Working Vacation Policy for Load Sharing K-out-of-N: G System

Abstract

In industrial systems, the K-out-of-N: G system is a prominent type of redundancy. The load sharing protects such system from malfunctioning/destroying and avoids overload problem that affects the system reliability in a significant manner. In this paper we develop a Markovian model of load-sharing K-out-of-N: G system having non-identical repairable components wherein the server may on working vacation. During his vacation period, the server repairs the failed components with different service rates rather than completely terminating service rate. The failed component gets immediately repaired by the server if not on vacation, and unequal load is distributed among remaining surviving components. The lifetime of each component is load dependent followed by non-identical exponential distribution with different failure rates. The system is failed down due to common cause with failure density which is also exponentially distributed. We suggest closed structure analytic expressions for reliability, cost estimation and other performance measures of the load-sharing K-out-of-N: G repairable system by incorporating the concept of working vacation. For the solution aspiration, Runge-Kutta method is utilized to solve the system of differential equations. Furthermore, we perform the numerical analysis for two illustrations 1-out-of-3: G system and 3-out-of-4: G system. The numerical simulation is carried out for the validation of analytical results which are exhibited and compared by giving numerical outcomes and neuro-fuzzy outcomes based on fuzzy interference system with the help of MATLAB.

Vacation Policy for k-out-of-n Redundant System with Reboot Delay

“…Further, the term "Vacation Policy" introduces a dynamic element to system management. In the context of a redundant system, a vacation policy could imply a deliberate decision-making process regarding when and how to temporarily deactivate components for maintenance, upgrades, or other purposes (Kumar and Gupta, 2022). This strategic downtime, similar to a vacation for components, may be a key factor in optimizing the overall system performance and resource utilization.…”