Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems

Mavrotas, G

dc.contributor.author	Mavrotas, G	en
dc.date.accessioned	2014-03-01T01:30:17Z
dc.date.available	2014-03-01T01:30:17Z
dc.date.issued	2009	en
dc.identifier.issn	0096-3003	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19533
dc.subject	ε-Constraint method	en
dc.subject	GAMS	en
dc.subject	Multi-Objective Programming	en
dc.subject.classification	Mathematics, Applied	en
dc.subject.other	Apriori	en
dc.subject.other	Classification ,	en
dc.subject.other	Computational effort	en
dc.subject.other	Constraint methods	en
dc.subject.other	Decision makers	en
dc.subject.other	Final decision	en
dc.subject.other	GAMS	en
dc.subject.other	Generation method	en
dc.subject.other	Interactive approach	en
dc.subject.other	Mathematical programming problem	en
dc.subject.other	Modelling language	en
dc.subject.other	Multi objective	en
dc.subject.other	Multi-Objective Programming	en
dc.subject.other	Pareto optimal solutions	en
dc.subject.other	Pareto set	en
dc.subject.other	Posteriori	en
dc.subject.other	Whole process	en
dc.subject.other	Multiobjective optimization	en
dc.subject.other	Optimal systems	en
dc.subject.other	Pareto principle	en
dc.subject.other	Mathematical programming	en
dc.title	Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.amc.2009.03.037	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.amc.2009.03.037	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	As indicated by the most widely accepted classification, the Multi-Objective Mathematical Programming (MOMP) methods can be classified as a priori, interactive and a posteriori, according to the decision stage in which the decision maker expresses his/her preferences. Although the a priori methods are the most popular, the interactive and the a posteriori methods convey much more information to the decision maker. Especially, the a posteriori (or generation) methods give the whole picture (i.e. the Pareto set) to the decision maker, before his/her final choice, reinforcing thus, his/her confidence to the final decision. However, the generation methods are the less popular due to their computational effort and the lack of widely available software. The present work is an effort to effectively implement the epsilon-constraint method for producing the Pareto optimal solutions in a MOMP. We propose a novel version of the method (augmented epsilon-constraint method - AUGMECON) that avoids the production of weakly Pareto optimal solutions and accelerates the whole process by avoiding redundant iterations. The method AUGMECON has been implemented in GAMS, a widely used modelling language, and has already been used in some applications. Finally, an interactive approach that is based on AUGMECON and eventually results in the most preferred Pareto optimal solution is also proposed in the paper. (C) 2009 Elsevier Inc. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE INC	en
heal.journalName	Applied Mathematics and Computation	en
dc.identifier.doi	10.1016/j.amc.2009.03.037	en
dc.identifier.isi	ISI:000266271700020	en
dc.identifier.volume	213	en
dc.identifier.issue	2	en
dc.identifier.spage	455	en
dc.identifier.epage	465	en