Data envelopment analysis (DEA), as a useful management and decision tool, has been widely used since it was first invented by Charnes et al. in 1978. On the one hand, the DEA models need accurate inputs and outputs data. On the other hand, in many situations, inputs and outputs are volatile and complex so that they are difficult to measure in an accurate way. The conflict leads to the researches of uncertain DEA models. This paper will consider DEA in uncertain environment, thus producing a new model based on uncertain measure. Due to the complexity of the new uncertain DEA model, an equivalent deterministic model is presented. Finally, a numerical example is presented to illustrate the effectiveness of the uncertain DEA model.

The purpose of spare parts management is to maximize the system’s availability and minimize the economic costs. The problem of cost availability trade-off leads to the problem of spare parts demand prediction. Accurate and reasonable spare parts demand forecasting can realize the balance between cost and availability. So, this paper focuses on spare parts management during the equipment normal operation phase and tries to forecast the demand of spare parts in a specific inspection and replacement cycle. Firstly, the equipment operation and support scenarios are analyzed to obtain the supportability data related to spare parts requirements. Then, drawing on the idea of ensemble learning, a new feature selection method has been designed, which can overcome the limitations of a single feature selection method. In addition, an improved stacking model is proposed to predict the demand for spare parts. In the traditional stacking model, there are two levels of learning, base-learning, and meta-learning, in which the outputs of base learners are taken as the input of the meta learner. However, the proposed model brings the initial feature together with the output of the base learner layer as the input of the meta learner layer. And experiments have shown that the performance of the improved stacking model is better than the base learners and the traditional stacking model on the same data set.

Industrial equipment or systems are usually constructed as a multi-component series system with k-out-of-n:G subsystems to fulfill a specified function. As a common type of standby, warm standby is considered in the multi-component series system with k-outof-n:G standby subsystems. When a subsystem fails, the non-failed subsystems are shut off and cannot fail, which is defined as suspended animation (SA). If the SA is ignored the non-failed subsystems are assumed to keep working in the SA time, which will cause inaccuracy in the availability analysis for the system. In this paper, we focus on the SA to construct an availability model for a multi-component series system with k-out-of-n:G warm standby subsystems. Multiple continuous time Markov chains are constructed to model the system availability. A Monte Carlo simulation has been carried out to verify our method. Several interesting findings are obtained. 1) The failure rates of subsystems with SA and their limits are derived. 2) The closed-form expressions for the stationary availability of the system and subsystems, mean time to failure, mean time to repair and stationary failure frequency are obtained considering SA. 3) The system stationary availability is a monotone function for its parameters. 4) The SA effect on the stationary availability should be emphasized in two cases, one is both the value of n/k and the failure rate of active components in a k-out-of-n subsystem are relatively large or small, the other is both the value of n/k and the repair rate are relatively small.

No abstract

No abstract