Inverse optimization for multi-objective linear programming

This paper generalizes inverse optimization for multi-objective linear programming where we are looking for the least problem modifications to make a given feasible solution a weak efficient solution. This is a natural extension of inverse optimization for single-objective linear programming with re...

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Veröffentlicht in:Optimization letters Jg. 13; H. 2; S. 281 - 294
Hauptverfasser: Naghavi, Mostafa, Foroughi, Ali Asghar, Zarepisheh, Masoud
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2019
Schlagworte:
Computational Intelligence
Mathematics
Mathematics and Statistics
Numerical and Computational Physics
Operations Research/Decision Theory
Optimization
Original Paper
Simulation
Linear programming
Inverse optimization
Efficiency
Multi-objective linear programming
ISSN:1862-4472, 1862-4480
Online-Zugang:Volltext
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Zusammenfassung:This paper generalizes inverse optimization for multi-objective linear programming where we are looking for the least problem modifications to make a given feasible solution a weak efficient solution. This is a natural extension of inverse optimization for single-objective linear programming with regular “optimality” replaced by the “Pareto optimality”. This extension, however, leads to a non-convex optimization problem. We prove some special characteristics of the problem, allowing us to solve the non-convex problem by solving a series of convex problems.
Bibliographie:ObjectType-Article-1
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ISSN:1862-4472
1862-4480
DOI:10.1007/s11590-019-01394-0