Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Neural Neighborhood Search for Pickup and Delivery Problems
Authors: Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Hongliang Guo, Yuejiao Gong, Yeow Meng Chee
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our N2S is generic, and extensive experiments on two canonical PDP variants show that it can produce state-of-the-art results among existing neural methods. Moreover, it even outstrips the well-known LKH3 solver on the more constrained PDP variant. |
| Researcher Affiliation | Collaboration | 1National University of Singapore 2Singapore Institute of Manufacturing Technology, A*STAR 3Institute of Marine Science and Technology, Shandong University 4Institute for Infocomm Research, A*STAR 5South China University of Technology |
| Pseudocode | Yes | Algorithm 1 n-step PPO with CL strategy Algorithm 2 N2S-A Inference |
| Open Source Code | Yes | Our implementation for N2S is available online1. 1Code is available at https://github.com/yining043/PDP-N2S. |
| Open Datasets | Yes | We evaluate N2S on PDTSP and PDTSP-LIFO... where the node coordinates of instances are randomly and uniformly generated in the unit square [0, 1] [0, 1]... We further evaluate our N2S on benchmark instances, including all the ones from [Renaud et al., 2002] for PDTSP and the ones with size |V| ≥ 201 from [Carrabs et al., 2007] for PDTSP-LIFO |
| Dataset Splits | No | The paper specifies training and test datasets ('All baselines are evaluated on a test dataset with 2,000 instances'), but does not explicitly mention a separate validation dataset split or its size/proportion in the main text. |
| Hardware Specification | Yes | Our experiments were conducted on a server equipped with 8 RTX 2080 Ti GPU cards and Intel E5-2680 CPU @ 2.4GHz. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' but does not specify version numbers for any programming languages, libraries, or other software components used in the experiments. |
| Experiment Setup | Yes | Our N2S is trained with E = 200 epochs and B = 20 batches per epoch using batch size 600. We set n=5, Ttrain =250 for the n-step PPO with κ=3 mini-batch updates and a clip threshold ϵ=0.1. Adam optimizer is used with learning rate ηθ =8·10−5 for πθ, and ηϕ =2·10−5 for vϕ (decayed β = 0.985 per epoch). The discount factor γ is set to 0.999 for both PDPs. We clip the gradient norm to be within 0.05, 0.15, 0.35, and set the curriculum learning ρCL to 2, 1.5, 1 for the three problem sizes, respectively. |