Simulation-based Optimization Using Genetic Algorithms for Multi-objective Flexible JSSP
The fast technological progress, along with growing requirements in
the manufacturing systems have led in the last decades to a true revolution
regarding the optimization methods for job
shop scheduling problem (JSSP), which
regularly has the greatest impact on the global optimality from the temporal perspective.
An extension to the mathematical framework associated to the JSSP for
multi-objective flexible JSSP (MOFJSSP) is proposed; here, the flexibility of type II, where the
routings of the jobs on the resources are not fixed is considered. Also, a short review
of the most used simulation-based optimization methods for (MOF)JSSP is made
and a genetic algorithm-based control system is proposed. This is then tested
on a complex real-world MOFJSS instance and the ft10 test-instance.
Simulation-based Optimization Using Genetic Algorithms for Multi-objective Flexible JSSP.
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Simulation-based Optimization Using Genetic Algorithms for Multi-objective Flexible JSSP.
Autori:
Elena Simona
NICOARĂ
[1]
| Florin Gheorghe
FILIP
[2]
[1]
Universitatea de Petrol şi Gaze din Ploieşti
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[2]
Romanian Academy - INCE and BAR
Rezumat
The fast technological progress, along with growing requirements in
the manufacturing systems have led in the last decades to a true revolution
regarding the optimization methods for job
shop scheduling problem (JSSP), which
regularly has the greatest impact on the global optimality from the temporal perspective.
An extension to the mathematical framework associated to the JSSP for
multi-objective flexible JSSP (MOFJSSP) is proposed; here, the flexibility of type II, where the
routings of the jobs on the resources are not fixed is considered. Also, a short review
of the most used simulation-based optimization methods for (MOF)JSSP is made
and a genetic algorithm-based control system is proposed. This is then tested
on a complex real-world MOFJSS instance and the ft10 test-instance.
Cuvinte cheie:
Multi-objective Flexible Job Shop Scheduling Problem, Simulation-based Optimization, Genetic Algorithm, GA-based Control, NSGA-II
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