Locating a bioenergy facility using a hybrid optimization method

Rentizelas, AA; Tatsiopoulos, IP

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