Attribute-Based Worker Selection Scheme by Using Blockchain in Decentralized Crowdsourcing Scenario

Traditional crowdsourcing based on cen-tralized management platform is vulnerable to Distributed denial of service (DDoS) attack and single point of failure. Combining blockchain technology with crowdsourcing can well solve the above problems, enabling users to realize peer-to-peer transactions and collaboration based on decentralized trust in distributed systems where nodes do not need to trust each other. Although the current methods have solved the above problems, task publishers select workers based on their reputation values, which has two disadvantages: subjectivity and difficulty in initial value setting. Due to the complexity of crowdsourcing network, there will be malicious users in the network. The requirement for anonymity protects both legitimate and malicious users. In order to solve these problems, we propose an attribute-based worker selection scheme using the private set intersection technology. Our scheme also realizes the malicious user identity disclosure function. A concrete example of our scheme is given.     

Multi-attribute decision making based on neutral averaging operators for intuitionistic fuzzy information

In this paper, we construct the probability sum (PS) function and the proportional distribution rules of membership function and non-membership function of intuitionistic fuzzy sets (IFSs), and give their corresponding geometric interpretations. Based on which, we present the neutrality operation and the scalar neutrality operation on intuitionistic fuzzy numbers (IFNs). We propose the intuitionistic fuzzy weighted neutral averaging (IFWNA) operator and the intuitionistic fuzzy ordered weighted neutral averaging (IFOWNA) operator. The properties of the IFWNA operator and the IFOWNA operator are investigated. The principal advantages of the proposed operators are that both the attitude of the decision makers and the interactions between different intuitionistic fuzzy numbers (IFNs) are considered. Furthermore, approaches to multi-criteria decision making based on the proposed IFWNA and IFOWNA operator are given. Finally, an example is illustrated to show the feasibility and validity of the new approaches to the application of decision making.     

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.     

大型群体多属性决策的目标函数线性回归法

在定义标准决策值的基础上,运用线性回归的方法建立了带有约束条件的大型群体多属性决策目标函数线性回归模型,证明了该模型解的存在性和唯一性.大型群体多属性决策目标函数线性回归法对各专家的标准决策值的信息进行了最优意义上的集结和协调.此外,对大型群体多属性决策目标函数线性回归模型进行了讨论,并获得了一整套可用Matlab软件求解该模型最优解的算法,具体应用算例验证了所提算法的有效性.     

基于信息熵多属性决策的Mashup服务推荐方法

随着Mashup服务的快速发展,如何在海量的服务集合中找到用户感兴趣的、服务质量高的Mashup服务,成为一个难题。针对该问题,提出一种基于信息熵多属性决策的Mashup服务推荐方法。首先建立用户兴趣模型和Mashup服务的质量(Quality of Service,QoS)模型;其次,利用信息熵多属性决策方法,预测用户对候选Mashup服务的综合评分,并将综合评分最高的Top-K Mashup服务推荐给用户;最后,在不同数据集上进行实验比较与分析,结果表明:基于信息熵多属性决策的Mashup服务推荐方法能够有效地为用户推荐其感兴趣的、高质量的Mashup服务。     

基于改进的可能度优势关系的排序方法研究

优势关系是解决多属性决策问题的一种重要方法,也是研究不完备区间值信息系统的一种重要方法。在不完备区间值信息系统排序方法的研究中,针对属性过多从而可能引起排序失效的问题,提出了一种改进的可能度优势关系排序方法。这种新的排序方法把区间值优势关系和可能度相结合来改进经典优势关系定义的不足,并给出了平均综合优势度的定义。最后,通过与 ∝-β优势关系和容差优势关系等排序方法在具体算例中的比较分析,证明了改进的可能度优势关系排序方法不仅能够解决不完备区间值信息系统中的问题,而且能够使排序结果更合理、有效。     

A hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes

Global economic competition has spurred the manufacturing sector to improve and invest in modern equipment to satisfy the needs of the market. In particular, machine tool selection is the most important problem; it plays a primary role in the improvement of productivity and flexibility in the manufacturing environment and involves the imprecise, vague and uncertain information. This paper presents the hybrid approach of the fuzzy ANP (Analytic Network Process) and COPRAS-G (COmplex PRoportional ASsessment of alternatives with Grey relations) for fuzzy multi-attribute decision-making in evaluating machine tools with consideration of the interactions of the attributes. The fuzzy ANP is used to handle the imprecise, vague and uncertain information from expert judgments and model the interaction, feedback relationships and interdependence among the attributes to determine the weights of the attributes. COPRAS-G is employed to present the preference ratio of the alternatives in interval values with respect to each attribute and calculate the weighted priorities of the machine alternatives. Alternatives are ranked in ascending order by priority. As a demonstration of the proposed model, a numerical example is implemented based on the collected data and the literature. The result is then compared with the rankings provided by other methods such as TOPSIS-G, SAW-G and GRA. Moreover, a sensitivity analysis is conducted to verify the robustness of the ranking. The result highlights that the hybrid approach of the fuzzy ANP and COPRAS-G is a highly flexible tool and reaches an effective decision in machine tool selection.     

Multi-attribute auctions with different types of attributes: Enacting properties in multi-attribute auctions

Multi-attribute auctions allow agents to sell and purchase goods and services taking into account more attributes than just price (e.g. service time, tolerances, qualities, etc.). In this paper we analyze attributes involved during the auction process and propose to classify them between verifiable attributes, unverifiable attributes and auctioneer provided attributes. According to this classification we present VMA2, a new Vickrey-based reverse multi-attribute auction mechanism, which takes into account the different types of attributes involved in the auction and allows the auction customization in order to suit the auctioneer needs. On the one hand, the use of auctioneer provided attributes enables the inclusion of different auction concepts, such as social welfare, trust or robustness whilst, on the other hand, the use of verifiable attributes guarantee truthful bidding. The paper exemplifies the behavior of VMA2 describing how an egalitarian allocation can be achieved. The mechanism is then tested in a simulated manufacturing environment and compared with other existing auction allocation methods.     

基于可能性测度的工程管理决策的研究

考虑可能性Kripke结构的一种扩展形式——带有成本的可能性Kripke结构,并且研究在此之上的期望测度和多属性决策问题。带有成本的可能性Kripke结构是在可能性Kripke结构的转移关系上(或者是状态上)给定了成本——这个自然数可以看成是成本,当然也可以看成是收益。在此只考虑转移关系上的成本。为了便于理解,文中给出实例,即应用带有成本的可能性Kripke结构的相关理论来解决工程管理决策的某些问题。     

求解Web服务组合QoS优化的多属性决策及自适应遗传算法

随着面向服务计算(Service-oriented Computing,SOC)的不断发展,基于服务质量(Quality of Service,QoS)的Web服务组合研究成为了必然趋势。鉴于QoS属性的多维性及相互矛盾性,提出将基于QoS的Web服务组合优化问题转化为多属性决策问题进行求解。采用折中系数 对每个组合服务实例到正负理想点的距离进行累加求和,最终得到一组最优服务排序结果,用户可以根据自身偏好进行选择。传统的多属性决策方法无法有效地处理大规模的组合服务搜索空间,因此,为了有效地解决Web服务组合优化这一NP难题,提出一种结合多属性决策方法和自适应遗传算法的新型优化算法来解决该问题。实验采用真实的QoS综合服务数据集进行验证,实验结果表明,该方法能够在较短时间内找到全局近似最优解,且解集的排序结果接近于实际的最优服务排序。同时,该方法对于解决大规模的Web服务组合优化问题具有良好的可伸缩性。