dc.contributor.author |
Rentizelas, AA |
en |
dc.contributor.author |
Tatsiopoulos, IP |
en |
dc.date.accessioned |
2014-03-01T01:33:42Z |
|
dc.date.available |
2014-03-01T01:33:42Z |
|
dc.date.issued |
2010 |
en |
dc.identifier.issn |
0925-5273 |
en |
dc.identifier.uri |
https://dspace.lib.ntua.gr/xmlui/handle/123456789/20547 |
|
dc.subject |
Bioenergy facility |
en |
dc.subject |
Facility location |
en |
dc.subject |
Hybrid optimization |
en |
dc.subject |
OR in energy |
en |
dc.subject |
Supply chain |
en |
dc.subject.classification |
Engineering, Industrial |
en |
dc.subject.classification |
Engineering, Manufacturing |
en |
dc.subject.classification |
Operations Research & Management Science |
en |
dc.subject.other |
Bio-energy |
en |
dc.subject.other |
Bioenergy facility |
en |
dc.subject.other |
Energy applications |
en |
dc.subject.other |
Facility location |
en |
dc.subject.other |
Holistic approach |
en |
dc.subject.other |
Hybrid method |
en |
dc.subject.other |
Hybrid optimization |
en |
dc.subject.other |
Hybrid optimization method |
en |
dc.subject.other |
Investment costs |
en |
dc.subject.other |
Optimization method |
en |
dc.subject.other |
Optimization problems |
en |
dc.subject.other |
Optimum location |
en |
dc.subject.other |
OR in energy |
en |
dc.subject.other |
Sequential quadratic programming |
en |
dc.subject.other |
Specific problems |
en |
dc.subject.other |
Facilities |
en |
dc.subject.other |
Investments |
en |
dc.subject.other |
Location |
en |
dc.subject.other |
Quadratic programming |
en |
dc.subject.other |
Supply chain management |
en |
dc.subject.other |
Supply chains |
en |
dc.subject.other |
Optimization |
en |
dc.title |
Locating a bioenergy facility using a hybrid optimization method |
en |
heal.type |
journalArticle |
en |
heal.identifier.primary |
10.1016/j.ijpe.2009.08.013 |
en |
heal.identifier.secondary |
http://dx.doi.org/10.1016/j.ijpe.2009.08.013 |
en |
heal.language |
English |
en |
heal.publicationDate |
2010 |
en |
heal.abstract |
In this paper, the optimum location of a bioenergy generation facility for district energy applications is sought. A bioenergy facility usually belongs to a wider system, therefore a holistic approach is adopted to define the location that optimizes the system-wide operational and investment costs. A hybrid optimization method is employed to overcome the limitations posed by the complexity of the optimization problem. The efficiency of the hybrid method is compared to a stochastic (genetic algorithms) and an exact optimization method (Sequential Quadratic Programming). The results confirm that the hybrid optimization method proposed is the most efficient for the specific problem. (C) 2009 Elsevier B.V. All rights reserved. |
en |
heal.publisher |
ELSEVIER SCIENCE BV |
en |
heal.journalName |
International Journal of Production Economics |
en |
dc.identifier.doi |
10.1016/j.ijpe.2009.08.013 |
en |
dc.identifier.isi |
ISI:000272879100018 |
en |
dc.identifier.volume |
123 |
en |
dc.identifier.issue |
1 |
en |
dc.identifier.spage |
196 |
en |
dc.identifier.epage |
209 |
en |