A Minimisation of Network Covering Services in a Threshold Distance
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F15%3APU116694" target="_blank" >RIV/00216305:26210/15:PU116694 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A Minimisation of Network Covering Services in a Threshold Distance
Popis výsledku v původním jazyce
In this paper, we deal with a special version of the set covering problem, which consists in finding the minimum number of service centres providing specialized services for all customers (or larger units, such as urban areas) within a reasonable distance given by a threshold. If a suitable threshold is found that makes it possible to determine a feasible solution of the task, the task is transformed into a general set covering problem. However, this has a combinatorial nature and, because it belongs tothe class of NP-hard problems, for a large instance of the problem, it cannot be used to find the optimal solution in a reasonable amount of time. In the paper, we present a solution by means of two stochastic heuristic methods - genetic algorithms andsimulated annealing ? and, using a test instance from OR-Library, we present the parameter settings of both methods and the results achieved.
Název v anglickém jazyce
A Minimisation of Network Covering Services in a Threshold Distance
Popis výsledku anglicky
In this paper, we deal with a special version of the set covering problem, which consists in finding the minimum number of service centres providing specialized services for all customers (or larger units, such as urban areas) within a reasonable distance given by a threshold. If a suitable threshold is found that makes it possible to determine a feasible solution of the task, the task is transformed into a general set covering problem. However, this has a combinatorial nature and, because it belongs tothe class of NP-hard problems, for a large instance of the problem, it cannot be used to find the optimal solution in a reasonable amount of time. In the paper, we present a solution by means of two stochastic heuristic methods - genetic algorithms andsimulated annealing ? and, using a test instance from OR-Library, we present the parameter settings of both methods and the results achieved.
Klasifikace
Druh
C - Kapitola v odborné knize
CEP obor
BB - Aplikovaná statistika, operační výzkum
OECD FORD obor
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Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2015
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název knihy nebo sborníku
Recent Advances in Soft Computing
ISBN
978-3-319-19823-1
Počet stran výsledku
11
Strana od-do
159-169
Počet stran knihy
388
Název nakladatele
Springer
Místo vydání
Heidelberg, Germany
Kód UT WoS kapitoly
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