Paper Info
Title
Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints
Abstract
This paper presents a novel, two-level mixed-integer programming model of scheduling N jobs on M parallel machines that minimizes bi-objectives, namely the number of tardy jobs and the total completion time of all the jobs. The proposed model considers unrelated parallel machines. The jobs have non-identical due dates and ready times, and there are some precedence relations between them. Furthermore, sequence-dependent setup times, which are included in the proposed model, may be different for each machine depending on their characteristics. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches or optimization tools is extremely difficult. This paper proposes an efficient genetic algorithm (GA) to solve the bi-objective parallel machine scheduling problem. The performance of the presented model and the proposed GA is verified by a number of numerical experiments. The related results show the effectiveness of the proposed model and GA for small and large-sized problems.
Year
DOI
Venue
2009
10.1016/j.cor.2009.02.012
Computers & OR
Keywords
DocType
Volume
two-level mixed-integer programming model,bi-objective unrelated parallel machine,M parallel machine,genetic algorithm,large-sized problem,ready time,bi-objective parallel machine scheduling,unrelated parallel machine,precedence constraint,sequence-dependent setup time,proposed GA,proposed model,reasonable computational time
Journal
36
Issue
ISSN
Citations 
12
Computers and Operations Research
20
PageRank 
References 
Authors
0.82
14
5
Name
Order
Citations
PageRank
R. Tavakkoli-Moghaddam176748.39
F. Taheri2572.99
M. Bazzazi3573.33
M. Izadi4200.82
F. Sassani519618.49