Optimal pacing of trains in freight railroads: model formulation and solution
Operations Research
Modelling the number and location of sidings on a single line railway
Computers and Operations Research
Distributed search in railway scheduling problems
Engineering Applications of Artificial Intelligence
The First Optimized Railway Timetable in Practice
Transportation Science
Engineering Applications of Artificial Intelligence
Train timetable problem on a single-line railway with fuzzy passenger demand
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Intelligent Transportation Systems
Type-2 fuzzy logic based urban traffic management
Engineering Applications of Artificial Intelligence
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
Engineering Applications of Artificial Intelligence
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Energy efficiency is an important concern in for railway administrations and operators. Strategies focused on traffic operation can achieve energy savings in short term and with associated low investments. For that purpose the main strategies are the design of efficient timetables and driving (ecodriving). The ecodriving applies coasting commands (null traction force) to reduce energy consumption, taking into account downhill slopes, speed reductions, etc. (Acikbas and Soylemez, 2008). However, timetable models in literature do not typically consider energy minimization as a goal, and punctuality requirements under uncertainty. In this paper a model for the joint design of ecodriving and timetable under uncertainty for high speed lines is proposed where the railway operator and administrator requirements are incorporated. Uncertainty in delays is modeled as fuzzy numbers and punctuality constraints, and the timetable optimization model is a fuzzy linear programming model, in which the objective function includes the consumptions of delayed scenarios and the behavioral response of the driver that will affect the consumption. The ecodriving design is based on a Genetic Algorithm that makes use of a detailed simulation model, taking into account the specific characteristics of high speed lines and trains. The proposed method is applied to a real Spanish high speed line to optimize the operation and it is compared to the current commercial service in order to evaluate the potential energy savings.