Neural Networks: Implementations and Applications

Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering area

Neural Network Applications

Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering area

Using genetic algorithms with grammar encoding to generate neural networks

Kitano's approach to neural network design is extended in the sense that not just the neural network structure, but also the values of the weights are coded in the chromosome. Experimental results are presented demonstrating the capability of the technique in the solution of a standard test problem

Integrating Evolutionary Computation with Neural Networks

There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing technique

Railway Crew Rescheduling with Retiming

Railway operations are disrupted frequently, e.g. the Dutch railway network experiences about three large disruptions per day on average. In such a disrupted situation railway operators need to quickly adjust their resource schedules. Nowadays, the timetable, the rolling stock and the crew schedule are recovered in a sequential way. In this paper, we model and solve the crew rescheduling problem with retiming. This problem extends the crew rescheduling problem by the possibility to delay the departure of some trains. In this way we partly integrate timetable adjustment and crew rescheduling. The algorithm is based on column generation techniques combined with Lagrangian heuristics. In order to prevent a large increase in computational time, retiming is allowed only for a limited number of trains where it seems very promising. Computational experiments with real-life disruption data show that, compared to the classical approach, it is possible to find better solutions by using crew rescheduling with retiming.

Fewer Trains for Better Timetables: The Price of Fixed Line Frequencies in the Passenger-Oriented Timetabling Problem

This paper introduces the Passenger-Oriented Timetabling problem with flexible frequencies (POT-flex) in the context of railway planning problems. POT-flex aims at creating feasible railway timetables minimising total perceived passenger travel time. The contribution of the POT-flex lies in its relaxation of the generally adopted assumption that line frequencies should be a fixed part of the input. Instead, we consider flexible line frequencies, encompassing a minimum and maximum frequency per line, allowing the timetabling model to decide on optimal line frequencies to obtain better solutions using fewer train services per line. We develop a mixed-integer programming formulation for POT-flex based on the Passenger-Oriented Timetabling (POT) formulation of [Polinder et al., 2021] and compare the performance of the new formulation against the POT formulation on three instances. We find that POT-flex allows to find feasible timetables in instances containing bottlenecks, and show improvements of up to 2% on the largest instance tested. These improvements highlight the cost that fixed line frequencies can have on timetabling