Interdependent Scheduling Games
Authors: Andres Abeliuk, Haris Aziz, Gerardo Berbeglia, Serge Gaspers, Petr Kalina, Nicholas Mattei, Dominik Peters, Paul Stursberg, Pascal Van Hentenryck, Toby Walsh
IJCAI 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented the ILP and solved 1000 randomly generated instances where (a) general rewards are drawn from [50,100] and (b) rewards are uniform. The dependency graphs are generated by first randomly permuting the list of all services; then for each service i, drawing a random number of child services c 2 {0, 1, 2} and adding edge (i, i + c) with probability 0.5. Increasing the number/likelihood of dependencies by increasing the potential number of children or increasing the connection probability significantly increases runtime. Figure 1 shows the results for different parameters using Gurobi 6.5 on a computer equipped with an 2.0 GHz Intel Xeon E5405 CPU with 4 GB of RAM. |
| Researcher Affiliation | Collaboration | Andres Abeliuk Data61/NICTA EMAIL Haris Aziz Data61/NICTA and UNSW EMAIL Gerardo Berbeglia University of Melbourne EMAIL Serge Gaspers UNSW and Data61/NICTA EMAIL Petr Kalina Czech Technical University EMAIL Nicholas Mattei Data61/NICTA and UNSW EMAIL Dominik Peters University of Oxford EMAIL Paul Stursberg Technische Universit at M unchen EMAIL Pascal Van Hentenryck University of Michigan EMAIL Toby Walsh UNSW and Data61/NICTA EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper states: 'We implemented the ILP and solved 1000 randomly generated instances'. This indicates custom generated data, not a publicly available dataset with specific access information. |
| Dataset Splits | No | The paper mentions 'randomly generated instances' but does not provide specific dataset split information (like percentages or counts for training, validation, or test sets). |
| Hardware Specification | Yes | Figure 1 shows the results for different parameters using Gurobi 6.5 on a computer equipped with an 2.0 GHz Intel Xeon E5405 CPU with 4 GB of RAM. |
| Software Dependencies | Yes | Figure 1 shows the results for different parameters using Gurobi 6.5 on a computer equipped with an 2.0 GHz Intel Xeon E5405 CPU with 4 GB of RAM. |
| Experiment Setup | Yes | We implemented the ILP and solved 1000 randomly generated instances where (a) general rewards are drawn from [50,100] and (b) rewards are uniform. The dependency graphs are generated by first randomly permuting the list of all services; then for each service i, drawing a random number of child services c 2 {0, 1, 2} and adding edge (i, i + c) with probability 0.5. Increasing the number/likelihood of dependencies by increasing the potential number of children or increasing the connection probability significantly increases runtime. |