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Indeed, compared to dynamic and random algorithms, the proposed instantiation request task scheduling GA outperformed in terms of total completion time and average processors utilization.
In both cases, the total completion time is significantly reduced.
As shown in Figure 13, the total completion time for the three algorithms increased as the number of tasks increased.
The results indicate that the proposed parallel greedy algorithm approach is very effective in terms of reduced total completion time or makespan (Cmax) for the attempted problems.
Numerical results show that the proposed scheme not only satisfies mobile users' quality-of-service requirements (in terms of the total completion time and hand-off dropping probability) but also maximizes channel utilization.
After a comparison was made with the studies of Oğuz [1], Oğuz and Ercan [2] and Kahraman et al. [3] related to the HFSMT problems, the computational results indicated that the proposed genetic algorithm approach is very effective in terms of reduced total completion time or makespan (Cmax) for the attempted problems.
Tasks are then allocated to actors according to their measured capabilities in such a way to reduce the total completion times of all tasks in the network.
We study the problem of minimizing total completion time in the two-machine flow shop with exact delay model.
This paper focuses on minimizing the total completion time in two-machine group scheduling problems with sequence-dependent setups that are typically found in discrete parts manufacturing.
This paper addresses the total completion time minimization in a two-stage differentiation flowshop where the sequences of jobs per type are predetermined.
Identical parallel machine scheduling problems with sequence dependent setup times, to minimize the total completion time are studied in this paper.
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