An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem

By L. D. Giovanni, F. Pezzella. European Journal of Operational Research, 2010

341 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/8075e71cbde2365a4402ce6a4a4c6c9c635284fe

How researchers see this paper

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

In Control and Systems Engineering

1. Liang Gao
2. Xinyu Li
3. Abid Ali Khan
4. Imran Ali Chaudhry

In Industrial and Manufacturing Engineering

1. Xinyu Li
2. Liang Gao
3. Abid Ali Khan
4. Imran Ali Chaudhry

Citations in the corpus

• Software Architecture Optimization Methods: A Systematic Literature Review (2013, neutral): “[38], [41], [57], [58], [59], [60], [71], [73], [76], [88], [92], [102],”
• A research survey: review of flexible job shop scheduling techniques (2016, methodology): “…Objective function Citations Lei (2010) Research Decomposition-integration GA, FJSSP with fuzzy processing times Minimization of fuzzy makespan 56 De Giovanni and Pezzella (2010) Research GA, FJSSP in distributed manufacturing environment where jobs are processed by a system of several flexible…”
• An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem (2016, background): “…strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals to solve FJSP. Giovanni and Pezzella (2010) proposed an improved GA to solve the distributed FJSP. Zhang et al. (2011) proposed a modi fi ed GA for FJSP and obtained…”