<![CDATA[Scheduling is a fundamental combinatorial optimization problem that arises in numerous real-world domains, including manufacturing, cloud computing, healthcare, and transportation. Particle Swarm Optimization (PSO), inspired by the collective behaviour of bird flocking and fish schooling, has emerged as one of the most effective and widely applied metaheuristic approaches for solving scheduling problems. This systematic literature review synthesizes research published between 2004 and 2024, examining 35 peer-reviewed journal articles and conference papers to provide a comprehensive analysis of PSO applications, variants, and performance outcomes in scheduling. The review identifies key research trends, categorizes PSO variants, including Standard PSO, Hybrid PSO, Multi-Objective PSO, Adaptive PSO, and Quantum PSO, and evaluates their performance across different scheduling contexts. The findings indicate that Hybrid PSO approaches consistently outperform Standard PSO in terms of solution quality, while Adaptive PSO demonstrates superior convergence behaviour in dynamic environments. Current challenges, including premature convergence, scalability limitations, and parameter sensitivity, are highlighted alongside existing research gaps and potential directions for future research.]]>
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