Constraint-Based Scheduling with Complex Setup Operations: An Iterative Two-Layer Approach

Authors: Adriana Pacheco, Cédric Pralet, Stéphanie Roussel

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments realized on representative benchmarks of a multi-robot application show the efficiency of the approach.
Researcher Affiliation Academia Adriana Pacheco , C edric Pralet and St ephanie Roussel ONERA / DTIS, Universit e de Toulouse, F-31055 Toulouse France {adriana.pacheco, cedric.pralet, stephanie.roussel}@onera.fr
Pseudocode Yes Algorithm 1 presents a generic pseudo-code of a process between two layers that use each others solutions to minimize an objective function.
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 Experiments were performed over several multi-robot problem instances generated randomly. These instances contain from 1 to 15 observation requests, each request requiring observations from 1 to 3 robots.
Dataset Splits No The paper mentions that problem instances were generated randomly but does not specify any training, validation, or test dataset splits.
Hardware Specification Yes The scheduling problems were solved using IBM ILOG CP Optimizer 12.5 on an Intel Xeon E51603, 2.80GHz 8GB RAM
Software Dependencies Yes The scheduling problems were solved using IBM ILOG CP Optimizer 12.5
Experiment Setup Yes Function update L1 is implemented with a learning rate α ranging from 0.2 to 1 and the reinitialization rate rate Reinit for perturb L1 is 0.2. The scheduling problems were solved using IBM ILOG CP Optimizer 12.5 on an Intel Xeon E51603, 2.80GHz 8GB RAM, setting cpu Max = {5, 30} minutes and an adequate iterations number n Loops, depending on the number of observations (problem size) and the cpu Max.