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Availability of military systems is of major concern for military planners at both tactical (battle) level and at strategic level (long‐term national planning). Availability factors critically affect the operational effectiveness during military operations. Military systems are complex and lend themselves to simulation approach for availability estimation as analytical solutions are extremely difficult. The purpose of this paper is to discuss the method of systems modeling to approach the simulation for availability estimation of military systems.

Availability measures are needed for two main domains of application: peacetime operations and battlefield situations. Availability measures include not only inherent availability of interest to designers/manufacturers, but also operational availability and field/service availability. The simulation approach adopted here involves discrete event simulation (DES) techniques using Monte Carlo methods since a network of events can be included in the model. A system engineering approach is emphasized, starting with system representation and characterisation, and using system aggregation techniques.

Modeling involves hierarchical models and network diagrams for events. First the system is described by a hierarchical model; the events and transitions are represented with state transition diagrams (STD). The simulation scheme would be based on initial resources or inventory as military operations proceed, with random variates for event times or rates. The availability as a function of time A(t) is arrived at. The reliability and maintainability models are simulated with probability distributions or using empirical distributions. The methods of data collection and analysis, and sensitivity analysis are mentioned. The methodology is explained with two case studies from the authors' work. The approaches of other workers in recent years are summarised.

The paper shows that the simulation models can suitably be modified to include their applications for army and navy military operations. Also, with proper data on all major subsystems of interest for the weapon platform and accurate past war data, it is possible to fine‐tune the models for online use during military campaigns. The availability figures thus obtained may also be used for procurement decisions for long‐term and strategic planning.

Maintaining a high level of availability of weapon systems during battles becomes important from the point of view of winning the battle. Due to attrition factors (failure due to battle damage and unreliability) and logistic delays in the repair process, maintaining the required level of availability is difficult. In this paper, we develop a simulation model for availability of fighter aircraft considering multiple failures causing system failure and logistic delays in the repair process. The methodology is based on discrete event simulation using Monte Carlo techniques. The failure time distribution (Weibull) and the repair time distribution (exponential) for the considered subsystems of the aircraft and the logistic delay time distribution (log‐normal) for the logistic factors spares, crew and equipment were chosen with suitable parameters. The results indicate the pronounced decrease in availability (as low as less than 10 per cent in some cases) due to multiple failures and logistic delays. The results are, however, highly sensitive to a combination of reliability, maintainability and logistic delay parameters.

While steady-state analysis is useful, it does not consider the inherent transient characteristics of repairable systems' behavior, especially in systems that have relatively short life spans, or when their transient behavior is of special concern such as the motivating example used in this paper, military systems. Therefore, a maintenance policy that considers both transient and steady-state availability and aims to achieve the best trade-off between high steady-state availability and rapid stabilization is essential.

This paper studies the transient behavior of system availability under the Kijima Type II virtual age model. While such systems achieve steady-state availability, and it has been proved that deploying preventive maintenance (PM) can significantly improve its steady-state availability, this improvement often comes at the price of longer and increased fluctuating transient behavior, which affects overall system performance. The authors present a methodology that identifies the optimal PM policy that achieves the best trade-off between high steady-state availability and rapid stabilization based on cost-availability analysis.

When the proposed simulation-based optimization and cost analysis methodology is applied to the motivating example, it produces an optimal PM policy that achieves an availability–variability balance between transient and steady-state system behaviors. The optimal PM policy produces a notably lower availability coefficient of variation (by 11.5%), while at the same time suffering a negligible limiting availability loss of only 0.3%. The new optimal PM policy also provides cost savings of about 5% in total maintenance cost. The performed sensitivity analysis shows that the system's optimal maintenance cost is sensitive to the repair time, the shape parameter of the Weibull distribution and the downtime cost, but is robust with respect to changes in the remaining parameters.

Most of the current maintenance models emphasize the steady-state behavior of availability and neglect its transient behavior. For some systems, using steady-state availability as the sole metric for performance is not adequate, especially in systems that have relatively short life spans or when their transient behavior affects the overall performance. However, little work has been done on the transient analysis of such systems. In this paper, the authors aim to fill this gap by emphasizing such systems and applications where transient behavior is of critical importance to efficiently optimize system performance. The authors use military systems as a motivating example.