Research and Applications of Shop Scheduling Based on Genetic Algorithms

• Autor(a) principal: ZHAO,Hang
• Data de Publicação: 2016
• Outros Autores: KONG,Fansen
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Archives of Biology and Technology
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000200601
• topic: shop scheduling; genetic algorithm; local minimization; cyclic search

ABSTRACT Shop Scheduling is an important factor affecting the efficiency of production, efficient scheduling method and a research and application for optimization technology play an important role for manufacturing enterprises to improve production efficiency, reduce production costs and many other aspects. Existing studies have shown that improved genetic algorithm has solved the limitations that existed in the genetic algorithm, the objective function is able to meet customers' needs for shop scheduling, and the future research should focus on the combination of genetic algorithm with other optimized algorithms. In this paper, in order to overcome the shortcomings of early convergence of genetic algorithm and resolve local minimization problem in search process,aiming at mixed flow shop scheduling problem, an improved cyclic search genetic algorithm is put forward, and chromosome coding method and corresponding operation are given.The operation has the nature of inheriting the optimal individual ofthe previous generation and is able to avoid the emergence of local minimum, and cyclic and crossover operation and mutation operation can enhance the diversity of the population and then quickly get the optimal individual, and the effectiveness of the algorithm is validated. Experimental results show that the improved algorithm can well avoid the emergency of local minimum and is rapid in convergence.