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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
HVAC-Aware Occupancy Scheduling
Authors: BoonPing Lim, Menkes van den Briel, Sylvie Thiebaux, Scott Backhaus, Russell Bent
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments aim at explaining the usefulness of the standby mode and at demonstrating that our HVAC-aware scheduling model leads to significant consumption reduction (50% to 70% in our experiments) |
| Researcher Affiliation | Academia | Boon Ping Lim, Menkes van den Briel, Sylvie Thi ebaux Optimisation Research Group, NICTA Research School of Computer Science, ANU {firstEMAIL} Scott Backhaus, Russell Bent Defense Systems and Analysis Division Los Alamos National Laboratory EMAIL |
| Pseudocode | No | The paper describes the mathematical models and equations but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing its source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | we extracted 70 problem instances from the PATAT dataset, consisting of 40 instances of 10 meetings each, 20 instances of 20 meetings each, and 10 instances of 50 meetings each. (Reference: Melbourne University. 2002. PATAT 2002 Dataset. http://www.or.ms.unimelb.edu.au/timetabling/.) |
| Dataset Splits | No | The paper uses problem instances for experiments but does not explicitly define or specify any training, validation, or test splits for these instances. |
| Hardware Specification | Yes | All experiments were conducted on a cluster consisting of 2 AMD 6-Core Opteron 4184, 2.8 GHz with 64 GB of memory. |
| Software Dependencies | Yes | The MILP models are solved using Gurobi 5.6 (2014). |
| Experiment Setup | Yes | Experiments are conducted over 5 summer days with a row of 4 co-located zones, each consisting of a single 60 m2 room with a capacity of 30 people. The duration between successive time steps is t = 30min... For each run, both MILP and LNS were seeded with HS as the initial solution and were given the same run-time limit of 15 minutes. |