How to Tame Your Anticipatory Algorithm
Authors: Allegra De Filippo, Michele Lombardi, Michela Milano
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluated the three hybrid offline/online methods on realistic instances for the two case studies. The baseline in both cases are myopic heuristics, obtained by running ANTICIPATE with an empty set of scenarios. ... Each evaluated algorithm and configuration is run 50 times, with the same 50 sequences of realizations. We use a time limit of 300 seconds for both the VPP and the TSP. For each run we record both the time required by each approach and the corresponding solution cost, and we report their average values over the 50 realizations. In all cases, |I| = |T| = 100, and for the CONTINGENCY method, the contingency table is built using ANTICIPATE with 20 scenarios. Due to space constraints, we report results only for a few representative instances. Figure 1: Methods solution/quality comparison for the VPP Figure 2: Methods solution/quality comparison for the TSP Table 1: Standard deviation comparison for the VPP and the TSP |
| Researcher Affiliation | Academia | Allegra De Filippo, Michele Lombardi and Michela Milano Department of Computer Science and Engineering, University of Bologna allegra.defilippo@unibo.it, michele.lombardi2@unibo.it, michela.milano@unibo.it |
| Pseudocode | Yes | Algorithm 1 ANTICIPATE (s1, ξ) ... Algorithm 2 BUILDTABLE (s1, AA) ... Algorithm 3 FIXING (s1, ξ, T) ... Algorithm 4 ANTICIPATE-D (s1, ξ) ... Algorithm 5 CONTINGENCY (s1, ξ) ... Algorithm 6 CONTINGENCY-D (s1, ξ) |
| Open Source Code | Yes | The full useful information for grounding our approaches are in a companion repository1 ... 1https://github.com/alle De/Off On |
| Open Datasets | Yes | For the TSP we use classical benchmarks2, by including problems from 10 to 40 nodes. ... 2http://myweb.uiowa.edu/bthoa/TSPTWBenchmark Data Sets.htm |
| Dataset Splits | No | The paper describes running algorithms multiple times with different realizations of uncertainty, but it does not specify traditional train/validation/test dataset splits for model training. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU or GPU models, or memory specifications. It only mentions the use of Gurobi solver. |
| Software Dependencies | Yes | As an underlying solver we use Gurobi 3, which can handle both MILPs and Quadratic Programs. |
| Experiment Setup | Yes | Each evaluated algorithm and configuration is run 50 times, with the same 50 sequences of realizations. We use a time limit of 300 seconds for both the VPP and the TSP. ... In all cases, |I| = |T| = 100, and for the CONTINGENCY method, the contingency table is built using ANTICIPATE with 20 scenarios. |