Benders Decomposition for Large-Scale Prescriptive Evacuations
Authors: Julia Romanski, Pascal Van Hentenryck
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |