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..
Benders Decomposition for Large-Scale Prescriptive Evacuations
Authors: Julia Romanski, Pascal Van Hentenryck
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Benders decomposition provides significant improvements in solution quality in reasonable time: It finds provably optimal solutions to scenarios considered in prior work, closing these instances, and increases the number of evacuees by 10 to 15% on average on more complex flood scenarios. This section presents experimental results for a case study of the evacuation of the Hawkesbury-Nepean (HN) floodplain, which is located near Sydney. |
| Researcher Affiliation | Academia | 1Brown University, Providence, RI. 2University of Michigan, Ann Arbor, MI. |
| Pseudocode | Yes | Algorithm 1 The two-stage algorithm for clearance time minimization |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology described is publicly available. |
| Open Datasets | Yes | Experimental results for a case study of the evacuation of the Hawkesbury-Nepean (HN) floodplain, which is located near Sydney. The HN evacuation graph has 80 evacuation nodes, 184 transit nodes, 5 safe nodes, and 580 edges. The Benders decomposition was evaluated on the real-case study from (Even, Pillac, and Van Hentenryck 2015). |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, as it focuses on solving an optimization problem for a case study rather than training a machine learning model. |
| Hardware Specification | Yes | The algorithms were implemented using JAVA 8 and GUROBI 6.0 and the results were obtained on a 64 bit machine with a 1.4 GHz Intel Core i5 processor and 4 GB of RAM. |
| Software Dependencies | Yes | The algorithms were implemented using JAVA 8 and GUROBI 6.0 and the results were obtained on a 64 bit machine with a 1.4 GHz Intel Core i5 processor and 4 GB of RAM. |
| Experiment Setup | Yes | We use time horizons of 600 min for scenarios without flooding and 1000 min for scenarios with flooding, discretized into 5 minute time-steps. Each instance was run for one hour, unless the algorithm converged earlier. Each TDP iteration in the initial dichotomic search is solved with a time limit of 300 s. For the Benders decomposition dichotomic search, each run is given for up to 20 iterations, using the tree from the TDP-DS as a seed. |