Split algorithms for multiobjective integer programming problems

We consider split algorithms that partition the objective function space into p or p−1 dimensional regions so as to search for nondominated points of multiobjective integer programming problems, where p is the number of objectives. We provide a unified approach that allows different split strategies...

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Bibliographic Details
Published in:Computers & operations research Vol. 140; p. 105673
Main Authors: Karsu, Özlem, Ulus, Firdevs
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.04.2022
Pergamon Press Inc
Subjects:
Algorithms
Computing time
Epsilon constraint scalarization
Function space
Integer programming
Multiobjective discrete optimization
Multiobjective integer programming
Operations research
Parameters
Pascoletti–Serafini scalarization
Weighted sum scalarization
Pascoletti–Serafini scalarization
Epsilon constraint scalarization
Weighted sum scalarization
Multiobjective integer programming
Multiobjective discrete optimization
ISSN:0305-0548, 0305-0548
Online Access:Get full text
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Summary:We consider split algorithms that partition the objective function space into p or p−1 dimensional regions so as to search for nondominated points of multiobjective integer programming problems, where p is the number of objectives. We provide a unified approach that allows different split strategies to be used within the same algorithmic framework with minimum change. We also suggest an effective way of making use of the information on subregions when setting the parameters of the scalarization problems used in the p-split structure. We compare the performances of variants of these algorithms both as exact algorithms and as solution approaches under time restriction, considering the fact that finding the whole set may be computationally infeasible or undesirable in practice. We demonstrate through computational experiments that while the (p−1)-split structure is superior in terms of overall computational time, the p-split structure provides significant advantage under time/cardinality limited settings in terms of representativeness, especially with adaptive parameter setting and/or a suitably chosen order for regions to be explored. •We consider split algorithms for multiobjective integer programming problems.•We suggest an effective way of making use of the information on subregions.•We compare the algorithm variants as exact approaches.•We compare the variants under time/cardinality limit in terms of representativeness.•The results show the advantage of adaptive parameter settings under time limit.
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ISSN:0305-0548
0305-0548
DOI:10.1016/j.cor.2021.105673