Distributionally robust joint chance-constrained approach for the stochastic survivable capacitated network design problem

In this paper, we first present a stochastic model for the survivable capacitated network design problem, in which the demand vector is uncertain. To deal with the uncertainty of the demand vector, we use the joint chance-constrained programming method. According to the real-world application, it is...

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Bibliographic Details
Published in:Journal of combinatorial optimization Vol. 50; no. 4; p. 37
Main Authors: Khodayifar, Salman, Farjaie, Mohammadreza
Format: Journal Article
Language:English
Published: New York Springer US 01.11.2025
Springer Nature B.V
Subjects:
Approximation
Combinatorics
Commodities
Constraints
Convex and Discrete Geometry
Demand
Diversification
Failure
Integer programming
Linear programming
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Mixed integer
Network design
Network management systems
Nonlinear programming
Operations Research/Decision Theory
Optimization
Robustness
Stochastic models
Theory of Computation
Traffic assignment
Uncertainty
Upper bounds
Bonferroni approximation
Joint chance- constrained programming
Survivability
Distributionally robust
Network design problem
ISSN:1382-6905, 1573-2886
Online Access:Get full text
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Summary:In this paper, we first present a stochastic model for the survivable capacitated network design problem, in which the demand vector is uncertain. To deal with the uncertainty of the demand vector, we use the joint chance-constrained programming method. According to the real-world application, it is assumed that only partial distribution information of uncertain demand, such as support, mean, and variance is available, and we use a robust method to deal with this uncertainty. The obtained robust counterpart model is a distributionally robust joint chance-constrained model. Due to the intractability of the distributionally robust joint chance-constrained model, we use the deterministic and Bonferroni approximation methods to solve it. The obtained models from the first and second methods are mixed integer non-linear programming (MINLP) and mixed integer linear programming (MILP), respectively. We propose an iterative approximation optimization method to provide the lower and upper bounds for the MINLP model. In the end, the efficiency of the proposed techniques is evaluated by some realistically sized instances.
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ISSN:1382-6905
1573-2886
DOI:10.1007/s10878-025-01370-8