Reference : Comparative Study of Genetic and Discrete Firefly Algorithm for Flexible Job-Shop Sch...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/33349
Comparative Study of Genetic and Discrete Firefly Algorithm for Flexible Job-Shop Scheduling Problem
English
Tessaro Lunardi, Willian mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Voos, Holger [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
2018
33rd ACM/SIGAPP Symposium On Applied Computing, Pau, France, April 9 - 13, 2018
Yes
No
International
33rd ACM/SIGAPP Symposium On Applied Computing
09/04/2018 to 13/04/2018
[en] Firefly algorithm ; Genetic Algorithm ; Combinatorial optimization ; Flexible job-shop problem ; Scheduling ; Artificial Intelligence
[en] Flexible job-shop scheduling problem (FJSP) is an extension of the classical job shop scheduling problem where an operation can be processed by several different machines. The FJSP contains two sub-problems, namely machine assignment problem and operation sequencing problem. In this paper, we propose and compare a discrete firefly algorithm (FA) and a genetic algorithm (GA) for the multi-objective FJSP. Three minimization objectives are considered, the maximum completion time, workload of the critical machine and total workload of all machines. Five well-known instances of FJSP have been used to evaluate the performance of the proposed algorithms. Comparisons among our methods and state-of-the-art algorithms are also provided. The experimental results demonstrate that the FA and GA have achieved improvements in terms of efficiency. Solutions obtained by both algorithms are comparable to those obtained by algorithms with local search. In addition, results show that the proposed discrete firefly algorithm is feasible, more effective and efficient than the proposed genetic algorithm for solving multi-objective FJSP.
http://hdl.handle.net/10993/33349

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