TY - JOUR
T1 - Robust and stable flexible job shop scheduling with random machine breakdowns
T2 - multi-objectives genetic algorithm approach
AU - Sajadi, Seyed Mojtaba
AU - Alizadeh, Azar
AU - Zandieh, Mostafa
AU - Tavan, Fereshteh
PY - 2019/2
Y1 - 2019/2
N2 - In this paper, robust and stable scheduling for a flexible job-shop problem with random machine breakdowns has been discussed. A two-stage genetic algorithm is used to generate the predictive schedule. The first stage optimises the primary objective, which minimises the makespan, where all data is considered to be deterministic with no expected disruptions. The second stage optimises two objectives, makespan and stability, function in the presence of random machine breakdowns. For the second stage two different versions of multi-objective genetic algorithm, non-dominated sorting genetic algorithm II and non-dominated ranking genetic algorithm, is used. A simulator is proposed to simulate random machine breakdowns. An experimental study and analysis of variance is conducted to study the results of each multi-objective algorithm and breakdown simulator. The results of their comparison indicate that, non-dominated ranking genetic algorithm (NRGA) performs better and also shows a significant difference between various repair times in the proposed breakdown simulator.
AB - In this paper, robust and stable scheduling for a flexible job-shop problem with random machine breakdowns has been discussed. A two-stage genetic algorithm is used to generate the predictive schedule. The first stage optimises the primary objective, which minimises the makespan, where all data is considered to be deterministic with no expected disruptions. The second stage optimises two objectives, makespan and stability, function in the presence of random machine breakdowns. For the second stage two different versions of multi-objective genetic algorithm, non-dominated sorting genetic algorithm II and non-dominated ranking genetic algorithm, is used. A simulator is proposed to simulate random machine breakdowns. An experimental study and analysis of variance is conducted to study the results of each multi-objective algorithm and breakdown simulator. The results of their comparison indicate that, non-dominated ranking genetic algorithm (NRGA) performs better and also shows a significant difference between various repair times in the proposed breakdown simulator.
KW - Flexible job-shop scheduling problem
KW - Machine breakdowns
KW - Robustness
KW - SMEs
KW - Stability
UR - http://www.scopus.com/inward/record.url?scp=85061323757&partnerID=8YFLogxK
UR - https://www.inderscienceonline.com/doi/abs/10.1504/IJMOR.2019.097759
U2 - 10.1504/IJMOR.2019.097759
DO - 10.1504/IJMOR.2019.097759
M3 - Article
AN - SCOPUS:85061323757
SN - 1757-5850
VL - 14
SP - 268
EP - 289
JO - International Journal of Mathematics in Operational Research
JF - International Journal of Mathematics in Operational Research
IS - 2
ER -