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Optimizing resource utilization and train scheduling is essential to satisfy passengers and reduce operating costs. This study develops the train schedule under scenario-oriented stochastic conditions. The proposed approach is a multi-objective mathematical-based mixed integer linear programming (MILP) approach; the objective is to minimize the average passenger expectation and the total number of train operation cycles. The non-dominated sorting genetic algorithm (NSGA-II) has been developed with multi-crossover and multi-mutation operators, then hybrid with simulating annealing (SA) operator (NSGA-II-SA). The model with four meta-heuristic algorithms has been technically analyzed. In a case study, the train schedule in the double-track rail network of the Tehran–Mashhad railway of Iran has been compared with the golden point. Experimental results show that a proposed approach can suitably fit the problem considering important metrics with an improvement of %7.34 and %6.89 for the average passenger waiting time and the total number of train operation cycles, respectively. [ABSTRACT FROM AUTHOR]

One of the crucial tasks during the EPB shield tunnelling is estimating the optimum tunnel face pressure (FP), which ensures self-drilling safety, helps to reduce surface settlement and prevents the entire tunnel from collapsing. This study aims to propose an optimized and state-of-the-art machine learning model to predict the EPB-FP as accurately as possible. To this end, a support vector regression SVR model and six metaheuristic optimization algorithms of particle swarm optimization (PSO), grey wolf optimization (GWO), multiverse optimization (MVO), moth flame optimization (MFO), sine cosine algorithm (SCA), and social spider optimization (SSO) were developed to predict the FP in the EPB tunnelling. 250 data sets, including seven input parameters and one output parameter (FP) were utilized in the models obtained from the Tehran metro Line 3. Finally, the performance prediction of the models from high to low was SVR–PSO,SVR–GWO,SVR–MVO,SVR–MFO,SVR–SCA,SVR–SSO, and SVR with ranking scores of 55,49,45,39,37,30, and 21, respectively. Therefore, the SVR–PSO hybrid model produced the most accurate results and it was recommended to predict the FP in the EPB tunnelling. In addition, using the mutual information test, the surface load (SL) parameter was identified as the most influential parameter on the FP. This work's significance is that it allows geotechnical engineers to accurately estimate the FP during the EPB tunnelling, which ensures the safety of the excavation itself, helps to minimize surface settlement, and ultimately prevents the collapse of the entire tunnel. Also, it can prevent the time-consuming and cost overruns that the FP may cause during the EPB tunnelling. Highlights: Improve the SVR ability through meta-heuristic optimization for low data. Develop six hybrid meta-heuristic algorithms to predict the tunnel face pressure. High accuracy in the prediction of face pressure during EPB tunnelling. Sensitivity analysis of the input parameters using mutual information test Recognition of the most robust model. [ABSTRACT FROM AUTHOR]