A two-stage stochastic optimization model for integrated tram timetable and speed control with uncertain dwell times

Li, Jiajie; Bai, Yun; Chen, Yao; Yang, Lin; Wang, Qian

doi:10.1016/j.energy.2022.125059

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…Based on the optimized running times of inter-station sectors, they adjusted departure intervals and dwelling times to reduce energy consumption. Li et al [25] developed a two-stage stochastic optimization model to reduce energy usage and travel time while optimizing tram control and timetable.…”

Section: Integrated Energy-efficient Optimizationmentioning

confidence: 99%

“…Some simplifications (e.g. a constant speed limit [25], fixed train parameters [23], and a three-stage strategy [27]) are used to reduce the difficulty of a solution, making them challenging to apply to actual lines with complex plane curves and speed limits. In addition, they have optimized the operation strategy within a given inter-station running time, which regards the inter-station running time as a continuous variable.…”

Section: Research Gapsmentioning

confidence: 99%

“…Li et al. [25] developed a two‐stage stochastic optimization model to reduce energy usage and travel time while optimizing tram control and timetable.…”

Section: Introductionmentioning

confidence: 99%

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Integrated energy‐efficient optimization for urban rail transit timetable

Deng

Chen

Duan

et al. 2023

IET Intelligent Trans Sys

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The energy‐efficient optimization of the train timetable should be carried out on the basis of meeting passenger demand and determining the train operation plan. This paper proposes an integrated energy‐efficient optimization framework for the train timetable. Firstly, two energy‐efficient optimization strategies for this problem are proposed: inter‐station running time allocation and regenerative braking energy utilization. The first strategy aims to reduce the traction energy consumption, realized by selecting the profile based on a given set of speed profiles for each inter‐station sector when ensuring a stable travel time of train routing. The second strategy is intended to fully utilize the regenerative energy feedback from braking trains, realized by setting the departure time of trains based on a small‐range headway adjustment. Then, an integrated energy‐efficient optimization model of the timetable is developed and solved using the genetic algorithm. Finally, a case study of the Guangzhou Metro Line 8 is presented to verify the effectiveness of the method. The results show that the utilization rate of regenerative braking energy in the optimized timetable increases by 8.60%, and the total energy consumption decreases by 9.52%. Regenerative energy utilized roughly doubles during peak hours and increases more than twice as much during off‐peak hours.

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Section: Integrated Energy-efficient Optimizationmentioning

confidence: 99%

Section: Research Gapsmentioning

confidence: 99%

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Integrated energy‐efficient optimization for urban rail transit timetable

Deng

Chen

Duan

et al. 2023

IET Intelligent Trans Sys

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“…The speed trajectory optimization strategy based on the dynamic programming was constructed to pass the traffic light at the green time and reach the destination with minimum energy consumption within the predetermined time interval. Li et al (2022) proposed an optimization model of two-stage stochastic for timetable and tram control to improve the reliability of Transit Signal Priority (TSP) considering uncertain dwell times. Compared with other methods, this method can reduce the energy consumption of trams and the number of stops of trams at junctions under the same travel time.…”

Section: Tram Speed Trajectory Optimizationmentioning

confidence: 99%

Energy-efficient tram speed trajectory optimization considering the influence of the traffic light

Long

et al. 2022

Front. Energy Res.

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The tram typically adopts the form of semi-independent right of way, which is affected by the traffic light at junctions. The parking and wait time of the tram due to red lights will increase the energy consumption and reduce the passenger comfort. This paper proposes a speed trajectory optimization approach for the tram without parking when encountering a red light. This approach is realized by reasonably adjusting the running time and speed of the tram between two adjacent sections bounded by the traffic light at the junction, on the premise that the total travel time of the whole section remains unchanged. In the model, the constraints of tram movement, speed limits, and line conditions are considered. Moreover, considering the influence of the traffic light, the comfort and traffic light constraints are added to establish the energy-efficient tram speed trajectory optimization model. The nonlinear constraints in the optimization model are converted to linear constraints using the segmental approximation method and adding constraints. As a result, the optimization model is reformulated into a mixed-integer linear programming model, which could be solved by a commercial solver like CPLEX. Numerical experiments are conducted with a 3,660 m long section of the tram line with the traffic light at the junction. The experimental results show that the speed trajectory optimization approach considering the influence of the traffic light proposed in this paper can reduce the total energy consumption by 23.46% and improve passenger comfort.

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“…Liu et al [24] established a multi-objective optimization model for belt grinding and applied applies a multi-objective particle swarm optimization (PSO) algorithm to obtain the Pareto optimal solution of resource allocation objectives. To improve the rationality of the tram schedule, a non-dominated sorting genetic algorithm and Gurobi solver were used to optimize the schedule [22]. Zhang et al [41] studied the kinetic model and parameter optimization for Tangkou bituminous coal.…”

mentioning

confidence: 99%

Research on particle size and consumption collaborative optimization problems of grinding process based on an enhanced Harris hawks optimization algorithm

Liu,

Dai,

Chen

et al. 2024

JIMO

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The grinding process is an essential part of iron and steel smelting. Due to the changeable ore source and the complex dynamic characteristics of the process, the grinding particle size often cannot meet the requirements, resulting in an increase in process energy and material consumption, as well as reworking. In response to the above problems, the following goal was achieved through this study: a collaborative optimization problem for minimization of the grinding particle size error, process water, amountand electricity consumption has been established, which yields the grinding particle size in real time through the use of a neural network. In order to improve the optimization performance, an enhanced Harris hawks optimization (EHHO) algorithm has been developed. EHHO strengthens the team cooperation between individuals in the algorithm initialization phase and location update phase, as well as introduces an attenuation factor in the search phase to improve the stability and accuracy of the algorithm. The effectiveness of the proposed method is demonstrated through the use of some benchmark functions and an industrial grinding process. The results show that the enhanced Harris hawks optimization algorithm has better optimization accuracy and generalizability; furthermore, it can be effectively applied to the collaborative optimization of grinding process.

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A two-stage stochastic optimization model for integrated tram timetable and speed control with uncertain dwell times

Cited by 5 publications

References 27 publications

Integrated energy‐efficient optimization for urban rail transit timetable

Integrated energy‐efficient optimization for urban rail transit timetable

Energy-efficient tram speed trajectory optimization considering the influence of the traffic light

Research on particle size and consumption collaborative optimization problems of grinding process based on an enhanced Harris hawks optimization algorithm

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