This article investigates the optimal joint inspection and mission abort policies for deteriorating systems which execute a mission continuously. The system degradation is modelled by gamma process and the information on the system state can only be obtained via inspections. To enhance the system survivability, the mission can be aborted and the rescue procedure will be initiated immediately. Different from previous research, the decision to abort or to continue the mission depends on the predictive reliability other than the fixed degradation level, which is proved to be more effective in cost saving. The inspection period and the mission abort threshold jointly form a two-dimensional policy. Under this policy, the explicit expressions for mission success probability and system survivability are derived analytically. The optimal joint policy is investigated to balance the tradeoff between mission success probability and system survivability. It aims to determine the inspection period and the reliability threshold simultaneously such that the expected total cost including mission failure cost, system loss cost and inspection cost is minimized. Some structural properties of the optimal policy are obtained theoretically. Finally, numerical examples are provided to demonstrate the model.

Due to ubiquitous Internet connectivity, widely available cloud services, and popular mobile devices, mobile networks have become service delivery and consumption platforms for many industries worldwide. To recommend optimal mobile Web services with trustworthy Quality-of-Service (QoS) and dynamic user preferences, this paper proposes a novel service recommendation model based on Fuzzy Analytic Hierarchy Process (FAHP) and ordinal utility function. First, a Multi-QoS vector is defined, and to take into account the trustworthiness of QoS, the fidelity of QoS is modeled as one component of the Multi-QoS vector. Then, a fuzzy hierarchy including dual attributes of QoS (objective attribute and subjective evaluation) is established to fully consider the objective and subjective attributes' impact on optimal service recommendation. Furthermore, a FAHP-based weighting mode is developed, in which the resolution ratio of weight can be adjusted dynamically by decision-maker according to user preferences. Finally, the optimal service is obtained through the calculation of ordinal utility function of candidate service. Experimental results and method comparison illuminate the feasibility and efficiency of the proposed model.

Link prediction plays an important role in complex network analysis. It is to predict the existence of an unknown link or a future link in a network. Classical methods for link prediction evaluate the similarity of vertices based on common neighbors, and denote that every common neighbor makes equal contribution to the connection likelihood. However, common neighbors may play different roles depending on whether they belong to the same community, where vertices are densely or sparsely connected to other communities. This paper proposes a novel similarity index for link prediction which combines the topology information and community information. The proposed approach is compared with ten classical local similarity indices on ten real-world networks. The experiment results shown that the proposed approach can improve the accuracy of link prediction no matter which community detection algorithm is used.

E-business based maintenance, repair and overhaul (E-MRO) is a new MRO service mode. Although in real world there are a number of E-MRO prototype systems, few comprehensive studies have been conducted on this topic. Motivated by the challenges of making optimal E-MRO service planning, simultaneously considering the capacity constraints of MRO service providers and the maintenance constraints of equipment users, this paper proposes a stochastic programming model involving multi-choice parameters, where uncertain factors in E-MRO are quantified. To solve the model, the properties of expectation of a random variable, and the Lagrange interpolating polynomial approach are used to derive the deterministic model equivalent to the stochastic programming model. The objective of the model is to seek optimal service planning, including determining whether to configure the corresponding service from the corresponding provider to the corresponding user at the corresponding period, and determining the time of the corresponding service. The optimal service planning can be referred by practitioners for a more reasonable decision. A numerical example validated the feasibility of proposed model.

Motivated by the challenges of seeking the optimal E-business based maintenance, repair and overhaul (E-MRO) service policy, simultaneously considering bilateral requirements of quality of service (QoS), this paper presents a mathematical model based on variable fuzzy recognition and multi-objective programming. Cloud model is utilized to quantify the information of bilateral requirements as the numerical values. Then, the comprehensive satisfaction of multiple attribute is calculated by using variable fuzzy recognition method. Based on bilateral satisfactions, a multi-objective programming model is formulated, where bilateral QoS satisfactions are modeled as objective functions. By using global criteria method, the multi-objective optimization is transformed to an equivalent single objective optimization, which can be solved by LINGO. Finally, the optimal E-MRO service policy satisfying bilateral requirements is obtained. A case study illustrated the feasibility and efficiency of the proposed model.