A set of Python Jupyter notebooks exploring the Flow Shop Scheduling Problem (FSP) through exhaustive search, heuristics, local search-based metaheuristics, and population-based metaheuristics such as genetic algorithms, focusing on both solution quality and computational efficiency.
Code and data for "Fast heuristics for minimizing the makespan in non-permutation flow shops"
Eclipse project for solving a flowshop-scheduling problem with the algorithm of Nawaz, Enscore and Ham (NEH algorithm)
Research code for flow shop, job shop and open shop scheduling problems
Branch and bound implementation for flowshop permutation probleme (Séparation et évaluation )
Code and data for "Flow shop scheduling with heterogeneous workers"
Projeto de iniciação científica desenvolvido como bolsista do CNPq entre 08/2021 e 08/2022
A flexible and extensible framework for generating, and solving instances of common scheduling problems such as Flow Shop, Job Shop, Open Shop and Hybrids. It provides a structured instance representation, solver interfaces, and basic solvers, making it a powerful foundation for research and development in scheduling optimization.
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