An Improved Self-adaptive Genetic Algorithm for Scheduling Steel-Making Continuous Casting Production
The steel-making continuous casting scheduling problem holds more constraints than flow shop in no-idle machine constraint, multiple buffers and batch scheduling, resulting in strong NP-hardness. Its mathematical programming model is first established based on unit-specific event-point continuous-time representation. Then a novel improved self-adaptive genetic algorithm (SAGA) is proposed to optimize the sequence among casts with the objective of reducing the total idle times on all machines and minimizing the make-span. In SAGA, the probabilities of crossover and mutation rate are rectified exquisitely and automatically so as to avoid being trapped in local optima and neighbourhood-based mutation operation is adopted to improve the diversity. Experimental comparisons with GAMS/CPLEX and other two state-of-art algorithms demonstrate the effectiveness and efficiency of SAGA in solving the large-size problems.
KeywordsFlow shop scheduling Steel-making continuous casting Self-adaptive genetic algorithm
We express our deepest gratitude to the National Science Foundation of China under grants of 51275366 and 51305311 and the Specialized Research Fund for the Doctoral Program of Higher Education of China (20134219110002). The authors also thank suggestions and comments from the anonymous referees to improve this paper.
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