Applications of Stochastic Mixed-Integer Second-Order Cone Optimization

Second-order cone programming problems are a tractable subclass of convex optimization problems that can be solved using polynomial algorithms. In the last decade, stochastic second-order cone programming problems have been studied, and efficient algorithms for solving them have been developed. The...

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Vydané v:IEEE access Ročník 10; s. 3522 - 3547
Hlavní autori: Alzalg, Baha, Alioui, Hadjer
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
applications
Computational geometry
Convex functions
Convexity
Mixed integer
Mixed integer linear programming
mixed-integer programming
Optimization
Polynomials
Programming
Second-order cone programming
Stochastic processes
stochastic programming
Uncertainty
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí:Second-order cone programming problems are a tractable subclass of convex optimization problems that can be solved using polynomial algorithms. In the last decade, stochastic second-order cone programming problems have been studied, and efficient algorithms for solving them have been developed. The mixed-integer version of these problems is a new class of interest to the optimization community and practitioners, in which certain variables are required to be integers. In this paper, we describe five applications that lead to stochastic mixed-integer second-order cone programming problems. Additionally, we present solution algorithms for solving stochastic mixed-integer second-order cone programming using cuts and relaxations by combining existing algorithms for stochastic second-order cone programming with extensions of mixed-integer second-order cone programming. The applications, which are the focus of this paper, include facility location, portfolio optimization, uncapacitated inventory, battery swapping stations, and berth allocation planning. Considering the fact that mixed-integer programs are usually known to be NP-hard, bringing applications to the surface can detect tractable special cases and inspire for further algorithmic improvements in the future.
Bibliografia:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3139915