# Systems Engineering: Availability and Reliability
## 4 Production Systems Scheduling, Management and Maintenance  
Advancements in technology, such as information and communication technologies, have changed the traditional manufacturing systems practices. This is especially true for different manufacturing systems due to their ability to cater to their needs such as Big Data, interoperability, timely delivery, etc. Production and maintenance tasks apply for access to the same resources. Maintenance-related machine downtime reduces productivity, but the costs incurred due to unplanned machine failures often outweigh the costs associated with predictive maintenance. Costs incurred due to unplanned machine failure include corrective maintenance, reworks, delays in deliveries, breaks in the work of employees and machines. Therefore, scheduling production and maintenance tasks should be considered jointly. The problem of generating a predictive schedule with given constraints is considered by [PERSON] et al. [8], with an objective to develop a scheduling method that reflects the operation of the production system and the nature of disturbances. The original value research results presented is the development of the method of a basic schedule generation with the application of Ant Colony Optimization. A predictive schedule is built by planning the technical inspection of the machine at the time of the predicted failure-free time. The numerical simulations are performed for job/flow shop systems. In the future, the presented method for generating predictive schedules will be compared with the genetic algorithm, as well as immune and clonal selection algorithms. The presented algorithm may, however, contribute to the development of a method that reflects the operation of the production system and the nature of disturbances and improves the system operation. In production systems maintenance, fault propagation behavior analysis is the basis of fault diagnosis and health maintenance. As presented by [PERSON] et al. [9], traditional fault propagation studies are mostly based on a priori knowledge of a causality model combined with rule-based reasoning, disregarding the limitations of experience and the dynamic characteristics of the system that cause deviations in the identification of critical fault sources. Thus, the authors [9] propose a dynamic analysis method for fault propagation behavior of machining centers that combines fault propagation mechanisms with model structure characteristics. They use the design structure matrix to establish the fault propagation hierarchy structure model. Considering the correlation of fault time, the fault probability function of a component is obtained, and the fault influence degree of nodes are calculated. By introducing the Copula and Coupling degree functions, the fault influence degree of the edges between the same level and different levels are calculated, respectively. As a result, a fault propagation intensity model was constructed by integrating the edge betweenness and then used as an index to analyze real-time fault propagation behavior. Finally, a certain type of machining center is taken as an example for a specific application. This study can provide a reference for the fault maintenance and reliability growth of a machining center. According to the fault propagation intensity of the components, the critical fault propagation paths and nodes of a machining center can be identified, provide a reference for the fault maintenance and encourage reliability growth of machining centers. This paper [9] demonstrates the effectiveness and practicability of the proposed method through the application of the specific case. [PERSON] et al. [10] point out that rising energy prices, increasing maintenance costs and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment.