(untitled) (American Journal of Engineering , Mechanics and Architecture (2993-2637), 2025)
The operation of motor trains presents a complex, dynamic control problem, requiring real-time decision-making that balances multiple conflicting objectives—such as energy efficiency, punctuality, passenger comfort, and component wear. While recent advancements in multi-objective optimization (MOO) algorithms have enabled more intelligent train control strategies, significant research gaps remain. This paper reviews the current state of control algorithms for motor train operations and identifies key shortcomings in existing literature. These include limited real-world validation, insufficient modeling of uncertainty, inadequate integration of environmental constraints, and lack of scalability for large rail networks. Moreover, many approaches still rely on deterministic models, overlooking the stochastic nature of operational conditions. The study emphasizes the need for hybrid, adaptive, and learning-based optimization methods that can better handle complex trade-offs and uncertainties. Bridging these gaps is essential to develop more robust, energy-efficient, and reliable control systems for future intelligent rail transport.