Solving hybrid flow-shop scheduling based on improved multi-objective artificial bee colony algorithm

In the model of hybrid flow shop scheduling problem with unrelated parallel machines, the makespan, total weighted earliness/tardiness and total waiting time are established as evaluation index. An algorithm of artificial bee colony based on the method of adaptive neighborhood search is designed. Ac...

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Bibliographic Details
Published in2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT) pp. 43 - 47
Main Authors Liang Xu, Ji Yeming, Huang Ming
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
artificial bee colony algorithm
hybrid flow-shop scheduling
Job shop scheduling
multi-objective
Optimal scheduling
Parallel machines
Sociology
Statistics
unrelated parallel machines
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Summary:In the model of hybrid flow shop scheduling problem with unrelated parallel machines, the makespan, total weighted earliness/tardiness and total waiting time are established as evaluation index. An algorithm of artificial bee colony based on the method of adaptive neighborhood search is designed. According to the characteristics of the model, initial processing sequence is used as solution vector in order to narrow down feasible solutions. Fitness of populations is distinguished by non-dominated sorting. In the process of iteration, excellent individuals are retained so that the diversity of population distribution is increased. Finally, the method is applied to a simulation example, compared with the traditional multi-objective algorithm. The results obtained demonstrate that the improved ABC algorithm for hybrid flow shop scheduling problem is good effective and diversified.
DOI:10.1109/CCIOT.2016.7868300