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Group train operation (GTO) applications have reduced the tracking intervals for overloaded trains, and can affect the efficiency of rail transport. In this paper, we first analyze the differences between GTO and traditional operation (TO). A new mathematical model and simulated annealing algorithm are then used to study the problem of mixed group train operation. The optimization objective of this model is to maximize the transportation volume of special heavy-haul railway lines within the optimization period. The main constraint conditions are extracted from the maintenance time, the minimum ratio of freight volume, and the committed arrival time at each station. A simulated annealing algorithm is constructed to generate the mixed GTO plan. Through numerical experiments conducted on actual heavy-haul railway structures, we validate the effectiveness of the proposed model and meta-heuristic algorithm. The results of the first contrastive experiment show that the freight volume for group trains is 37.5% higher than that of traditional trains, and the second experiment shows a 30.6% reduction in the time during which the line is occupied by trains in GTO. These findings provide compelling evidence that GTO can effectively enhance the capacity and reduce the transportation time cost of special heavy-haul railway lines. [ABSTRACT FROM AUTHOR]

In light of the improvements to the capacity and timeliness of heavy-haul railway transportation that can be organized through group trains originating at a technical station, we address a group train operation scheduling problem with freight demand importance via a newly proposed mixed integer programming model and a simulated annealing algorithm. The optimization objective of the mixed integer programming model is to minimize the weighted sum of the transportation cost and the total cargo travel time under the condition of matching freight supply and demand within the optimization period. The main constraints are extracted from the supply and demand relations, the cargo delivery time commitment, the maintenance time, and the number of locomotives. A simulated annealing algorithm was constructed to generate the grouping scheme, the stopping scheme and the running schedule of group trains. A numerical experiment based on a real heavy-haul railway configuration was employed to verify the efficacy of the proposed model and heuristics algorithm. The results show that the proposed methodology can achieve high-quality solutions. The case results reveal that the freight volume increased by 2.03%, the departure cost decreased by CNY 337,000, the transportation cost which results from the difference in the supply and demand matching increased by CNY 27,764, and the total cargo travel time decreased by 40.9%, indicating that group train operation can create benefits for both railway enterprises and customers. [ABSTRACT FROM AUTHOR]