FROM SIMULATION TO STRATEGY: DISCRETE-EVENT APPROACHES TO PRIME MOVER ALLOCATION IN CONTAINER TERMINALS

The increasing complexity of container terminal operations has amplified the challenges associated with the efficient allocation of prime movers, which serve as the vital transport link between quay cranes and yard cranes. Inefficient allocation can lead to congestion, prolonged vessel turnaround times, and higher operational costs, ultimately undermining port competitiveness. Addressing this challenge requires robust analytical frameworks capable of capturing the dynamic, stochastic, and interdependent nature of container terminal systems. This study aims to examine how discrete-event simulation (DES) and related analytical approaches have been applied to optimize prime mover allocation in container terminals. By utilizing Scopus AI analytics, the study conducted a structured review based on a comprehensive search string that integrated key concepts of simulation, resource allocation, optimization, and efficiency in port logistics. The search produced a systematically organized corpus of scholarly works, encompassing journal articles, conference proceedings, and technical reports published between 2000 and 2025. The analysis was guided by Scopus AI features including the Summary, Expanded Summary, Concept Maps, Topic Experts, and Emerging Themes, allowing for both bibliometric mapping and narrative synthesis of the research landscape. The findings highlight three major contributions of DES to prime mover allocation: (i) operational optimization through simulation-based frameworks that reduce congestion and improve quay crane productivity, (ii) strategic planning via predictive simulation models and integrated scheduling, and (iii) sustainability-focused approaches that minimize fuel consumption and emissions. Emerging themes such as machine learning enhanced DES, decision support systems, and Boolean-algebraic methods underscore the evolution of research toward intelligent and adaptive allocation strategies. The study advances theory by framing prime mover allocation as both an operational and strategic systems problem, while offering practical insights for improving terminal efficiency, resilience, and sustainability. Limitations include dataset scope and limited empirical validation, with future research needed on predictive simulation, digital twin integration, and comprehensive sustainability metrics.