The Capacitated Team Orienteering and Profitable Tour Problems
Bibliographic record
Abstract
A huge number of papers appeared in the literature which study the well known Traveling Salesman Problem (TSP) and its generalizations to the case of multiple vehicles known as Vehicle Routing Problems (VRPs). While there exists one and only one TSP, many problems belong to the class of VRPs (see Toth, Vigo (2002)). In the TSP and in the VRPs all customers need to be visited. This means that in the situations modeled all customers are known at the time the optimization model is run and all require service. While this is indeed the case in many practical problems, there are many other practical problems where this assumption is not valid. Let us consider the following situation where a set of customers is given and only a subset has to be selected and served. Nowadays it is more and more frequent that shippers post their demands for transportation service on the web, usually in specific databases, and carriers identify some of those demands and offer their service to the shippers. Usually a carrier has a fleet of vehicles and a set of regular customers that have to be served. If the capacity of the vehicles is not fully used, the carrier may wish to look for spot customers on the web. In this case, the carrier has to
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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 | 0.003 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".