Detail publikace

Hybrid Algorithm for Network Design Problem with Uncertain Demands

Originální název

Hybrid Algorithm for Network Design Problem with Uncertain Demands

Anglický název

Hybrid Algorithm for Network Design Problem with Uncertain Demands

Jazyk

en

Originální abstrakt

The purpose of the paper is to present a hybrid algorithm to solve a transportation optimization model with random demand parameters and network design variables. At first, the classical deterministic linear transportation model with network design 0-1 variables is introduced. Then, randomness is considered for demand parameters and modeled by here-and-now approach. The obtained scenario-based model leads to a mixed integer linear program (MILP) that can be solved by common integer programming techniques, see e.g. the branch-and-bound algorithm implemented in the CPLEX solver. Such a program may reach solvability limitations of MIP algorithms for large scale real world data, so a suitable heuristic development is welcome. Therefore, the idea of combination of traditional optimization algorithm and genetic algorithm is discussed and developed. At the end, the results are illustrated and also verified for a small test instance by figures.

Anglický abstrakt

The purpose of the paper is to present a hybrid algorithm to solve a transportation optimization model with random demand parameters and network design variables. At first, the classical deterministic linear transportation model with network design 0-1 variables is introduced. Then, randomness is considered for demand parameters and modeled by here-and-now approach. The obtained scenario-based model leads to a mixed integer linear program (MILP) that can be solved by common integer programming techniques, see e.g. the branch-and-bound algorithm implemented in the CPLEX solver. Such a program may reach solvability limitations of MIP algorithms for large scale real world data, so a suitable heuristic development is welcome. Therefore, the idea of combination of traditional optimization algorithm and genetic algorithm is discussed and developed. At the end, the results are illustrated and also verified for a small test instance by figures.

BibTex


@inproceedings{BUT103257,
  author="Jan {Roupec} and Pavel {Popela} and Dušan {Hrabec} and Jan {Novotný} and Kjetil Kare {Haugen} and Asmund {Olstad}",
  title="Hybrid Algorithm for Network Design Problem with Uncertain Demands",
  annote="The purpose of the paper is to present a hybrid algorithm to solve a transportation optimization model with random demand parameters and network design variables. At first, the classical deterministic linear transportation model with network design 0-1 variables is introduced. Then, randomness is considered for demand parameters and modeled by here-and-now approach. The obtained scenario-based model leads to a mixed integer linear program (MILP) that can be solved by common integer programming techniques, see e.g. the branch-and-bound algorithm implemented in the CPLEX solver. Such a program may reach solvability limitations of MIP algorithms for large scale real world data, so a suitable heuristic development is welcome. Therefore, the idea of combination of traditional optimization algorithm and genetic algorithm is discussed and developed. At the end, the results are illustrated and also verified for a small test instance by figures.",
  booktitle="Lecture Notes in Engineering and Computer Science WCECS 2013",
  chapter="103257",
  edition="1",
  howpublished="print",
  number="1",
  year="2013",
  month="october",
  pages="554--559",
  type="conference paper"
}