Bibliographic record
Abstract
This thesis introduces a new delivery method for electronic grocery shopping. This method involves a peer-to-peer matching system in which customers (carriers) who shop at a grocery store are assigned other customers (clients) who have ordered groceries to be delivered to their home. Carriers deliver groceries to the clients’ homes, in return for an incentive. The matching system matches carriers to clients to minimize the total travel time on the network.In order to examine how this new method of delivery performs, we compare it with the current methods of grocery delivery: regular grocery shopping, where every customer visits the grocery store, and truck delivery service, where groceries are delivered to customers who have ordered them. Four different scenarios are designed and simulated. A set of performance measures is then developed in order to evaluate the different scenarios. The matching system is simulated by employing a mixed-integer optimization method and solved by the IBM-CPLEX add-on in Matlab. The truck delivery service is presumed to be a multi-depot split-delivery truck delivery service and is simulated by using a partial inference of a multi-depot split-delivery vehicle routing problem heuristic. The scenarios are simulated and examined on a hypothetical network –The Sioux Falls traffic network.The most important finding from the results is that the new delivery system – the matching system – performs well in terms of total travel time in the network and has the potential to be implemented alongside the two other current methods of grocery shopping. The matching system reduces the travel time in the network compared to regular grocery shopping, but does not outperform truck home delivery service. The performance of the matching system is found to be the greatest when the number of carriers is equal to the number of clients.The matching system produced acceptable results in the study presented in this thesis; however, the need for a more detailed investigation is obvious to be able to judge its privileges to the regular truck delivery service.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".