Comparison of metaheuristics for the single vehicle pickup and delivery routing problem with multiple commodities
Bibliographic record
Abstract
The problem that we are going to study in this thesis is an extension of a Vehicle Routing Problem with Pickups and Deliveries -Single Vehicle Pickup and Delivery Problem with Multiple Commodities.In this problem there is only one vehicle that serves customers which have delivery and pickup demands in many commodities.The main objective of the problem is to find the least cost route, provided that pickup and delivery demands of all customers are satisfied.Additionally, the vehicle capacity should not be exceeded for any of the commodities.At present only one method which allows for solving Single Vehicle Pickup and Delivery Problem with Multiple Commodities exists -it is a heuristic by Gjengstrø and Vaksvik (2008).So, there is the possibility for improvements.This thesis presents two new Tabu search based metaheuristics.These metaheuristics are more advanced in comparison to heuristic by Gjengstrø and Vaksvik.Their core concept is changing the neighborhood when the search process sticks in the local optimum and can no longer improve the solution.The first metaheuristic utilizes the idea of Variable Neighborhood Search and changes the neighborhood each time the current solution cannot be improved.The second is more sophisticated -it selects the neighborhood with the probability which is calculated according to the search history.It also uses Simulated annealing technique to accept worse solutions.Computational results show that both metaheuristics are especially good in solving problem instances where the vehicle load is less than 100% of its maximal capacity.It is also shown that changing the load influences shapes of the obtained solutions and the number of visits to customers in the routes.Assumption that optimal solutions have only customers that are visited once is not true.We present situations when Hamiltonian-shaped routes are load-infeasible and the optimal solution then is non-Hamiltonian.Sometimes even if Hamiltonian solutions are feasible the route of the least cost is non-Hamiltonian and thus, it has customers which are visited more than once.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".