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A hybrid approach for biobjective optimization

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A hybrid approach for biobjective optimization. / Stidsen, Thomas; Andersen, Kim Allan.

In: Discrete Optimization, Vol. 28, No. May, 2018, p. 89-114.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Stidsen, T & Andersen, KA 2018, 'A hybrid approach for biobjective optimization', Discrete Optimization, vol. 28, no. May, pp. 89-114. https://doi.org/10.1016/j.disopt.2018.02.001

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Stidsen, Thomas ; Andersen, Kim Allan. / A hybrid approach for biobjective optimization. In: Discrete Optimization. 2018 ; Vol. 28, No. May. pp. 89-114.

Bibtex

@article{107469667a6b417eb23fa989d0560ae9,
title = "A hybrid approach for biobjective optimization",
abstract = "A large number of the real world planning problems which are today solved using Operations Research methods are actually multiobjective planning problems, but most of them are solved using singleobjective methods. The reason for converting, i.e. simplifying, multiobjective problems to singleobjective problems is that no standard multiobjective solvers exist and specialized algorithms need to be programmed from scratch.In this article we will present a hybrid approach, which operates both in decision space and in objective space. The approach enables massive efficient parallelization and can be used to a wide variety of biobjective Mixed Integer Programming models. We test the approach on the biobjective extension of the classic traveling salesman problem, on the standard datasets, and determine the full set of nondominated points. This has only been done once before (Florios and Mavrotas, 2014), and in our approach we do it in a fraction of the time.",
keywords = "BOUND ALGORITHM, Biobjective optimization, Branch-and-cut algorithm, CUT ALGORITHM, EPSILON-CONSTRAINT METHOD, INTEGER PROGRAMS, LOCAL SEARCH, MULTIOBJECTIVE COMBINATORIAL OPTIMIZATION, Mixed integer programming, PPM, PROGRAMMING-PROBLEMS, TRAVELING SALESMAN PROBLEM, Traveling salesman problem",
author = "Thomas Stidsen and Andersen, {Kim Allan}",
year = "2018",
doi = "10.1016/j.disopt.2018.02.001",
language = "English",
volume = "28",
pages = "89--114",
journal = "Discrete Optimization",
issn = "1572-5286",
publisher = "Elsevier BV",
number = "May",

}

RIS

TY - JOUR

T1 - A hybrid approach for biobjective optimization

AU - Stidsen, Thomas

AU - Andersen, Kim Allan

PY - 2018

Y1 - 2018

N2 - A large number of the real world planning problems which are today solved using Operations Research methods are actually multiobjective planning problems, but most of them are solved using singleobjective methods. The reason for converting, i.e. simplifying, multiobjective problems to singleobjective problems is that no standard multiobjective solvers exist and specialized algorithms need to be programmed from scratch.In this article we will present a hybrid approach, which operates both in decision space and in objective space. The approach enables massive efficient parallelization and can be used to a wide variety of biobjective Mixed Integer Programming models. We test the approach on the biobjective extension of the classic traveling salesman problem, on the standard datasets, and determine the full set of nondominated points. This has only been done once before (Florios and Mavrotas, 2014), and in our approach we do it in a fraction of the time.

AB - A large number of the real world planning problems which are today solved using Operations Research methods are actually multiobjective planning problems, but most of them are solved using singleobjective methods. The reason for converting, i.e. simplifying, multiobjective problems to singleobjective problems is that no standard multiobjective solvers exist and specialized algorithms need to be programmed from scratch.In this article we will present a hybrid approach, which operates both in decision space and in objective space. The approach enables massive efficient parallelization and can be used to a wide variety of biobjective Mixed Integer Programming models. We test the approach on the biobjective extension of the classic traveling salesman problem, on the standard datasets, and determine the full set of nondominated points. This has only been done once before (Florios and Mavrotas, 2014), and in our approach we do it in a fraction of the time.

KW - BOUND ALGORITHM

KW - Biobjective optimization

KW - Branch-and-cut algorithm

KW - CUT ALGORITHM

KW - EPSILON-CONSTRAINT METHOD

KW - INTEGER PROGRAMS

KW - LOCAL SEARCH

KW - MULTIOBJECTIVE COMBINATORIAL OPTIMIZATION

KW - Mixed integer programming

KW - PPM

KW - PROGRAMMING-PROBLEMS

KW - TRAVELING SALESMAN PROBLEM

KW - Traveling salesman problem

UR - http://www.scopus.com/inward/record.url?scp=85042283024&partnerID=8YFLogxK

U2 - 10.1016/j.disopt.2018.02.001

DO - 10.1016/j.disopt.2018.02.001

M3 - Journal article

VL - 28

SP - 89

EP - 114

JO - Discrete Optimization

JF - Discrete Optimization

SN - 1572-5286

IS - May

ER -