏差的数学优化模型,较合理地确定属性的权重向量。克服已有方法中不能保证每一方案的主、客观偏差都最小的问题。产品开发应用实例表明方法的有效性和实用性。     

基于Pythagorean不确定语言的扩展VIKOR多属性群决策方法

针对属性值为Pythagorean不确定语言变量,属性权重和专家权重完全未知的群决策问题,提出一种扩展VIKOR多属性群决策方法.首先,给出Pythagorean不确定语言变量的概念,提出考虑语义变化的Pythagorean不确定语言变量运算规则、大小比较方法和Hamming距离测度;其次,提出基于Pythagorean 不确定语言模糊熵的属性权重确定方法和基于相似度的专家权重确定方法,进而提出一种新的扩展VIKOR方法;最后,通过国内航空公司服务质量评估实例验证所提出方法的有效性和可行性.     

区间灰色不确定语言多属性群决策方法

针对属性值为区间灰色不确定语言信息的多属性群决策问题,在定义区间灰色不确定语言变量及其运算规则的基础上,给出了3种几何加权集结算子,由区间灰色不确定语言几何加权算子集结各决策者给出的决策矩阵得到群体决策矩阵。在属性权重已知的情形下,基于该算子集结单个决策者给出的属性权重向量得到群体属性权重向量;在属性权重完全未知的情形下,采用信息熵法确定属性权重向量。采用区间灰色不确定语言混合几何加权算子集结各属性评价信息,得到各方案的综合评价值,基于区间灰色不确定语言变量大小比较的方法得到方案排序结果。算例分析表明了该方法的有效性与可行性。     

采办协同工程中的不确定多属性决策技术

针对复杂武器装备采办协同工程全寿命周期、全系统、全方位的决策需求,在分析了虚拟采办SBA信息具有宏观、定性和不确切特点的基础上,提出将不确定多属性决策引入到SBA问题空间的策略.通过将SBA问题求解过程分解与归纳为不确定多属性决策过程,继而根据SBA信息不确定类别确定解的获取方式,从而将理论上的复杂采办决策问题转化为智能工程化处理的迭代逼近的数值计算,并建立了基于Web服务的多属性个人决策和群体决策2类不确定算法工具包.最后在SBA协同工程平台中检验了该方法的可行性和工具包的有效性.     

不确定性多属性决策中的ER方法改进

基于对不确定性多属性决策问题中ER方法的研究,提出一种不确定性多属性决策中的改进ER方法,井证明该方法完全满足证据合成的4个公理．通过实例运算,进一步验证了新方法的有效性和合理性．     

基于前景理论的不确定TOPSIS多属性决策方法

针对属性权重未知、属性值为犹豫模糊集的多属性决策问题,本文提出一种基于前景理论和粗糙集的多属性决策方法,充分考虑了决策者心理风险因素对决策结果的影响.首先,以正、负理想点作为参考点计算各属性下的前景价值函数,定义新的综合前景值,并根据给定的阈值得到判断矩阵;然后,根据判断矩阵进行属性约简,确定属性权重;最后,计算各备选方案的加权综合前景值,利用TOPSIS方法对备选方案进行排序,并通过算例证实该方法的可行性和有效性.     

Relationship preserving multi-attribute reverse auction: A web-based experimental analysis

Repeated use of reverse auction often degrades the buyer–supplier relationship. Theoretical studies show that providing incentive to the losing but competing suppliers can keep them interested to participate in future auctions thereby maintaining a healthy level of competition. We conduct web-based experiments to validate this theoretical observation in multi-attribute reverse auctions. We compare incentive-oriented and standard multi-attribute reverse auctions and demonstrate that the results in the laboratory setting corroborate the theoretical findings. Adopting incentive-oriented mechanism, the buyer is able to provide better utility to suppliers while protecting her own. We conclude that such a mechanism can reduce the negative perception of the suppliers and help build better buyer–supplier relationship in the long run.     

一种基于在线反向拍卖技术的计算网格资源分配方法

利用经济模型研究网格资源管理是当前网格研究新的热点。在已有的网格资源管理方法的基础上,针对供大于求的计算网格环境,提出了一种基于在线反向拍卖技术的计算网格资源分配方法,并定义了相应的QoS函数,分析了该方法的适用范围和优点。最后通过模拟实验验证了该方法的效用,实验结果证明该方法是一种有效的计算网格资源分配方法。     

Market structure and quality uncertainty: a theoretical framework for online auction research

Given large numbers of buyers and sellers, with access to a wide variety of information, economic theory suggests that online auction markets should provide an efficient mechanism for establishing equilibrium prices. Previous research on online auction prices, however, is far from conclusive, having produced mixed findings. The seemingly inconsistent and sometimes contradictory results make it very difficult to integrate empirical findings into a coherent body of knowledge. The purpose of this paper is to present a framework that can reconcile previous findings and provide direction for future research. Accordingly, we propose a simple theoretical framework with two dimensions—market structure (thick vs. thin) and quality uncertainty (high vs. low). By examining the literature in the context of market structure and quality uncertainty we find that previous studies are not necessarily at odds, but that there is actually a fairly consistent pattern of results.     

