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 [1].
Integrated Offline and Online Decision Making under Uncertainty
Authors: Allegra De Filippo, Michele Lombardi, Michela Milano
JAIR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To test our methods, we ground our approaches on two real cases studies with both offline and online decisions: an energy management problem with uncertain renewable generation and demand, and a vehicle routing problem with uncertain travel times. The application domains feature respectively continuous and discrete decisions. An extensive analysis of the experimental results shows that indeed offline/online integration may lead to substantial benefits. |
| Researcher Affiliation | Academia | Allegra De Filippo EMAIL Michele Lombardi EMAIL Michela Milano EMAIL Department of Computer Science and Engineering University of Bologna, viale Risorgimento, 2 40136 Bologna, ITALY |
| Pseudocode | No | The paper describes the methods (Baseline, ANTICIPATE, TUNING, ACKNOWLEDGE, ACTIVE) using mathematical formulations and descriptive text, but it does not include explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We use data from two public datasets to define problem instances: a residential plant (Espinosa & Ochoa, 2015) 1 with only PV energy production for renewable sources and an industrial plant 2 with wind and PV production. ... We use classical Solomon TSPTW instances3. |
| Dataset Splits | Yes | We consider a sample of 100 realizations for six different instances of each problem. ... The scenarios in our models, conversely, are not sampled, but programmatically chosen: we consider four extreme scenarios where (resp.) the load and the RES generation are at low/high values. Concerning the uncertainty in our models, we deal with: 1) uncertainty via sampling (i.e. we sample realizations by assuming that the error for our forecasts can be considered an independent random variable); 2) a number of edge scenarios, thus making our model somewhat close to robust optimization. |
| Hardware Specification | No | We solve our LPs and MILPs using Gurobi, while for the non-linear problems we use BARON via the GAMS modeling system on the Neos server for optimization. |
| Software Dependencies | No | We solve our LPs and MILPs using Gurobi, while for the non-linear problems we use BARON via the GAMS modeling system on the Neos server for optimization. The specific versions of Gurobi, BARON, or GAMS are not mentioned. |
| Experiment Setup | Yes | The time limit is 100 seconds and we use data from two public datasets to define problem instances... The problem has 24 online stages. ... The time limit is 500 seconds. We use classical Solomon TSPTW instances... We always consider 2 available vehicles. Concerning the scenarios, we assume that, whenever a node is reached, its binary state becomes known, and with that the (uniform) distributions followed by the travel times of all its outgoing arcs. |