Abstract: Iron molten steel transportation serves as a critical link connecting ironmaking and steelmaking in steel enterprises, with its efficiency constrained by factors such as solution quality and computation speed of multi-locomotive path planning algorithms at the iron-steel interface. To address existing bottlenecks including low computational efficiency and susceptibility to local optima, this paper proposes a Whale-optimized Dynamic Weight Time A^* algorithm（WODWT-A^*） for multi-locomotive path planning. Firstly, the algorithm introduces a dynamic weight mechanism that linearly combines cost ratios with difference functions to enhance the heuristic function. This innovation significantly improves temporal efficiency and pathfinding accuracy in dynamic environments, overcoming the efficiency limitations of traditional A^* algorithms in complex scenarios. Furthermore, to enhance global optimization capability, the method incorporates the Whale Optimization Algorithm（WOA）. By simulating humpback whales′ group predation strategies, it achieves dynamic optimization of initial solutions and adaptive parameter adjustment, thereby strengthening multidimensional exploration in complex solution spaces and effectively avoiding local optima traps. This synergistic mechanism between global search and local optimization enables WODWT-A^* to significantly improve planning stability and solution quality. Practical case studies demonstrate that WODWT-A^* exhibits high adaptability and reliability in multi-task concurrency, path congestion, and dynamic environments, providing an optimized solution for coordinated scheduling of multiple locomotives at the iron-steel interface.