A genetic algorithm for the Flexible Job-shop Scheduling Problem

By F. Pezzella, G. Morganti, G. Ciaschetti. Computers & Operations Research, 2008

695 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/77e61887bc686f573ce84a4bd6190d6475a8ab09

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jian Lian
2. Liang Gao
3. Xinyu Li
4. Weikuan Jia
5. Sujuan Hou
6. Meili Sun

In Industrial and Manufacturing Engineering

1. Xinyu Li
2. Liang Gao

Citations in the corpus

• An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem (2016, methodology): “However, exact algorithms are not effective for solving large scale FJSP instances (the total number of operations is more than 200) (Pezzella et al., 2008).”
• Feature dimensionality reduction: a review (2022, background): “Pezzella showed a GA for Flexible Job Shop Scheduling (FJSP) that integrates different initial population generation strategies and selects individuals for breeding [34].”