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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Vydáno v:Optimization letters Ročník 13; číslo 2; s. 281 - 294
Hlavní autoři: Naghavi, Mostafa, Foroughi, Ali Asghar, Zarepisheh, Masoud
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2019
Témata:
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
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1862-4472
1862-4480
DOI:10.1007/s11590-019-01394-0