Abstract
Lot streaming means breaking a lot into sublots, where sublots may be transferred to a number of machines for the operations. Here, the multi-job lot streaming problem in a multistage hybrid flow shop having identical parallel machines at stages with work-in-process (WIP) jobs, work shifts constraint, and sequence-dependent setup times is studied. The aim is to minimize the sum of weighted completion times of jobs in each shift in order to furnish a better machine utilization for the following shifts. Our model in meeting the job demands appropriates job scheduling on machines for processing, the sequence of operations on allocated machines, the size of the sublots in the work shifts, the work completion times in all the shifts, and the jobs in each stage as the WIP jobs. To solve the problem, a genetic algorithm (GA) and simulated annealing (SA) are proposed to compute the best scheduling for the hybrid flow shop problem. Numerical illustrations demonstrate the applicability of the proposed model and the effectiveness of the GA.
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