Sustainable vehicle-routing problem with time windows by heterogeneous fleet of vehicles and separated compartments: Application in waste collection problem
Bibliographic record
Abstract
The purpose of this study is solving a sustainable vehicle routing problem (VRP) which in this problem special features such as mixed close–open VRP, multi-depot VRP and some others which will be discussed in this section are considered for achieving closer to real life applications. Fleets of vehicle studied in this paper are heterogeneous and for each vehicle separated compartments with different capacity for each type of wastes is took into consideration. Vehicles have different limitation on traveling time, different fixed and variable cost and amount of pollutants that is emitted from them. For achieving a sustainable VRP economic, environment and society aspects should considered simultaneously which in this paper objective functions (1) to (3) respectively are about mentioned purposes; first one minimizes the cost of collecting wastes from customer’s location, second one minimizes the pollutants which are emitted from vehicles while they are collecting wastes and finally third one minimizes violation from time limitations which are exist on each customer’s location. A new mathematical mixed integer programming model is developed for solving this problem and problem is solved by CPLEX solver and augmented ɛ-constraint method. Moreover, AHP technique for making decisions is applied in order to help us to choose the best decision. Finally, sensitivity analysis is done on some important parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".