Particle Swarm Optimization for a redundant repairable machining system with working vacations and impatience in a multi-phase random environment.

With the increasing reliance on cloud computing as the foundational manufacturing systems with intricate dynamics, featuring multiple service areas, varying job arrival rates, diverse service requirements, and the interplay of failures and impatience, significant analytical challenges arise. Queueing networks offer a powerful stochastic modeling framework to capture such complex dynamics. This paper develops a novel, exhaustive queueing model for a finite-capacity redundant multi-server system operating in a multi-phase random environment. The proposed model uniquely integrates real-world factors, including server breakdowns and repairs, waiting servers, synchronous working vacations, and state dependent balking and reneging, into a single queueing model, representing a significant advancement in the field. Using the matrix-analytic method, we establish the steady-state solution and derive key performance metrics. Numerical experiments and sensitivity analyses elucidate the impact of system parameters on performance measures. Additionally, a cost model is formulated, enabling cost optimization analysis using direct search method and Particle Swarm Optimization (PSO) to identify efficient operating configurations. [Display omitted] • Analytical framework for finite capacity redundant multi-server queue in multi-phase environment. • Matrix analytic method calculates steady-state probabilities to assess system performance. measures. • Through sensitivity analysis, key cost variables are identified, aiding decision-making. • Optimizing the cost function by merging Direct Search Method (DSM) and Particle Swarm Optimization (PSO). [ABSTRACT FROM AUTHOR]