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Multi-agent systems for energy efficient train and train station interaction modelling
Published in Konstantinos Papadikis, Chee S. Chin, Isaac Galobardes, Guobin Gong, Fangyu Guo, Sustainable Buildings and Structures: Building a Sustainable Tomorrow, 2019
In addition to passenger density data, developing proper algorithms are essential for EETT optimization. Station sequential model, train sequential model and space-time sequential model are three basic traffic models used to analyze train dynamic problems (Campion et al. 1985).The schedule of metro transportation contains relatively complex relations which is supposed to use approximate decomposition techniques to figure out an result that closes to optimized schedule (Kroon et al. 2013), station and train model could solve the problem by considering series of stations. In 2005, Kwan & Chang (2005) proposed to minimize the total energy consumption and passengers by a multi-objective evolutionary model. Carey & Crawford (2007) generated a multi-Station and multi-line model with heuristic-based algorithms to optimize the train schedule for busy metro system. In 2010, Cacchiani et al. (2010) raised the train circulation problem could be solved by integer linear programming with the space-time model, it is efficient to generate a schedule for train unit assignment. Furthermore, Cacchiani et al. (2016) applied a mixed integer programming (MIP) for coordinating the departure time for each train at each station, which take the headway as the control variable. Moreover, the most common train schedule is certain time headway for applications, the irregular headway schedule is more reasonable because it is more effective and efficient to improve the traffic environment (Cacchiani et al. 2016). In 2019, the multi-agent algorithm proposed by Guo et al. 2019(a) & (b) established a direct connection between trains, and the station can obtain the estimation of the real-time arrival time of trains through the direct connection between trains and stations. These algorithms are summarized in Table 2.
Network Models and the Network Simplex Algorithm
Published in Craig A. Tovey, Linear Optimization and Duality, 2020
The minimum cost circulation problem is the minimum cost flow problem restricted to cases in which si=0∀i∈V. This restriction is inconsequential. Any minimum cost flow instance can be converted to an equivalent circulation instance as follows: create a new node t; for all v∈V such that sv>0, create an arc (t,v) with ltv=utv=sv and zero cost ctv=0; for all v∈V such that sv<0, create an arc (v,t) with lvt=uvt=−sv and zero cost cvt=0; set sv=0∀v∈V∪{t}. Figure 11.7 illustrates the transformation.
An intelligent simulation platform for train traffic control under disturbance
Published in International Journal of Modelling and Simulation, 2019
Masoud Shakibayifar, Erfan Hassannayebi, Hamid Mirzahossein, Firouzeh Taghikhah, Amir Jafarpur
Giacco et al. [41] proposed mixed-integer linear programming (MILP) formulation for rolling stock circulation problem under maintenance constraints. The model could result in an optimized rostering plan with minimal cost. The real-world instances of the problem, adopted from Italian railway company, were solved using a commercial MILP solver in reasonable computation time. The outcomes verified that the combined rolling stock and maintenance planning model could reduce the fleet size and deadhead runs in comparison with pure rolling stock circulation models. Larsen et al. [42] presented a stochastic model for stability analysis of the train timetables generated by a microscopic scheduling model. The designed model can obtain the robustness of several train rescheduling procedures to fluctuations in the running and dwell times. The validity of the model was confirmed by testing the instances from Dutch railway system. The experimental result indicated that the advanced branch and bound algorithm (B&B) outperforms the traditional FCFS dispatching rule both in deterministic and in random traffic condition.