Robust binary linear programming under implementation uncertainty

This article studies binary linear programming problems in the presence of uncertainties that may prevent implementing the computed solution. This type of uncertainty, called implementation uncertainty, is modelled affecting the decision variables rather than model parameters. The binary nature of t...

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Bibliographic Details
Published in:Engineering optimization Vol. 56; no. 2; pp. 219 - 239
Main Authors: Ramirez-Calderon, Jose, Jorge Leon, V., Lawrence, Barry
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 01.02.2024
Taylor & Francis Ltd
Subjects:
Constraints
implementation uncertainty
Knapsack problem
Linear programming
Mathematical models
Parameters
Robust binary linear optimization
Robustness
Uncertainty
ISSN:0305-215X, 1029-0273
Online Access:Get full text
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Summary:This article studies binary linear programming problems in the presence of uncertainties that may prevent implementing the computed solution. This type of uncertainty, called implementation uncertainty, is modelled affecting the decision variables rather than model parameters. The binary nature of the decision variables invalidates using existing robust models for implementation uncertainty. The robust solutions obtained are optimal for a worst-case min-max objective. Structural properties allow the reformulation of the problem as a binary linear program. Constraint relaxation and cardinality-constrained parameters control the degree of solution conservatism. An optimization problem permits the selection of solutions from the obtained set of robust solutions. Results from a case study in the context of the knapsack problem suggest the methodology yields solutions that perform well in terms of objective value and feasibility. Furthermore, the selection approach can identify robust solutions with desirable implementation characteristics.
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2022.2150181