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Pedro José Correia Duarte

Consequently, infrastructure upgrade decisions must be strategically planned over time to maximize societal benefits. We introduce a new model aimed at optimizing the phased deployment of infrastructure upgrades in a railway network operating a given cyclic timetable structure. We propose a Mixed Integer Linear Programming formulation and a greedy heuristic aimed at selecting infrastructure upgrades based on their expected cost effectiveness. A detailed case study on the main Dutch railway network reveals that significant gains can be achieved using timetables as evaluation tools for infrastructure upgrade deployment decisions over multiple periods. We further provide insights on the cost of additional infrastructure deployment constraints, such as ensuring regional equity requirements and connectivity.

Rick Willemsen

Generating Random Vectors satisfying Linear and Nonlinear Constraints

We consider the problem of generating n-dimensional vectors with a fixed sum, with the goal of generating a uniform distribution of vectors over a valid region. This means that each possible vector has an equal probability of being generated. The Dirichlet-Rescale (DRS) algorithm aims to generate a uniform distribution of vectors with fixed sum that satisfies lower and upper bounds on the individual entries. However, we demonstrate that the uniform distribution property of the DRS algorithm does not hold in general. Using an analytical procedure and a statistical test, we show that the vectors generated by the DRS algorithm do not appear to be drawn from a uniform distribution. To resolve this issue, we propose the Dirichlet-Rescale-Constraints (DRSC) algorithm, which handles more general constraints, including both linear and nonlinear constraints, while ensuring that the vectors are drawn from a uniform distribution. In our computational experiments we demonstrate the effectiveness of the DRSC algorithm.

Coordinators

Ruben van Beesten, , Ece Karakoyun