A Minimisation of Network Covering Services in a Threshold Distance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F15%3A43908504" target="_blank" >RIV/62156489:43110/15:43908504 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-19824-8_13" target="_blank" >http://dx.doi.org/10.1007/978-3-319-19824-8_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-19824-8_13" target="_blank" >10.1007/978-3-319-19824-8_13</a>

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

MENDEL 2015: Recent Advances in Soft Computing

ISBN

978-3-319-19823-1

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

11

Strana od-do

159-169

Název nakladatele

Springer Switzerland

Místo vydání

Cham

Místo konání akce

Brno

Datum konání akce

23. 6. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000364847700013