The future research on hybrid nanofluids need to be done as there are many applications of hybrid nanofluids. Some of them are electro-mechanical, renewable energy and manufacturing process application and also the application in automotive cooling. Therefore, this numerical study is conducted to investigate the aligned magnetohydrodynamics (MHD) flow of hybrid nanofluid over a vertical plate through a porous medium. The base fluid (water), silver and copper oxide flowed over a vertical plate in the presence of MHD and porous medium with boundary conditions of constant wall temperature by taking a few types of parameters into consideration. By using similarity transformation, the governing nonlinear partial differential equations are transformed into nonlinear ordinary differential equations. Keller Box method is applied to solve numerically the nonlinear ordinary differential equations and the system is solved by using Fortran programming. The results of the study are illustrated graphically to show the behavior of velocity and temperature also skin friction and Nusselt number of hybrids nanofluid affected by the parameters. It is found that the velocity profiles increase, and temperature profiles decrease when the angle of aligned magnetic field, the interaction of magnetic parameter and local Grashof number increase while the velocity profiles decrease, and temperature profiles increase when the porous parameter and volume fraction of nanoparticles parameter increase. Besides, the skin friction of Ag-CuO-water is less than CuO-water and Nusselt number of Ag-CuO-water is higher than CuO-water.

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.