Hybrid Algorithm for Network Design Problem with Uncertain Demands

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Hybrid Algorithm for Network Design Problem with Uncertain Demands

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Hybrid Algorithm for Network Design Problem with Uncertain Demands

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Engineering and Computer Science WCECS 2013

ISBN

978-988-19252-3-7

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

554-559

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

San Francisco

Datum konání akce

23. 10. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000335870200105