An Exact Algorithm for Multicommodity Network Design Under Stochastic Interdictions
Bibliographic record
Abstract
In this paper, we study the multicommodity network design problem by considering the effects of disruptions under an uncertain interdiction budget. The goal is to install links between nodes to satisfy the demand for different commodities with minimum installation cost and the weighted sum of flow costs before and after interdictions. Using the designer-interdictor-designer framework, we present a trilevel mixed-integer stochastic network design model. In the first level, the designer selects a subset of links to install and route flows under normal conditions. Most studies in the literature assume that the interdiction budget is known to the decision maker (network designer) with certainty; however, in practice, the designer is not aware of interdiction capabilities. Therefore, the designer’s objective is to minimize the installation cost and the weighted sum of pre-interdiction and expected post-interdiction costs. In the second level, the interdictor interdicts a subset of installed arcs with a limited interdiction budget. In the third level, the designer optimizes the flow over the surviving links in the residual network. Furthermore, we extend the model to consider the uncertainty in the demand besides uncertain interdiction budget. We present a branch-and-Benders-cut algorithm to solve the proposed model. The algorithm is enhanced through the use of several features such as multicut reformulation, warm start, variable fixing, cut selection, penalty reformulation, generation of strong Pareto-optimal cuts, and supervalid and valid inequalities. Extensive computational experiments are performed to evaluate the efficiency and robustness of the proposed algorithmic refinements. We compare the performance of our algorithm with a state-of-the-art, general-purpose stochastic mixed-integer bilevel linear optimization solver and show that our algorithm is faster by orders of magnitude. Our results demonstrate that the branch-and-Benders-cut algorithm combined with some of these acceleration techniques solves large-scale instances with up to 20 nodes, 220 arcs, and 200 commodities. Furthermore, we present a sensitivity analysis to highlight the advantages of stochastic design over deterministic design when the interdiction budget is uncertain. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This research was supported by grants from the National Science and Engineering Research Council of Canada (NSERC) [Grants 2017-06732, 2021-04139]. S. M. Vaziri acknowledges the support of the Fonds de recherche du Québec for an FRQNT doctoral research scholarship [Grant B2X/304415-2021]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0286 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0286 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".