The Minimisation of Public Facilities With Enhanced Genetic Algorithms Using War Elimination

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU130916" target="_blank" >RIV/00216305:26220/19:PU130916 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8605307" target="_blank" >https://ieeexplore.ieee.org/document/8605307</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2019.2891424" target="_blank" >10.1109/ACCESS.2019.2891424</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Minimisation of Public Facilities With Enhanced Genetic Algorithms Using War Elimination

Popis výsledku v původním jazyce

In this paper, we focus on the problem of minimising a network of state facilities that provide essential public services (schools, offices, and hospitals). The goal is to reduce the size of the network in order to minimise the costs associated with it. However, it is essential that every customer should be able to access an appropriate service centre within a reachable distance. This problem can arise in various scenarios such as a government cutting back on public service spending in remote areas or as a reaction to changing demographics (population increase/decrease). In general, this task is NP-hard which makes the problem particularly hard to scale. Therefore, for larger problems, heuristic methods must be employed to find an approximation of the optimum. To solve this problem with satisfactory results, we have presented an enhanced version of the Genetic Algorithm (GA) based on war elimination and migration operations. This modification overcomes the well-known shortcoming of GAs when the population becomes gradually more and more similar, this results in a diversity decrease which in turn leads to a sub-optimal local minimum. We test the performance of the novel algorithm against the standard heuristic benchmarks on the widely accepted Beasley OR-library dataset for optimisation problems. Finally, we provide a case study based on real-data where a municipality tries to minimise the number of schools in a region while satisfying accessibility and other region-specific constraints.

Název v anglickém jazyce

The Minimisation of Public Facilities With Enhanced Genetic Algorithms Using War Elimination

Popis výsledku anglicky

In this paper, we focus on the problem of minimising a network of state facilities that provide essential public services (schools, offices, and hospitals). The goal is to reduce the size of the network in order to minimise the costs associated with it. However, it is essential that every customer should be able to access an appropriate service centre within a reachable distance. This problem can arise in various scenarios such as a government cutting back on public service spending in remote areas or as a reaction to changing demographics (population increase/decrease). In general, this task is NP-hard which makes the problem particularly hard to scale. Therefore, for larger problems, heuristic methods must be employed to find an approximation of the optimum. To solve this problem with satisfactory results, we have presented an enhanced version of the Genetic Algorithm (GA) based on war elimination and migration operations. This modification overcomes the well-known shortcoming of GAs when the population becomes gradually more and more similar, this results in a diversity decrease which in turn leads to a sub-optimal local minimum. We test the performance of the novel algorithm against the standard heuristic benchmarks on the widely accepted Beasley OR-library dataset for optimisation problems. Finally, we provide a case study based on real-data where a municipality tries to minimise the number of schools in a region while satisfying accessibility and other region-specific constraints.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

7

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

9395-9405

Kód UT WoS článku

000457957000001

EID výsledku v databázi Scopus

2-s2.0-85061206702