Abstract
The transportation network design problem is a well-known optimization problem with many practical applications. This paper deals with demand-based applications, where the operational as well as many other decisions are often made under uncertainty. Capturing the uncertain demand by using scenario-based approach, we formulate the two-stage stochastic mixed-integer linear problem, where the decision, which is made under uncertainty, of the first-stage program, is followed by the second-stage decision that reacts to the observed demand. Such a program may reach solvability limitations of algorithms for large scale real world data, so we refer to the so-called hybrid algorithm that combines a traditional optimization algorithm and a suitable genetic algorithm. The obtained results are presented in an explanatory form with the use of a sequence of figures.
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Acknowledgments
The present work has been supported by the specific research project “Modern Methods of Applied Mathematics for the Use in Technical Sciences”, no. FSI-S-14-2290, id. code 25053. We would like to acknowledge the help of Petr Jindra with the visualization of achieved results.
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Hrabec, D., Popela, P., Roupec, J., Mazal, J., Stodola, P. (2015). Two-Stage Stochastic Programming for Transportation Network Design Problem. In: Matoušek, R. (eds) Mendel 2015. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-319-19824-8_2
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DOI: https://doi.org/10.1007/978-3-319-19824-8_2
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