Optimal pacing of trains in freight railroads: model formulation and solution
Operations Research
Optimal strategies for the control of a train
Automatica (Journal of IFAC)
Dynamic coast control of train movement with genetic algorithm
International Journal of Systems Science
Fuzzy decision making of profit function in production planning using S-curve membership function
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
Brief paper: Local energy minimization in optimal train control
Automatica (Journal of IFAC)
Train timetable problem on a single-line railway with fuzzy passenger demand
IEEE Transactions on Fuzzy Systems
An approximation approach for representing S-shaped membership functions
IEEE Transactions on Fuzzy Systems
Automatic train control system development and simulation for high-speed railways
IEEE Circuits and Systems Magazine
Intelligent timetable evaluation using fuzzy AHP
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Engineering Applications of Artificial Intelligence
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Nowadays one of the main priorities for railways administrations and operators is the reduction of energy consumption, due to its impact on CO"2 emissions and economic costs. This is especially important on high speed lines, in expansion in many countries, given that very high levels of consumption are involved. Energy saving strategies focused on traffic operation can be applied in the short term with low levels of investment, in particular ecodriving, timetable design and the on line regulation of trains. However approaches in the literature to minimize energy do not normally consider specific models for manual driving in high speed lines and the commercial punctuality constraints of this type of services, and do not take into account the uncertainty associated with manual driving. The aim of this paper is the on line regulation of high speed trains recalculating the energy efficient manual driving to be executed by the driver when significant delays arise. The manual driving is modeled by means of fuzzy parameters: the speed regulation and the response time of the driver when a new driving command has to be applied. The punctuality requirement of the railway operator is represented as a necessity fuzzy measure of punctual arrival at stations. The proposed method for the on line recalculation of efficient driving is a Genetic Algorithm with fuzzy parameters based on an accurate simulation of the train motion. This algorithm is applied on a real Spanish high speed line to assess the energy savings provided by the efficient regulation algorithm compared to the typical driving style that is applied when a train has to get back on schedule after a delay.