期刊名称：International Journal of Industrial Engineering Computations
印刷版ISSN：1923-2926
电子版ISSN：1923-2934
出版年度：2016
卷号：7
期号：2
页码：309-322
DOI：10.5267/j.ijiec.2015.9.004
语种：English
出版社：Growing Science Publishing Company
摘要：Recently, learning effects have been studied as an interesting topic for scheduling problems, however, most researches have considered single or two-machine settings. Moreover, learning factor has been considered for job times instead of setup times and the same learning effect has been used for all machines. This paper studies the m-machine no-wait flowshop scheduling problem considering truncated learning effect in no-wait flowshop environment. In this problem, setup time is a function of job position in the sequence with a learning truncation parameter and each machine has its own learning effect. In this paper, a mixed integer linear programming is proposed for the problem to solve such problem. This problem is NP-hard so an improved genetic algorithm (GA) and a simulated annealing (SA) algorithm are developed to find near optimal solutions. The accuracy and efficiency of the proposed procedures are tested against different criteria on various instances. Numerical experiments approve that SA outperforms in most instances.