Algorithm selection in bilateral negotiation

Despite the abundance of strategies in the multi-agent systems literature on repeated negotiation under incomplete information, there is no single negotiation strategy that is optimal for all possible domains. Thus, agent designers face an “algorithm selection” problem—which negotiation strategy to choose when facing a new domain and unknown opponent. Our approach to this problem is to design a “meta-agent” that predicts the performance of different negotiation strategies at run-time. We study two types of the algorithm selection problem in negotiation: In the off-line variant, an agent needs to select a negotiation strategy for a given domain but cannot switch to a different strategy once the negotiation has begun. For this case, we use supervised learning to select a negotiation strategy for a new domain that is based on predicting its performance using structural features of the domain. In the on-line variant, an agent is allowed to adapt its negotiation strategy over time. For this case, we used multi-armed bandit techniques that balance the exploration–exploitation tradeoff of different negotiation strategies. Our approach was evaluated using the GENIUS negotiation test-bed that is used for the annual international Automated Negotiation Agent Competition which represents the chief venue for evaluating the state-of-the-art multi-agent negotiation strategies. We ran extensive simulations using the test bed with all of the top-contenders from both off-line and on-line negotiation tracks of the competition. The results show that the meta-agent was able to outperform all of the finalists that were submitted to the most recent competition, and to choose the best possible agent (in retrospect) for more settings than any of the other finalists. This result was consistent for both off-line and on-line variants of the algorithm selection problem. This work has important insights for multi-agent systems designers, demonstrating that “a little learning goes a long way”, despite the inherent uncertainty associated with negotiation under incomplete information.     

Norm negotiation in online multi-player games

In recent years, the proliferation of VOIP data has created a number of applications in which it is desirable to perform quick online classification and recognition of massive voice streams. Typically such applications are encountered in real time intelligence and surveillance. In many cases, the data streams can be in compressed format, and the rate of data processing can often run at the rate of Gigabits per second. All known techniques for speaker voice analysis require the use of an offline training phase in which the system is trained with known segments of speech. The state-of-the-art method for text-independent speaker recognition is known as Gaussian mixture modeling (GMM), and it requires an iterative expectation maximization procedure for training, which cannot be implemented in real time. In many real applications (such as surveillance) it is desirable to perform the recognition process in online time, so that the system can be quickly adapted to new segments of the data. In many cases, it may also be desirable to quickly create databases of training profiles for speakers of interest. In this paper, we discuss the details of such an online voice recognition system. For this purpose, we use our micro-clustering algorithms to design concise signatures of the target speakers. One of the surprising and insightful observations from our experiences with such a system is that while it was originally designed only for efficiency, we later discovered that it was also more accurate than the widely used GMM. This was because of the conciseness of the micro-cluster model, which made it less prone to over training. This is evidence of the fact that it is often possible to get the best of both worlds and do better than complex models both from an efficiency and accuracy perspective. We present experimental results illustrating the effectiveness and efficiency of the method.

Cheating in online auction – Towards explaining the popularity of English auction

Action frauds constitute largest part of all Internet frauds. Cheating is a kind of fraud that does not have direct evidences of its occurrence. We conduct theoretical studies as well as simulation experiments to find out the effect of cheating in three important types of auctions: English auction, first-price sealed-bid, and second-price sealed-bid auction. Our cheating environment consists of shill bidding, bid shading and false bidding in English, first-price and second-price auction, respectively. In the experiments ordinary bidders, bidders with the equilibrium bidding strategy, and cheaters compete with each other. Both theoretical and experimental results confirm that the equilibrium bidding strategies indeed increases the bidders’ expected utility. Therefore, it can be concluded that adoption of rational bidding strategies can combat cheating. It is found that most of the auction sites intuitively prefer English auction to other auction mechanisms. There is not much theoretical or experimental evidence to support such an intuition. We use honest bidder’s expected gain and honest seller’s revenue loss as a basis to compare these three important auctions types. The analysis of the results reveals English auction to be the most preferred mechanism from both honest buyer’s and honest seller’s point of view. This result can be used as an experimental evidence to explain the popularity of English auction over the Internet.     

A secure reverse Vickrey auction scheme with bid privacy

In recent years auctions have become more and more important in the field of multi-agent systems as useful mechanisms for resource allocations, task assignments and electronic commerce. In this paper, we concentrate on the use of the reverse Vickrey auction for task assignment scenarios and propose a novel RVP auction protocol as a method to solve problems to bid privacy in reverse Vickrey auctions. A verifiable technique of encryption key chain is used to find the second lowest bid without revealing the losing bid and unnecessary information. Through analysis, it is verified that our new scheme is robust against cheating bidders.     

A meta-analysis on the effects of online auction design options: The moderating effect of value uncertainty

Online auction design options (the public reserve price, secret reserve option and buy-out option) are critical in determining auction outcomes (the number of bids, the probability of sale and auction price). However, previous studies about the impacts of online auction design options on auction outcomes have generated inconsistent or even contradictory results. To synthesize the inconsistencies and reach more substantive conclusions, we conduct this meta-analysis study. Furthermore, to explain the inconsistencies, we identify the value uncertainty of auction items as a key moderator on the impacts of auction design options on auction outcomes, and verify the moderating effects using meta-analysis methods.This study has three main findings: (i) the public reserve price has a positive effect on the auction price, and this effect is stronger when the value uncertainty of auction items is higher; (ii) the secret reserve option has a positive effect on the auction price when auction items are of low value uncertainty, but the magnitude of this effect decreases when the value uncertainty increases; (iii) the buy-out option has positive effects on both the probability of sale and the auction price when auction items are of low value uncertainty, but has negative effects on these two auction outcomes when auction items are of high value uncertainty.