RETRACTED ARTICLE: Conic Reformulations of a Class of Discrete Stochastic Location Problems with Congestion
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
In this paper, we study a class of discrete facility location problems with stochastic demand and congestion that arise in the design of various service systems. The problem is to determine the location and size of the facilities, as well as the assignment of clients to these facilities, to minimize the weighted sum of the total travel time, waiting time and service time at the facilities. Traditionally, these problems are formulated as mixed-integer nonlinear optimization problems that can be computationally challenging even for moderate-sized instances. We reformulate the problem as a mixed-integer second-order cone program and present two new formulations that are further strengthened using polymatroid inequalities. An exact and efficient branch-and-cut algorithm is proposed in which polymatroid inequalities are separated to improve convergence. Through computational experiments, we show that our reformulations are approximately twenty times faster than the exact cutting plane method and outperform other conic reformulations reported in the literature. Furthermore, the average computation time is reduced further by half when polymatroid inequalities are used with the reformulations. Using the proposed method, we solved a large, real-life instance of the optimal location of mammography screening centers in the city of Montreal, Canada, in a reasonable time.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".