Abstract
A real-world application which develops daily production plans for a large manufacturing company is presented. It is a hybrid system, which combines a genetic algorithm with simulation. Because of the time constraints involved when generating daily schedules, a number of modifications to the standard genetic algorithm were required. A real-valued chromosome representation stored in a hierarchical, dynamic data structure is proposed. Steady-state, rank-based selection, a two-point order crossover and a simple, order-based mutation were implemented. An adaptive feedback controller was introduced to vary the mutation rate as a function of population convergence. Integration of a tabu list minimizes time wasted reevaluating known solutions. A rank-based fitness function is proposed to handle multiple, competing objectives.
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© 1996 Springer-Verlag Berlin Heidelberg
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Niemeyer, G., Shiroma, P. (1996). Production scheduling with genetic algorithms and simulation. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1056
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DOI: https://doi.org/10.1007/3-540-61723-X_1056
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