A Benders Decomposition Method for Designing Reliable Supply Chain Networks Accounting for Multimitigation Strategies and Demand Losses
Bibliographic record
Abstract
This paper investigates the design of reliable supply networks to make them resilient to unpredictable disruptions. We develop an optimization model that incorporates several features, including (1) partial failure of facilities (instead of complete shutdown) resulting in interrupted supply capacity, (2) the effect of disruption on customer demand, and (3) the possibility to use multistrategies to mitigate disruption. We formulate a mixed-integer linear programming model to determine the optimal location of facilities and assignment of customers to opened facilities. An accelerated Benders decomposition method with valid inequalities is proposed to solve the problem. We discuss the computational efficiency of this decomposition procedure using two case studies as well as randomized data. For medium- and large-sized instances, our approach can decrease computational times by as much as 60% on average. We analyze the effect of multimitigation policies on the optimal solution and the model performance. Compared with the existing single-mitigation strategy models, we find that our model reduces the need for redundancy by as much as 50% and improves the total cost by as much as 8% in our case studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".