Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems

By Imed Kacem, S. Hammadi, P. Borne. IEEE Trans. Syst. Man Cybern. Part C, 2002

792 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/be3e72f47e3cb6f5d8b58e604325d20640040e2c

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In Artificial Intelligence

1. Abid Ali Khan
2. Imran Ali Chaudhry
3. Liang Gao
4. Yu-yan Han
5. Xinyu Li
6. Xinyu Shao
7. Peigen Li
8. Quan-Ke Pan

In Control and Systems Engineering

1. Liang Gao
2. Xinyu Li
3. Abid Ali Khan
4. Quan-Ke Pan
5. Guohui Zhang
6. Yang Shi
7. Imran Ali Chaudhry
8. Zhenghua Chen

In Industrial and Manufacturing Engineering

1. Xinyu Li
2. Liang Gao
3. Abid Ali Khan
4. Imran Ali Chaudhry
5. Smaïl Niar
6. A. Ammari
7. Abdelghani Bekrar
8. Abderezak Jemai

Citations in the corpus

• An effective genetic algorithm for the flexible job-shop scheduling problem (2011, methodology): “…of the idle time interval on machine Mx Ex the end time of the idle time interval on machine Mx L ¼ PN i¼1Jio the sum of all operations of all jobs Kacem, Hammadi, and Borne (2002) classified the FJSP into P-FJSP and T-FJSP as follows: If Xij X, then it has partial flexibility, it is partial…”
• A research survey: review of flexible job shop scheduling techniques (2016, background): “Based on flexibility, Kacem et al. (2002a) has classified the FJSSP into following two subproblems.”
• An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem (2015, methodology): “Kacem et al proposed in (Kacem et al. 2002) a genetic algorithm controlled by the assignment model that was generated by the approach of localization to mono-objective and multi-objective FJSP. Pezzel-laa et al proposed a genetic algorithm with several techniques to create population and select the…”
• An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem (2009, methodology): “A middle scale instance which is taken from Kacem et al. (2002b) is shown in Table 5.”
• An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem (2016, methodology): “The data of experiment 1 is adopted from Kacem et al. (2002).”
• A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems (2019, background): “FJSP is very complex and has been proven to be an NP-hard problem [3]–[4].”