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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| Vydáno v: | Engineering optimization Ročník 56; číslo 2; s. 219 - 239 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Abingdon
Taylor & Francis
01.02.2024
Taylor & Francis Ltd |
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| ISSN: | 0305-215X, 1029-0273 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Ramirez-Calderon, Jose Jorge Leon, V. Lawrence, Barry |
| Author_xml | – sequence: 1 givenname: Jose orcidid: 0000-0002-8414-6597 surname: Ramirez-Calderon fullname: Ramirez-Calderon, Jose email: joser@galileo.edu organization: Texas A&M University – sequence: 2 givenname: V. surname: Jorge Leon fullname: Jorge Leon, V. organization: Texas A&M University – sequence: 3 givenname: Barry surname: Lawrence fullname: Lawrence, Barry organization: Texas A&M University |
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| SubjectTerms | Constraints implementation uncertainty Knapsack problem Linear programming Mathematical models Parameters Robust binary linear optimization Robustness Uncertainty |
| Title | Robust binary linear programming under implementation uncertainty |
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