Simulation-Assisted Optimization for Large-Scale Evacuation Planning with Congestion-Dependent Delays
Authors: Kazi Ashik Islam, Da Qi Chen, Madhav Marathe, Henning Mortveit, Samarth Swarup, Anil Vullikanti
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use Harris County in Houston, Texas, as our study area. We show that, within a given time limit, MIP-LNS finds better solutions than existing methods in terms of three different metrics. However, when congestion dependent delay is considered, MIP-LNS-SIM outperforms MIP-LNS in multiple performance metrics. |
| Researcher Affiliation | Academia | Biocomplexity Institute, University of Virginia {ki5hd, wny7gj, marathe, henning.mortveit, swarup, vsakumar}@virginia.edu |
| Pseudocode | Yes | Algorithm 1: MIP-LNS Method and Algorithm 2: MIP-LNS-SIM Method |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We have used data from HERE maps [HERE, 2023] to construct its road network. ... We use a synthetic population [Adiga et al., 2015] to find the location of the households. |
| Dataset Splits | No | The paper describes its problem instance and how the optimization methods were applied, but it does not specify explicit training, validation, or test dataset splits in the traditional machine learning sense for model development or evaluation. |
| Hardware Specification | Yes | We performed all our experiments and subsequent analyses on a high-performance computing cluster, with 128GB RAM and 4 CPU cores allocated to our tasks. |
| Software Dependencies | No | The paper mentions using 'a MIP solver [Gurobi Optimization, LLC, 2023]' but does not provide a specific version number for Gurobi or any other software dependency. |
| Experiment Setup | Yes | In our experiments with MIP-LNS, for A-DCFP, we used thirty iterations (i.e. n = 30 in Algorithm 1 line 1). Also, since we have a random selection process within MIP-LNS, we ran ten experiment runs with different seeds. ... In our experiments, we start with p = 75 and set pinc = 0.5. When solving the reduced problem in each iteration (line 4), we use (i) a time limit, and (ii) a parameter threshold gap to decide when to stop. ... In our experiments, we set this to 5%. ... Within MIP-LNS-SIM, we set the parameter m = 10. We then experimented with two values for the parameter pe, which are 5, 10. |