Modelling Diversity of Solutions

Authors: Linnea Ingmar, Maria Garcia de la Banda, Peter J. Stuckey, Guido Tack1528-1535

AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experimental Evaluation Single Distance Measure Consider the Bulk Water Management Problem... Table 1 shows runtime and minimum distance... Table 2 shows runtime and hypervolume... Case Study: Process Plant Layout Optimization
Researcher Affiliation Academia Linnea Ingmar,1 Maria Garcia de la Banda,2 Peter J. Stuckey,2 Guido Tack2 1KTH Royal Institute of Technology, Sweden 2Monash University, Melbourne, Australia
Pseudocode Yes Algorithm 1 A greedy algorithm for the general diversity problem
Open Source Code No The paper discusses implementing the framework in Mini Zinc and providing Mini Zinc annotations, but it does not include an explicit statement or a link to the open-source code for the methodology described in the paper.
Open Datasets No The paper mentions standard problems like the 'Bulk Water Management Problem' and 'Resource-Constrained Project Scheduling Problem with Weighted Earliness/Tardiness objective (RCPSP/WET)' but does not provide specific links, DOIs, or repository names for the datasets used in its experiments, nor does it explicitly state they are standard benchmark datasets with defined public access.
Dataset Splits No The paper focuses on combinatorial optimization problems and finding diverse solutions, not on machine learning models requiring explicit training, validation, and test dataset splits. Therefore, it does not describe validation dataset splits.
Hardware Specification No The paper mentions running Gurobi on '4 threads' for the plant layout problem instance but does not provide any specific hardware details such as CPU or GPU models, or memory specifications.
Software Dependencies No The paper mentions several software tools like 'Mini Zinc', 'Gurobi', 'Gecode', and 'Chuffed' but does not specify their version numbers, which are necessary for reproducible software dependencies.
Experiment Setup Yes Table 1 and 2 mention a 'timeout (30 minutes)'. For the Bulk Water Management Problem, it states 'floating point precision of 0.1'. For generating random solutions, it mentions 'passing a different random seed in each invocation of the solver'. In the Case Study, it states 'solves to optimality using Gurobi on 4 threads' and 'within five percent of the optimal value'.