Disjunctive programming for multiobjective discrete optimisation
Disjunctive programming for multiobjective discrete optimisation
In this paper, I view and present the multiobjective discrete optimisation problem as a particular case of disjunctive programming where one seeks to identify efficient solutions from within a disjunction formed by a set of systems. The proposed approach lends itself to a simple yet effective iterative algorithm that is able to yield the set of all nondominated points, both supported and nonsupported, for a multiobjective discrete optimisation problem. Each iteration of the algorithm is a series of feasibility checks and requires only one formulation to be solved to optimality that has the same number of integer variables as that of the single objective formulation of the problem. The application of the algorithm show that it is particularly effective, and superior to the state-of-the-art, when solving constrained multiobjective discrete optimisation problem instances.
University of Southampton
Bektas, T.
0db10084-e51c-41e5-a3c6-417e0d08dac9
18 May 2016
Bektas, T.
0db10084-e51c-41e5-a3c6-417e0d08dac9
Bektas, T.
(2016)
Disjunctive programming for multiobjective discrete optimisation
Southampton, GB.
University of Southampton
20pp.
Record type:
Monograph
(Project Report)
Abstract
In this paper, I view and present the multiobjective discrete optimisation problem as a particular case of disjunctive programming where one seeks to identify efficient solutions from within a disjunction formed by a set of systems. The proposed approach lends itself to a simple yet effective iterative algorithm that is able to yield the set of all nondominated points, both supported and nonsupported, for a multiobjective discrete optimisation problem. Each iteration of the algorithm is a series of feasibility checks and requires only one formulation to be solved to optimality that has the same number of integer variables as that of the single objective formulation of the problem. The application of the algorithm show that it is particularly effective, and superior to the state-of-the-art, when solving constrained multiobjective discrete optimisation problem instances.
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Published date: 18 May 2016
Organisations:
Centre of Excellence in Decision, Analytics & Risk Research
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Local EPrints ID: 394482
URI: http://eprints.soton.ac.uk/id/eprint/394482
PURE UUID: cdb27f9a-7da8-4886-889c-603fbb1f0cfe
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Date deposited: 18 May 2016 15:56
Last modified: 16 Apr 2020 16:35
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Author:
T. Bektas
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