Optimization of railway operations using neural networks

Martinelli, Dante Pinheiro; Teng, Hualiang

doi:10.1016/0968-090x(95)00019-f

Cited by 56 publications

(22 citation statements)

References 4 publications

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“…Other authors developed the computer-aided dispatch systems (Petersen et al 1986;Jovanovic and Harker 1991). In order to solve some complex problems, researchers have devised various algorithms for optimizing the train routing, such as the heuristic method based on Lagrangian relaxation (Keaton 1989), the combination of genetic and tabu search algorithms (Gorman 1998a), expert system (Lozano et al 2002 and neural network (Martinelli and Teng 1996). However, with the time constraints for real-time optimization and the enormous size of the most railway networks, none of the existing methods provide ideal results (Blum and Eskandarian 2002).…”

Section: Introductionmentioning

confidence: 99%

Optimizing Train Network Routing Using Deterministic Search

Gao

Mao

et al. 2009

Netw Spat Econ

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Section: Introductionmentioning

confidence: 99%

Optimizing Train Network Routing Using Deterministic Search

Gao

Mao

et al. 2009

Netw Spat Econ

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“…Work in connection with the modeling and control of railway networks in a systems and control context can be found in [2,3,7,12]. We also refer the interested reader to [9,10,11,13] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Approach for Recovery from Delays in Railway Systems

Schutter

Boom

Hegyi

2002

Transportation Research Record

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We extend the model predictive control (MPC) framework, which is a very popular controller design method in the process industry, to transfer coordination in railway systems. In fact, the proposed approach can also be used for other systems with both hard and soft synchronization constraints, such as logistic operations. The main aim of the control is to recover from delays in an optimal way by breaking connections (at a cost). In general, the MPC control design problem for railway systems leads to a nonlinear non-convex optimization problem. We show that the optimal MPC strategy can also be computed using an extended linear complementarity problem. Furthermore, we present an extension with an extra degree of freedom to recover from delays by letting some trains run faster than usual (again at a cost). The resulting extended MPC railway problem can also be solved using an extended linear complementarity problem.

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“…In [3] authors propose a hybrid scheme with the Hopfield's network and some heuristic methods to create timetable of CPU load. In [4] authors solve some transport problem by the multilayer perceptron with one hidden layer. All these works are not dedicated to creating the timetable with complex analysis of transport constraints.…”

Section: Introductionmentioning

confidence: 99%

One approach to control of a neural network with variable signal conductivity

Olshansky¹,

Ignatenkov²

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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The article focuses on a new extent to synthesize a control strategy of learning of a special artificial neural network with variable signal conductivity and on features of neural networks with variable signal conductivity. The purpose of the research is to create a new control strategy based on analysis of the neural network error signal. Also the article contains an approach how to compute the control strategy. The main result is contained in the necessity to realize the control strategy on the basis of preliminary training of the neural network with specific techniques. Authors also suggest a technique of computing the crucial trajectory of the network's error decreasing. The trajectory may be computed using signal processing methods.

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Optimization of railway operations using neural networks

Cited by 56 publications

References 4 publications

Optimizing Train Network Routing Using Deterministic Search

Optimizing Train Network Routing Using Deterministic Search

Model Predictive Control Approach for Recovery from Delays in Railway Systems

One approach to control of a neural network with variable signal conductivity

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