期刊
MATHEMATICS
卷 10, 期 14, 页码 -出版社
MDPI
DOI: 10.3390/math10142395
关键词
scheduling; multi-objective; dynamic scheduling; predictive-reactive; greedy
类别
This paper proposes a hybrid dynamic non-dominated sorting genetic algorithm for solving multi-objective rescheduling problems in dynamic permutation flow shop contexts. The algorithm can find the optimal Pareto front and performs well under different types of disruptions.
In this work, we seek to design a model that contributes to the study and resolution of a multi-objective rescheduling problem in dynamic permutation flow shop contexts. In this type of problem, where the objectives can be valued in heterogeneous units, the difficulty of achieving an optimal solution leads to finding a set of non-dominated efficient solutions (also called Pareto front). On the other hand, we will also consider the potential appearance of disruptions in planned scheduling (such as machine breakdowns or arrival of new priority jobs) that require a rapid re-planning of the aforementioned scheduling. In this paper, a hybrid dynamic non-dominated sorting genetic II metaheuristic (HDNSGA-II) is proposed to find the optimal Pareto front. The algorithm is applied to a benchmark already tested in previous studies, defined by three conflicting objective functions (makespan, total weighted tardiness, and stability) and three different types of disruption (machine breakdowns, incorporation of new jobs, and modifications in process times). According to the statistical comparison performed, the HDNSGA-II algorithm performs better in the designed environment, especially in larger problems.
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