Abstract
In the production scheduling problem, due to various kinds of uncertain factors such as queuing, breakdowns and repairing time of machines, the optimal solution considering the stochastic behaviour of a real operation cannot be easily solved. To solve the problem, we present a hybrid approach with a genetic algorithm (GA) and a simulation. The GA is used for optimization of schedules, and the simulation is used to minimize the maximum completion time for the last job with fixed schedules from the GA model. We obtain more realistic production schedules with an optimal completion time reflecting stochastic characteristics by performing the iterative hybrid GA - simulation procedure. It has been shown that the hybrid approach is powerful for complex production scheduling.
| Original language | English |
|---|---|
| Pages (from-to) | 129-136 |
| Number of pages | 8 |
| Journal | International Journal of Advanced Manufacturing Technology |
| Volume | 28 |
| Issue number | 1-2 |
| DOIs | |
| Publication status | Published - 2006 Feb 1 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Software
- Mechanical Engineering
- Computer Science Applications
- Industrial and Manufacturing Engineering
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Hybrid approach to production scheduling using genetic algorithm and simulation. / Jeong, Suk Jae; Lim, Seok Jin; Kim, Kyung Sup.
In: International Journal of Advanced Manufacturing Technology, Vol. 28, No. 1-2, 01.02.2006, p. 129-136.Research output: Contribution to journal › Article
TY - JOUR
T1 - Hybrid approach to production scheduling using genetic algorithm and simulation
AU - Jeong, Suk Jae
AU - Lim, Seok Jin
AU - Kim, Kyung Sup
PY - 2006/2/1
Y1 - 2006/2/1
N2 - In the production scheduling problem, due to various kinds of uncertain factors such as queuing, breakdowns and repairing time of machines, the optimal solution considering the stochastic behaviour of a real operation cannot be easily solved. To solve the problem, we present a hybrid approach with a genetic algorithm (GA) and a simulation. The GA is used for optimization of schedules, and the simulation is used to minimize the maximum completion time for the last job with fixed schedules from the GA model. We obtain more realistic production schedules with an optimal completion time reflecting stochastic characteristics by performing the iterative hybrid GA - simulation procedure. It has been shown that the hybrid approach is powerful for complex production scheduling.
AB - In the production scheduling problem, due to various kinds of uncertain factors such as queuing, breakdowns and repairing time of machines, the optimal solution considering the stochastic behaviour of a real operation cannot be easily solved. To solve the problem, we present a hybrid approach with a genetic algorithm (GA) and a simulation. The GA is used for optimization of schedules, and the simulation is used to minimize the maximum completion time for the last job with fixed schedules from the GA model. We obtain more realistic production schedules with an optimal completion time reflecting stochastic characteristics by performing the iterative hybrid GA - simulation procedure. It has been shown that the hybrid approach is powerful for complex production scheduling.
UR - http://www.scopus.com/inward/record.url?scp=33644668514&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33644668514&partnerID=8YFLogxK
U2 - 10.1007/s00170-004-2345-7
DO - 10.1007/s00170-004-2345-7
M3 - Article
AN - SCOPUS:33644668514
VL - 28
SP - 129
EP - 136
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
SN - 0268-3768
IS - 1-2
ER -