Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints |
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| Authors: | R. Tavakkoli-Moghaddam F. Taheri M. Bazzazi M. Izadi F. Sassani |
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| Affiliation: | aDepartment of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran;bDepartment of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran;cDepartment of Computer Engineering and IT, Amirkabir University of Technology, Tehran, Iran;dDepartment of Mechanical Engineering, The University of British Columbia, Vancouver, Canada |
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| 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. |
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| Keywords: | Bi-objective parallel machine scheduling Sequence-dependent setup times Precedence constraints Genetic algorithm |
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