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The rapid development of metro transit systems brings very significant energy consumption, and the high service frequency of metro trains increases the peak power requirement, which affects the operation of systems. Train scheduling optimization is an effective method to reduce energy consumption and substation peak power by adjusting timetable parameters. This paper proposes a train timetable optimization model to coordinate the operation of trains. The overlap time between accelerating and braking phases is maximized to improve the utilization of regenerative braking energy (RBE). Meanwhile, the overlap time between accelerating phases is minimized to reduce the substation peak power. In addition, the timetable optimization model is rebuilt into a mixed integer linear programming model by introducing logical and auxiliary variables, which can be solved by related solvers effectively. Case studies based on one of Guangzhou Metro Lines indicate that, for all-day operation, the utilization of RBE would likely be improved on the order of 23%, the substation energy consumption would likely be reduced on the order of 14%, and the duration of substation peak power would likely be reduced on the order of 66%.

Algorithms for current automatic train operation (ATO) focus mainly on reducing the mechanical energy of motion for a single train within an existing timetable. However, the reuse of regenerative energy is another factor that contributes to energy consumption and conservation in multitrain networks. To improve regenerative energy receptivity and energy savings in a bidirectional metro transit network, this study formulated a coordinated train control algorithm that was based on genetic algorithm techniques. The energy saving potential of different station departure time intervals between two opposing trains (synchronization time) was tested. Simulation on the Visual C++ platform demonstrated that the algorithm could provide an optimal train speed profile with better energy performance while also satisfying operational constraints. Different synchronization times have different optimization ratios. This research was another step to facilitate the development of an ATO control algorithm that considers overall energy consumption. Increased knowledge of the influence of synchronization time at stations on energy consumption in regenerative multitrain networks will also aid in the design of more energy-efficient timetables.

The International Maritime Organization (IMO) is enforcing increasingly stringent regulations on ship carbon emissions, The International Maritime Organization (IMO) has been implementing progressively strict regulations on ship carbon emissions, leading to the adoption of the virtual arrival (VA) method by many vessels to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through Virtual Arrival and simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.