Iterative matheuristic for the biomedical sample transportation problem
Bibliographic record
Abstract
This paper proposes an iterative matheuristic for solving the biomedical sample transportation problem (BSTP), which is a routing problem with multiple and interdependent visits in the context of healthcare services. In this problem, the biomedical samples are collected from individuals at a set of healthcare or specimen collection centers and must be transported to designated laboratories to be analyzed. The perishable nature of the specimens forces to visit the collection centers more than once a day to ensure that the time from the moment they are drawn to the arrival at the laboratory do not exceed the samples lifespan. Also, a visit to one center imposes (1) a limit on the duration of the route that transports its samples to the laboratory, and (2) a limit on the latest time at which the same center must be visited again, creating an interdependency between visits, routes and the decision concerning the centers’ opening times. This paper first proposes a mathematical formulation to model the BSTP. Since this formulation is not able to solve medium or large sized instances efficiently, it also proposes an iterative matheuristic, which includes two main steps. The first step produces an approximated solution to the BSTP by a decomposition approach that splits the problem into a series of smaller subproblems that are solved by the proposed mathematical formulation. In the second step, two fix-&-optimize strategies are used with the mathematical formulation to perform a local search around the solutions produced by the decomposition method. The matheuristic has demonstrated its efficiency solving a rich set of real-life instances corresponding to the needs of several regions in the province of Quebec, Canada, in a fraction of the time required to solve the exact mathematical formulation.
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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 | 0.003 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".