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Simulation-based Optimization Using Genetic Algorithms for Multi-objective Flexible JSSP
| Content Provider | Semantic Scholar |
|---|---|
| Author | Nicoar, Elena Simona Filip, Florin G. Paraschiv, Nicolae |
| Copyright Year | 2017 |
| Abstract | 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 simulationbased 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://sic.ici.ro/wp-content/uploads/2011/12/SIC_2011-4-Art1.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Control system Diabetes Mellitus, Non-Insulin-Dependent Genetic algorithm JSSP Job Syndrome Job shop scheduling Job stream Mathematical optimization Mathematics Numerous Occupations Requirement Scheduling (computing) Scheduling - HL7 Publishing Domain Simulation-based optimization |
| Content Type | Text |
| Resource Type | Article |
