Applications of Genetic Algo!lithm to Production Scheduling
Tawfik Brekaa Mohamed Deifalla;
Abstract
Job shop system is a one of the common cases in manufacturing
systems and one of the most complicated problems to tackle. That is why more attention has been given to heuristics to solve a problem reaching acceptable solution in reasonable time. Recently genetic algorithm has become one of the key search techniques that are widely used to tackle complex combinatorial problems.
The objective of this work is to present genetic algorithm model for
the job shop systems and to investigate the differences between different crossover arrangements, test a new developed crossover technique (Multi-crossover genetic algorithm) and study the impact of varying the population size on solution.
The results showed promising results of the new Multi Crossover Genetic Algorithm (MXGA) technique specially the combination of Precedence Preservative Crossover (PPX)/ Linear Order Crossover (LOX) that out performed both PPX and LOX separately and even edged the performance of Partially Mapped Crossover (PMX) that maintained superiority over other single operators and combinations including PMX. The developed model reached optimum solution for the solved benchmark problem. Increasing population size has a positive impact on solution quality on the expense of increasing computation time, though the improvement in solution is relatively small compared to the increase in computational time.
Key Words:Genetic algorithm, Job shop scheduling, Crossover, population size.
systems and one of the most complicated problems to tackle. That is why more attention has been given to heuristics to solve a problem reaching acceptable solution in reasonable time. Recently genetic algorithm has become one of the key search techniques that are widely used to tackle complex combinatorial problems.
The objective of this work is to present genetic algorithm model for
the job shop systems and to investigate the differences between different crossover arrangements, test a new developed crossover technique (Multi-crossover genetic algorithm) and study the impact of varying the population size on solution.
The results showed promising results of the new Multi Crossover Genetic Algorithm (MXGA) technique specially the combination of Precedence Preservative Crossover (PPX)/ Linear Order Crossover (LOX) that out performed both PPX and LOX separately and even edged the performance of Partially Mapped Crossover (PMX) that maintained superiority over other single operators and combinations including PMX. The developed model reached optimum solution for the solved benchmark problem. Increasing population size has a positive impact on solution quality on the expense of increasing computation time, though the improvement in solution is relatively small compared to the increase in computational time.
Key Words:Genetic algorithm, Job shop scheduling, Crossover, population size.
Other data
| Title | Applications of Genetic Algo!lithm to Production Scheduling | Other Titles | تطبيق اسلوب الحل (الخوارزم ) الجينى فى جدولة الانتاج | Authors | Tawfik Brekaa Mohamed Deifalla | Issue Date | 2001 |
Attached Files
| File | Size | Format | |
|---|---|---|---|
| توفيق بريقع.pdf | 291.06 kB | Adobe PDF | View/Open |
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