Stochastic Multi-Objective Vehicle Routing Model in Green Environment With Customer Satisfaction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Vehicle Routing Problem (VRP) is one of the most studied combinatorial optimization problems in operations research that are classified as NP-hard. Introducing uncertainty to the problem increases the complexity of solving such problems. Sources of uncertainty in a VRP can be travel times, service times, and unpredictable demands of customers. Ignoring these sources may lead to inaccurate modeling of the VRP. Moreover, the area of green logistics and the environmental issues associated received significant attention. This paper aims to study the stochastic multi-objective Vehicle Routing Problem in a green environment. The stochastic Green VRP (GVRP) presented deals with three objectives simultaneously that consider economic, environmental, and social aspects. First, a new hybrid search algorithm to solve the VRP is presented and validated. The algorithm is then employed to solve the stochastic multi-objective GVRP. Pareto fronts were obtained, and trade-offs between the three objectives are presented. Furthermore, an analysis of the effect of customers’ time window relaxation is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it