Risk Based Optimization for Improving Emergency Medical Systems
Authors: Sandhya Saisubramanian, Pradeep Varakantham, Hoong Chuin Lau
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide an exhaustive evaluation on real-world datasets from two asian cities that demonstrates the improvement provided by our approach over current practice and the best known approach from literature. We evaluate the effectiveness of our techniques on real-world ambulance data sets from two large cities in Asia. The experimental results show that in addition to outperforming the best known approach in the literature (Yue, Marla, and Krishnan 2012), our approach is able to reduce the response time by at least a few minutes over current practice. |
| Researcher Affiliation | Academia | Sandhya Saisubramanian and Pradeep Varakantham and Hoong Chuin Lau School of Information Systems, Singapore Management University 80 Stamford Road, Singapore 178902 {sandhyas, pradeepv, hclau}@smu.edu.sg |
| Pseudocode | Yes | Algorithm 1: Solve LDD() 1 Initialize: λ0, it 0 ; 2 while duality gap > μ do 3 k : dk, Ak SOLVESLAVE(k) 4 Update prices according to Equation 18 5 p, Ap EXTRACTPRIMAL ({Ak}k |ξ|); 6 it it + 1; 7 return p, Ap |
| Open Source Code | No | The paper does not provide any explicit statement about making its source code available, nor does it include a link to a code repository. |
| Open Datasets | No | The paper states, "We experiment with two data sets, namely Dataset1 and Dataset2 obtained from two asian cities. Dataset2 is adopted from (Yue, Marla, and Krishnan 2012)." However, it does not provide concrete access information (e.g., a link, DOI, or explicit statement of public availability) for either dataset. |
| Dataset Splits | No | The paper states, "In making a comparison, we always consider a training set and test set," but it does not mention a validation set or specify exact split percentages or methodologies for training/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | Unless otherwise stated, the default settings of the experiments are α = 0.2, fleet size of dataset1 = 40 and fleetsize of datatset2 = 58. |