Due to the advancements in sensing technologies and computational capabilities, system health assessment and prognostics have been extensively investigated in the literature. Industry has adopted and implemented many advanced system prognostic applications. This article reviews recent research advances and applications in prognostics modeling methods for engineering systems. The reviewed papers are classified into three major areas based on whether the physics of failure knowledge is incorporated for prognostics, i.e., the data-driven, physics-based, and hybrid prognostic methods. The technical merits and limitations of each prognostic method are discussed. This review also summarizes research and technological challenges in engineering system prognostics, and points out future research directions.

This article proposes an approach based on the stochastic process and approximate dynamic behavior for modeling cascading failures of the cyberphysical power system (CPPS) considering component multistate failures. Our approach focuses on modeling the coupling effects of interdependencies of the integrated power and communication networks, including control and power supply dependencies as well as the effects of the degradation of one network performance on the other. Nine failure states of each component and the performance/capacity degradation in different failure states are incorporated into the modeling approach. The state transitions between nine failure states are modeled as a discrete time Markov process whose transition probabilities are time-varying. In addition, the article proposes a new robustness measure for multistate CPPS, which integrates the information of the local and global topology, the damage state of components, as well as the performance degradation of the CPPS. The system that couples the IEEE 118-bus model with a small-world communication network is used as a testbed to demonstrate the feasibility and effectiveness of the proposed modeling approach, and the comparison with existing robustness measures shows the superiority of the proposed measure.

In order to solve the problem of multi-AGV path planning in a packaging workshop, this paper proposes a multi-AGV path-planning method based on time windows. A grid method is selected to build a map model. A penalty function is added to the A* algorithm to reduce the number of invalid turns made by AGVs during transportation. The AGV priorities are set according to the differences in AGV transport states. At the same time, the raster time window method is used to describe the five types of conflict that may occur in the process of multi-AGV transport. Combined with the AGV priorities, a multi-AGV anti-collision strategy is provided to realize the conflict-free path planning of multiple AGVs. The algorithm’s effectiveness was verified by simulation, and a reasonable quantity of AGVs was proposed based on AGV sensitivity analysis. The improved A* algorithm combined with the time window method can realize multi-AGV collision-free transportation and improve the efficiency and reliability of multi-AGV transportation.

In order to explore the positive impact of the joint distribution model on the reduction in logistics costs in small-scale logistics enterprises, considering the demand on enterprises for simultaneous pick-up and delivery, as well as the cost of carbon emissions, this study considers the vehicle routing problem of simultaneous pick-up and delivery under a joint distribution model. First of all, an independent distribution model and a joint distribution model including fixed transportation, variable transportation, time penalty, and carbon emissions costs are established; second, by adding the self-adaption cross-mutation probability and the destruction and repair mechanism in the large-scale neighborhood search algorithm, the genetic algorithm is improved to adapt to the solution of the model in this paper, and the effectiveness of the improved algorithm is verified and analyzed. It is found that the improved genetic algorithm is more advantageous than the original algorithm for solving the problems of both models designed in this paper. Finally, the improved genetic algorithm is used to solve the two models, and the results are compared and analyzed. It is found that the joint distribution model can reduce the total cost by 6.61% and the carbon emissions cost by 5.73%. Additionally, the impact of the carbon trading mechanism on the simultaneous pick-up and delivery vehicle routing problem under the joint distribution model is further explored. The results of this study prove that enterprises can effectively reduce costs, improve profits, reduce carbon emissions, and promote the sustainable development of logistics enterprises under the condition of joint distribution.

To improve the satisfaction of both service demanders (SDs) and service providers (SPs) in the matching of cloud manufacturing (CMfg) tasks and services, a two‐sided stable matching model of CMfg tasks and service considering the nonlinear relationship between satisfaction and expectations is proposed. As the expectations of SDs and SPs are difficult to be quantified directly, an evaluation method based on interval‐valued hesitant fuzzy linguistic sets (IVHFLSs) is first presented. Next, a nonlinear model of satisfaction and expectations is built to quantify the satisfaction, which achieves accurate quantification of satisfaction. Then, a two‐sided stable matching model of CMfg tasks and service is built, which takes the satisfaction of SDs and SPs as the optimization goals and considers the individual rationalities and blocking pairs. Finally, an adaptive genetic algorithm (AGA) is designed to solve the proposed two‐sided matching model. A practical application and comparison analysis is used to verify the effectiveness and superiority of the research.