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It is found that the reliability and robustness of schedule implementation is significantly improved after optimisation.
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Therefore, in this paper, in addition to comparing the system throughput under different policies, we introduce the notion of robustness of scheduling policies.
Forecast errors exert inevitable disturbance on the robustness of scheduling strategy.
In addition, how to measure the robustness of scheduling heuristics (when they are applied to different/unseen scenarios) is still a open research question.
The rise in the level of flexibility increases the robustness of the schedule towards the uncertainties.
The results show that the robustness of the schedule is increased while the possible risk of failure is minimized, especially when the volume of generated data is not large in comparison with the input.
The goal in this problem is to minimize makespan (i.e., the time when the latest machine finishes its assigned tasks) and to maximize the robustness of the schedule (i.e., its tolerance to unexpected changes on the estimated time to complete the tasks).
Wang and Chen [124] propose a cost function that considers the robustness of a schedule regarding the probability of successful execution.
The two optimization objectives for scheduling schemes are the indices of the optimality and robustness of the scheduling results.
Robustness of the schedules is measured in terms of a plan achievement function which plays the role of a fitness landscape in the multi dimensional search space of feasible and non feasible schedules.
The second is related to the robustness of the scheduling, i.e., making it fault tolerant.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com