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..
Algorithms for Max-Min Share Fair Allocation of Indivisible Chores
Authors: Haris Aziz, Gerhard Rauchecker, Guido Schryen, Toby Walsh
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we report the results of our computational study where we tested the performance of our SCHED heuristic (Algorithm 3). We show that SCHED returns near-optimal solutions and greatly outperforms the simpler greedy round-robin protocol (Algorithm 1). |
| Researcher Affiliation | Academia | Haris Aziz Data61 and UNSW Sydney, NSW 2052, Australia EMAIL; Gerhard Rauchecker University of Regensburg 93053 Regensburg, Germany EMAIL; Guido Schryen University of Regensburg 93053 Regensburg, Germany EMAIL; Toby Walsh UNSW, Data61 and TU Berlin Sydney, NSW 2052, Australia EMAIL |
| Pseudocode | Yes | Algorithm 1 Greedy round-robin protocol; Algorithm 2 PTAS for optimal Mm S; Algorithm 3 SCHED heuristic for optimal Mm S |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | Utilities were drawn from a uniform distribution ui(j) U(0, 100) (and negated for obtaining chores instances) as it is common in the literature for fair division of goods (Amanatidis et al. 2015; Bouveret and Lemaหฤฑtre 2016; Kurokawa, Procaccia, and Wang 2016). This describes data generation, not the use of a publicly accessible dataset with concrete access info. |
| Dataset Splits | No | The paper does not explicitly provide information about training, validation, or test dataset splits. It describes generating instances for computational experiments but not how they are partitioned for model evaluation. |
| Hardware Specification | Yes | We coded all algorithms in C++ on a Linux Cent OS 7 based 12-core processor with a clock speed of 3.07 GHz and 24 Gi B memory. |
| Software Dependencies | Yes | Integer linear programs were solved via the Gurobi 6 C++ API. |
| Experiment Setup | Yes | The time limit in step 4 of SCHED was set to Tmax = 300s. |