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

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 a...

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Vydáno v:Applied mathematics and computation Ročník 213; číslo 2; s. 455 - 465
Hlavní autor: Mavrotas, George
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier Inc 15.07.2009
Elsevier
Témata:
Calculus of variations and optimal control
Exact sciences and technology
GAMS
Mathematical analysis
Mathematics
Multi-Objective Programming
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Sciences and techniques of general use
ε-Constraint method
ε-Constraint method
GAMS
Multi-Objective Programming
Optimization method
Iteration
Mathematical method
Implementation
Numerical analysis
Applied mathematics
Optimal solution
Classification
Software
Mathematical programming
ISSN:0096-3003, 1873-5649
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Popis
Shrnutí: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 ε-constraint method for producing the Pareto optimal solutions in a MOMP. We propose a novel version of the method (augmented ε-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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2009.03.037