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Locating a bioenergy facility using a hybrid optimization method

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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 http://hdl.handle.net/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


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