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A heuristic approach to the facility location problem for waste management: A case study

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dc.title A heuristic approach to the facility location problem for waste management: A case study en
dc.contributor.author Hrabec, Dušan
dc.contributor.author Viktorin, Adam
dc.contributor.author Šomplák, Radovan
dc.contributor.author Pluháček, Michal
dc.contributor.author Popela, Pavel
dc.relation.ispartof MENDEL 2016
dc.identifier.issn 1803-3814 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-802145365-4
dc.date.issued 2016
dc.citation.spage 61
dc.citation.epage 66
dc.event.title 22nd International Conference on Soft Computing: Evolutionary Computation, Genetic Programming, Swarm Intelligence, Fuzzy Logic, Neural Networks, Chaos, Bayesian Methods, Intelligent Image Processing, Bio-Inspired Robotics, MENDEL 2016
dc.event.location Brno
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2016-06-08
dc.event.edate 2016-06-10
dc.type conferenceObject
dc.language.iso en
dc.publisher Brno University of Technology
dc.subject Differential evolution en
dc.subject Facility location en
dc.subject Heuristic algorithm en
dc.subject Network flow en
dc.subject Waste management en
dc.description.abstract The aim of this paper is to develop a model for the waste processing facility location problem in selected regions of the Czech Republic and to test a heuristic algorithm that would provide an effective solution to similar larger problems. Within this context, it is understood that the facility location decisions are made on the basis of the minimization of the total costs. The total costs include waste collection costs, facility location investments (or waste treatment costs) and penalty costs for underutilized capacity. Two modelled problems are illustrated on the basis of two examples of limited size. In order to determine whether similar, larger problems can be solved e.g. covering the whole of the Czech Republic, a heuristic algorithm is put forward and test computations performed. The algorithm used in this paper is a novel variant of Success-History Based Parameter Adaptation for Differential Evolution which implements Multi-Chaotic parent selection (MC-SHADE). en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007118
utb.identifier.obdid 43875702
utb.identifier.scopus 2-s2.0-85014950825
utb.source d-scopus
dc.date.accessioned 2017-08-01T08:27:13Z
dc.date.available 2017-08-01T08:27:13Z
dc.description.sponsorship LO1202, MEYS, Ministry of Education, Youth and Science
utb.contributor.internalauthor Hrabec, Dušan
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Pluháček, Michal
utb.fulltext.affiliation Dušan Hrabec a∗ , Adam Viktorín a , Radovan Šomplák b , Michal Pluháček a , Pavel Popela b a Tomas Bata University in Zlín, Faculty of Applied Informatics, Nad Stráněmi 4511, 760 05 Zlín, Czech Republic b Brno University of Technology, Faculty of Mechanical Engineering, Technická 2, 616 69 Brno, Czech Republic ∗ corresponding author: hrabec@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.sponsorship This work was supported by the EEA Programme and Norway Grants under Institutional cooperation project No. NF-CZ07-ICP-4-345-2016 and by the Internal Grant Agency of Tomas Bata University under Project No. IGA/CebiaTech/2016/007. The authors also gratefully acknowledge the financial support provided by MEYS under the National Sustainability Programme I (Project LO1202).
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