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Rescheduling disrupted railway traffic is computationally hard even for small problem instances.
Using the appropriate programming model, it is shown that excellent performance scaling can be obtained even for small problem sizes.
Our experimental analysis on several test function classes shows advantages already for small problem sizes and broad parameter ranges.
The first is an exact algorithm based on dynamic programming that is suitable for small problem instances.
Then, for small, Problem 3 has a unique solution in with and.
This might explain why a black-box heuristic solver is able to outperform a problem-specific genetic algorithm for small problem instances.
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Stochastic dynamic programming is used to find the optimal schedule of resource allocation for small problems but is intractable for large problems owing to the "curse of dimensionality 8.
We first present a new simple integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems.
Computational results indicate that, for small problems, the average optimality gaps are less than 10.9% and 13.4% using linear and exponential lost sale functions, respectively.
The results highlighted two specific regions: the left angular gyrus was more activated for low interfering than for high interfering problems, and the right intraparietal sulcus was more activated for large problems than for small problems.
Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com