| --- |
| license: mit |
| tags: |
| - operations-research |
| - vehicle-routing |
| - combinatorial-optimization |
| - benchmark |
| task_categories: |
| - reinforcement-learning |
| --- |
| |
| # TTP-D benchmarks, datasets and results |
|
|
| Problem instances, behaviour-cloning datasets, and solver result tables for the |
| study *Fly, Pack, Drive: the Travelling Thief Problem with Drone*. |
|
|
| Paper: [Drive, Pack, Fly: The Travelling Thief Problem with Drone](https://arxiv.org/abs/2608.16435) |
|
|
| A capacitated truck and a single-package drone operate from a common depot on a |
| collection route. The truck's velocity decreases affinely with its accumulated |
| load, so an early pickup penalises every subsequent arc. The drone launches from |
| a node at which the truck is present, retrieves one item at an outlying |
| customer, and rejoins the truck at a later rendezvous node; whichever vehicle |
| arrives first waits, while the rental clock continues to run. The objective is |
| to maximise collected profit net of a rental cost proportional to the makespan. |
|
|
| Code: <https://github.com/corbit-lab/ttpd>. Trained policy checkpoints are in the |
| companion model repository |
| [`Murjani/ttpd-weights`](https://huggingface.co/Murjani/ttpd-weights). |
|
|
| ## Layout |
|
|
| ``` |
| instances/a280/ instances sampled from a280 (bench_<n>_<i>.txt) |
| instances/ttd300/ endurance-controlled instances |
| behaviour_cloning_datasets/ expert demonstrations for LISA (.jsonl) |
| results/a280/ per-solver result tables |
| results/ttd300/ likewise for the endurance study |
| ``` |
|
|
| Instance files follow the TTP text format — tab-separated, CRLF — extended with |
| a `DRONE ENDURANCE` header field. |
|
|
| ## Benchmark families |
|
|
| **a280.** Customers sampled from the TSPLIB `a280` TTP instance, preserving the |
| spatial structure and item economics of the classical configuration. The depot |
| is fixed at node 1, each remaining city contributes its single highest-profit |
| item, and five instances are drawn per size for |
| N ∈ {5, 10, 15, 20, 30, 40, 50, 100}. Capacity scales with size at the a280 |
| per-city rate, W = 637,010·N/280, with a renting ratio of R = 72.70 and a drone |
| speed of twice the maximum truck speed. Sortie length is unbounded. |
|
|
| **ttd300.** Introduced in this study. Uniform integer coordinates on a [0,300]² |
| box under `CEIL_2D` distances, N ∈ {10, 20, 30, 40, 50, 75, 100} with five |
| layouts each. Five items per customer with w ~ U[1000,1009] and p ~ U[1,1000], |
| the a280 "uncorrelated, similar weights" class. Capacity retains the a280 |
| per-city rate at W = round(2275.0357·N), fixed per size and shared across |
| layouts, with R = 50. |
|
|
| Each ttd300 layout is instantiated at four endurance fractions, |
| E_D = round(f · d_max) for f ∈ {0.25, 0.5, 0.75, 1.0}, where d_max is the |
| instance's own maximum pairwise distance, yielding 140 instances. Filenames |
| carry the fraction (`_f025`, `_f050`, `_f075`, `_f100`) and the absolute |
| endurance appears in the header; the bare `ttd300_n<N>_L<L>.txt` file, which has |
| no endurance header, is the unbounded reference. |
| |
| The endurance limit is specified relatively because an absolute budget carries a |
| different physical meaning at different sizes under non-uniform geometry, and |
| the a280 layout is strongly non-uniform. A fixed-size uniform domain combined |
| with a fraction of d_max gives the endurance radius a consistent interpretation |
| across instances, which is what makes it a clean axis to sweep. |
|
|
| ## Behaviour-cloning datasets |
|
|
| Expert demonstrations produced by running the full-budget simulated annealer on |
| a corpus of instances, inverting each solution into the composite action |
| sequence of the underlying MDP, and replaying it through the simulator to verify |
| that it reproduces the expert objective. Only certified state–action pairs are |
| retained. |
|
|
| Datasets are named for their coverage: `sampled_n<N>.jsonl` is drawn from |
| randomly sampled instances and `benchmark_n5_to_n100.jsonl` from the benchmark |
| instances themselves. They are regenerable from the repository code. |
|
|
| ## Instance labelling |
|
|
| Thirteen a280 files are labelled one seed offset: `bench_<n>_<k>.txt` contains |
| the instance whose internal `PROBLEM NAME` is `bench_<n>_<k+1>`, affecting |
| `bench_10_{2,3,4}`, `bench_20_{3,4}`, `bench_30_{1,2,3,4}` and |
| `bench_40_{1,2,3,4}`. No comparison is affected, as every solver reads the same |
| file for a given label and all methods therefore solved identical instances. The |
| issue concerns provenance only, and implies that these thirteen files will not |
| regenerate under their current names. |
|
|
| ## Loading |
|
|
| ```python |
| from huggingface_hub import hf_hub_download, snapshot_download |
| |
| one = hf_hub_download("Murjani/ttpd-benchmarks", |
| "instances/a280/bench_20_5.txt", repo_type="dataset") |
| |
| family = snapshot_download("Murjani/ttpd-benchmarks", repo_type="dataset", |
| allow_patterns=["instances/a280/*"]) |
| ``` |
|
|
| The repository code resolves these paths automatically through |
| `ttpd.hub.ensure_local` and `ttpd.hub.ensure_bench_dir`, which return existing |
| local files unchanged and download otherwise. |
|
|
| `manifest.json` records, for every file, the path from which it was published |
| together with its SHA-256 digest. |
|
|
| ## Citation |
|
|
| Please cite the accompanying paper. Reported results are given there; the tables |
| under `results/` are the raw per-run outputs from which they were computed. |
